Computers and Electronics in Agriculture 50 (2006) 25–47

SERRISTE: A daily set point determination softwarefor glasshouse tomato production

M. Tchamitchiana,∗, R. Martin-Clouaireb, J. Lagierc, B. Jeannequinc, S. Mercierd

a Ecodeveloppement, INRA Domaine St Paul, 84914 Avignon Cedex 9, Franceb UBIA, INRA Toulouse, BP 27, 31326 Castanet Tolosan, France

c SAD, INRA Domaine du Mas Blanc, 66200 Alenya, Franced PSH, Bat B, INRA, Domaine St Paul, 84914 Avignon Cedex 9, France

Received 9 August 2004; received in revised form 30 June 2005; accepted 27 July 2005

Abstract

SERRISTE is a decision making system that generates daily climate set points for greenhouse grown tomatoes. The system isbased on the mathematical formalisation of expert practices and scientific knowledge, as aconstraint satisfaction problem. Thestructure of SERRISTE is presented, as well as the knowledge used to describe the relationship between the crop behaviour and thegreenhouse climate, and the relationship between set points and the resulting greenhouse climate. The performances of the systemhave been tested in three different locations in France by applying a blind reference management and SERRISTE managementto two identical greenhouse compartments at each location. The main results are that SERRISTE maintains higher day to nighttemperature differences and lower vapour pressure deficit than the reference management, and leads to energy savings in the rangeof 5–20%. The SERRISTE crop yields at least the same harvest as the reference one. Moreover, the crop behaviour in summer isenhanced by the use of SERRISTE, because the plants are more vegetative and more able to endure high temperatures.© 2005 Elsevier B.V. All rights reserved.

Keywords: Decision making; Greenhouse climate control; Greenhouse; Tomato; Constraint satisfaction problem

1. Introduction

Greenhouses were originally designed to provide the crop a shelter from unfavourable climatic conditions. Whenproperly equipped with climate control devices, the greenhouse becomes a factory for intensive crop production withhigh running costs (as compared to production under tunnels with little control equipment or in the open field). Themanagement of the greenhouse is therefore a significant activity for the grower in which he has to assign prioritiesbetween the goals he pursues and find the appropriate actions to fulfil these goals.

The analysis of the decisions involved in the management of the greenhouse leads to a decomposition in a cascadeof three levels (Udink ten Cate and Challa, 1984; Baille et al., 1990). At the highest level (level 2) the grower decidesupon the crop to be planted (species and variety), the timing of the production, etc. He sets up the configuration for theproduction. The second level (level 1) is a tactical one where the grower must decide upon the environmental conditions

∗ Corresponding author. Tel.: +33 432 72 25 61; fax: +33 432 72 25 62.E-mail addresses: arm@avignon.inra.fr, marc.tchamitchian@avignon.inra.fr (M. Tchamitchian), rmc@toulouse.inra.fr (R. Martin-Clouaire),

jeannequin@ensam.inra.fr (B. Jeannequin).

0168-1699/$ – see front matter © 2005 Elsevier B.V. All rights reserved.doi:10.1016/j.compag.2005.07.004

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that will bring out the desired behaviour of the crop so as to meet the overall objectives assigned at level 2. Decisionsabout the technical operations performed directly on the crop (pruning, training, etc.) are also made at this level. Thefinal level (level 0) is where actions are taken to bring the system (the greenhouse and the crop) into the intended statespecified at level 1. The commercial climate computers used in greenhouses work at this level because they regulatethe climate and the fertigation so as to comply with the set points fixed by the grower. The decisions made at levels 2and 1 are the responsibility of the grower.

Choosing the set points for the greenhouse inside environment pertains to level 1. It is a daily or at least weeklyactivity, which must be undertaken with great care. The consequences of the decisions are not all immediately visibleand are hardly reversible in many cases. For example, a change in the daily mean temperature will result in a change inthe rate of new organ production, which will only be perceivable within a week or two; it will also result in a changein crop vigour (the concept of crop vigour will be defined and discussed later) 2 or 3 days later. This example alsoshows that a given decision about the environmental conditions may have impacts on different goals, positively ornegatively. The goals might be partially conflicting. To make an appropriate choice of set points the grower must firstdefine the goals that he assigns to the crop and their order of priority; this should be done within the frame set by thedecisions made at level 2. It is also necessary to take into account the weather forecasts because outside conditionsstrongly influence inside conditions and the way to control them. Finally, the grower must have some knowledge ofthe crop behaviour and responses to the environment (ecophysiology) and of the physics of the greenhouse climate.This knowledge partly originates from his education, but is also based on the grower’s own experience. In most cases,the grower will seek the help of a development adviser, whether institutional or private. In doing so, the grower obtainsaccess to a wider knowledge. The adviser is often more educated that the grower and updates continually his knowledgeas part of his job. He also has a wider experience in so far that he is counsel to many growers, thus multiplying thenumber of cases he knows and analyses.

Several attempts have been made to exploit scientific knowledge and especially crop growth models to determine theoptimal or simply suitable set points for a given criterion measuring the performance of the crop–greenhouse system.Martin-Clouaire et al. (1996)in their review have shown that most of the works were, at the time of their survey, stillin the scientific development phase, and not ready for testing or dissemination. Since this survey, the literature hasreported very few works that have matured enough to be used by growers.Rijsdijk and Vogelezang (2000)describe analgorithm to regulate the temperature in the greenhouse which optimises the hourly set point based on the estimatedcost of heating (using weather forecasts) and on a 24 h mean temperature goal. The average temperature and thelower/upper bounds during night and day are provided by the grower. This algorithm has been implemented in onecommercial greenhouse computer and is available to growers. However, it does not provide a full support for the climatemanagement of the greenhouse because the choice of the daily average temperature is left to the grower; a choice thatdoes have a significant role in the control of the behaviour of the crop.

The goal of this paper is to present the software SERRISTE, a decision support system for the climate managementof the greenhouse, dedicated to tomato production. SERRISTE (greenhouse grower in French) provides the growerwith a proposal of daily greenhouse climatic set points. Basically, the set points are generated every morning byprocessing an agronomic knowledge base in function of the specific data concerning the current state of the crop andthe weather forecasts for the next 24 h. In Section2, the system is described through its objectives and the computationalapproach that it implements. Section3 presents the knowledge that is processed by the system. Section4 describeshow SERRISTE has been implemented and the graphical user interface. Section5 reports on the experiments carriedout to assess the performance of SERRISTE. Section6 is devoted to an analysis of the agronomic results. The paperconcludes by summarising the main results, difficulties and prospects in the use of SERRISTE.

2. System description

2.1. Foundations

The main idea underpinning the development of the SERRISTE system is that the knowledge used by advisersor expert growers to manage the greenhouse climate can profitably be encapsulated and exploited in a set pointdetermination software (Martin-Clouaire et al., 1993a,c). Indeed, the growers and their advisers do have to decideweekly or more frequently upon the climate set points. To do so, they analyse the current situation (crop state,environmental condition), take into account the weather forecasts and, foremost, use their knowledge of the crop and

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greenhouse behaviour. It is this very agronomic knowledge, built from the melting of education, experience and self-learning, that SERRISTE aims at capturing and using to automate the decision-making process of greenhouse climatemanagement. Hence, SERRISTE is based on the analysis of the discourse ofsome selected experts about their own wayof choosing the set points. These experts were chosen for their knowledge of the practices that were widespread amongFrench growers and because of their supposed ability to be spokesmen of a community of experts. But, paramount,they also have been chosen for their ability to describe formally and analyse agronomically the practices they use.Hence the chosen experts were members of an INRA development station.

2.2. Objectives

The main goal of SERRISTE is to determine the daily climatic set points adequate for the growth and productionof a tomato crop grown under greenhouse in soil-less conditions and planted in autumn (October to December inEuropean locations). Although restrictive, these conditions are those of the largest part of the French greenhousetomato production, which is the first vegetable grown under greenhouse in France (and in Europe). Climatic set pointsare defined to be compatible with most commercial greenhouse computers. Therefore, they consist of the needed valuesto regulate air temperature, air humidity and the substrate temperature (more precisely the temperature of the pipeslocated directly against the growing medium, when they exist). However, commercial greenhouse computers do notregulate the climate to a given value (the true meaning of a set point), but between bounds. They define theheating setpoint and theventilation set point, values below which, or above which, the corresponding control device is activated;they also define the low and high humidity set points in a similar way. SERRISTE is designed to provide such set points.

Another objective of SERRISTE is to simultaneously take into account several goals assigned by the grower to thecrop such as disease outbreak prevention or production and development rates. Goals on production and developmentrates are not directly expressed by the grower who rather uses crop vigour as an indicator of the current rates. SERRISTEalso has to deal with knowledge belonging to several domains such as ecophysiology, to integrate the crop responsesto environmental conditions, agronomy, to exploit empirically-based knowledge or practices, and physics, to be ableto relate set points to the climate in the greenhouse.

SERRISTE also aims at taking advantage of weather forecasts to propose set points fit to them, but also to the currentstate of the crop. Considering weather forecasts implies to limit the horizon of the proposition to at most a few days,because of the unreliability of long-term forecasts. However, many aspects of crop behaviour cannot be understood ata time span shorter than a day. In most ecophysiologic models, crop growth and development are determined once aday, at which scale it is possible to balance the day’s photosynthetic activity and the consumption of assimilates by therespiration, growth and development (Bertin and Heuvelink, 1993) that are temperature driven processes.

SERRISTE is therefore designed for one daily run and a unique solution fit for the coming 24 h is determined. Thetermsolution is used because the climate management is seen as a problem to solve.

2.3. Useful structures and data

2.3.1. Knowledge basesGreenhouse tomato production lasts 10–11 months, thus covering almost all the seasons from autumn to the next

autumn. While the control of the greenhouse climate is possible during cold periods, it becomes more difficult or almostimpossible in hot situations like the Mediterranean summer. The knowledge used by SERRISTE must deal with thesecontrasted cases. To tackle this problem, the corpus used by SERRISTE is divided into threeknowledge bases, eachfit to a particular type of outside environmental conditions:

• Thewinter knowledge base applies when the power of the control equipment allows achieving any desired greenhouseclimate, without the need for night ventilation.

• Themild-night knowledge base applies when the night weather is mild, possibly requiring the use of ventilation atnight to maintain low temperatures in the greenhouse.

• Thehot-day knowledge base applies when the weather (day and night) is too hot for the greenhouse control devicesto be able to regulate the climate.

The rules used to determine which knowledge base applies to the current situation are given in Section2.5.1.

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Table 1Crop stages, agronomic goals assigned to the crop and climate management means to reach these goals

Stage Agronomic goals Climate management

1 Reinforce rooting Maintain air vs. soil temperature balanceSustain vegetative balance Maintain air temperature vs. solar radiation balanceEase fruit setting Avoid low air humidity

2 Ease fruit growth Maintain solar radiation vs. air and soil temperatureSustain vegetative balance Avoid high air humidityPreventBotrytis outbreak

3 Sustain vegetative balance Maintain solar radiation vs. air and soil temperaturePreventBotrytis outbreak Adapt air temperature to solar radiationCare for development rate and photosynthetic activity Insist on dehumidification by heating and ventilation

Protect plants from sudden weather change

4 Sustain fruit growth Avoid excess air temperatureSustain vegetative balance Avoid low air humidity at day timeSustain transpiration to provide enough minerals Adapt air humidity to expected crop transpiration

2.3.2. Crop stagesDuring the growing season, the crop evolves, starting with young plants bearing a limited number of leaves and

small in size to plants bearing many leaves, trusses and fruits, with a main stem that may be several meters long.Therefore, the crop has grown through distinct stages, which, understandably, do not have the same requirements interms of environmental conditions or training operations.

Four crop stages have been defined to follow the evolution of the crop so that each stage has an invariant set ofobjectives and means to reach them. They are presented inTable 1. As can be seen the goals shift from an emphasis onpreparing the plants to sustain production, with good root system and vegetative part, to plants producing new fruitsand growing the existing ones. For an autumn tomato crop, these stages correspond to key development states as shownin Table 2. The winter and mild-night knowledge bases are designed to be able to fulfil the goals of each crop stage.

2.3.3. Day divisionThe sequence of daytime and night-time conditions is significant to the crop behaviour. Photosynthetic activity

only occurs during daytime, but provides assimilates for growth and substrates for respiration throughout day andnight. The rates of assimilate use (growth and associated respiration) can generally not be identical between day(where new assimilates are produced) and night (where the amount available is limited). Other crop processes are,by experience, sensitive to the alternating conditions of day and night, like stem elongation or the vigour of the crop.Climate management must therefore consider these alternating conditions, which implies that while night-time anddaytime conditions are to be determined together, they must be different.

In addition to day and night, pre-dawn is a period of special interest because the air humidity is very high (thegreenhouse often stays closed at night and plants still transpire, even if at a low rate) and the air, crop and greenhousematerial temperatures are at their lowest values, as is the outside air temperature. Therefore, risks of water condensationon leaves or glazing are high. Specific actions must be taken to prevent this condensation and thus avoid the associatedrisks of disease outbreak. In addition to make the crop able to respond to the sunlight that will come soon, it is necessaryto increase canopy temperature so that photosynthetic activity will not be impaired, and to allow an increase in growthrate as soon as the assimilates are made available.

Table 2Crop stages and crop development states

Stage Crop initial development state Estimated duration

1 Plantation At least 3 weeks2 Flowering 3rd truss 4–5 weeks3 Flowering 5th truss 4 weeks4 Harvest 16 weeks or more

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In SERRISTE, the day is divided into three periods: daytime, night-time and pre-dawn. Daytime runs from dawn todusk. Night-time lasts from dusk to the start of pre-dawn. Pre-dawn ends when the next daytime period starts. Pre-dawntherefore covers the last part of the true night. SERRISTE generates a set of set points for each of these three periods.This solution is built as a whole, considering the interrelationship between the climates of the three periods. In thefollowing, the wordday means the 24 h day; the worddaytime (or daylight time) will be reserved for the period of theday where there is light. The word night will designate the period where there is no daylight. The night is thereforecomposed of the two periods namednight-time andpre-dawn.

2.3.4. Required data about the crop–greenhouse systemThree types of information about the crop–greenhouse system are needed to compute daily climatic set points.The first type of information describes static aspects of the system at hand, namely the greenhouse location, glazing

materials and transmissivity, heating system, thermal screens (if any) and the crop variety. These data remain constantduring the complete cropping season and allow determination of the physical properties of the greenhouse, especiallythe cost and efficiency of any heating policy. Although crop responses to temperature are determined by the sameprocesses for all cultivars, a given average temperature does not imply the same intensity in the response of differentcultivars. In SERRISTE, varieties are not individually addressed: a number oftypes have been defined, which groupvarieties with common behaviour with respect to temperature. For example, varieties requiring high temperature levelsall along their development such as some beef cultivars are grouped in theheat demanding type. At the opposite,varieties with low temperature requirements are grouped in thecold tolerant type.

The second type of information needed concerns dynamically evolving aspects of the system. This informationconsists of the measured past climate, inside and outside, of an appraisal of the crop state in terms of vigour and riskor occurrence ofBotrytis cinerea and of weather forecasts for the coming day.

The past climate is used for two purposes. First, it allows a comparison of the real past greenhouse climate with theclimate that SERRISTE would have decided upon using measured data (of the outside weather) instead of forecasts toproduce the solution. Any discrepancy in the greenhouse daily (24 h) average temperature due to inaccuracy of weatherforecasts can be corrected. IndeedHeuvelink (1989)andde Koning (1990)have shown that the crop can compensateover a few days for deviations from anoptimal average temperature. In other words, it is possible to compensate lowtemperatures (with respect to the supposed optimal temperature) by increasing the next day temperature, and viceversa, so that the achieved average over these 2 days is very close to the average of the optimal temperatures of these2 days (see also Section3.1.1). The second purpose in the use of past climate measurements is to enable computationof the energy balance of the greenhouse. The greenhouse has a significant thermal inertia.Boulard and Baille (1987)have shown that for a greenhouse with alternating daylight and night temperatures, the temperature of the previousperiod modifies that of the next. Their simple thermal balance model is used to estimate the energy requirement of thesolution found by SERRISTE. It is written:

Q = γRG− U1(Tg − To) − U2(Tg − Tg,−1)

whereQ is the energy needed to maintain a greenhouse temperature ofTg, Tg,−1 is the greenhouse set-point duringthe previous time period, RG is the outside solar radiation,To the outside temperature,γ is the solar efficiency,U1 theoverall heat loss coefficient andU2 the heat storage coefficient.

The crop state is defined by two variables, one qualifying its vigour, the second its health status with respect toBotrytis. The crop vigour is a concept widely used and shared by growers and horticultural advisers in France toappraise the ability of the crop to sustain a good or high fruit yield while continuing to set new leaves and trusses at apace ensuring an adequate future yield. The crop vigour therefore also gives an indication about the balance betweenvegetative and generative organs on the plant, as well as an indication about the ability of the crop to achieve sustainedphotosynthesis and water and mineral absorption rates. The appraisal of the vigour includes several visible aspectsof the crop. The head of the plant is observed with respect to its bearing, colour, elongation and stem diameter. Theoverall colour of the crop is also considered, with the impression the crop gives of being etiolated or vegetativelyunbalanced. Weak vigour is associated with plants seemingly etiolated, with stems of low diameter and long internodaldistance, with a head of a light green with possibly a little shade of yellow, with weak trusses and a relatively smallvegetative development. Such plants have a low potential for sustained production and are imbalanced because of a lowphotosynthetic activity compared to the demand in assimilates by growth and development. Strong vigour is associatedwith plants having a deep green head with leaves almost curled and seemingly crispy, with short internodal distance and

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a strong vegetative development. Such plants have a strong potential for sustained production but are also imbalancedbecause they use too much assimilates for the vegetative development, thus limiting fruit growth. SERRISTE alsoneeds the goal assigned by the grower to the crop vigour: maintain, strengthen or weaken.

The third type of information required in the determination of the daily climate set points is weather forecasts.Outside maximal and minimal temperatures, solar radiation and wind speed are required. They are used to determinethe energy cost of the solution found by SERRISTE. Solar radiation is also used to determine an adequate range forthe 24 h average temperature, as will be discussed below.

2.4. Knowledge representation

In SERRISTE, the basic knowledge representation structures are variables and constraints. A variable characterisesa property that is relevant in the greenhouse climate management problem, such as the average temperature for agiven period or a set point. A constraint is a mathematical expression describing the relation between some variables.Formally, a constraint in SERRISTE is composed of a linear combination of variables and a fuzzy interval (Martin-Clouaire and Kovats, 1993b) that allows representation of fuzzy (flexible) constraints. More formally, all constraintshave the general form:

n∑

i=1

βivi ∈ F

whereβi is a rational coefficient,vi is a variable,n is the number of variables (n ≥ 1) andF is a fuzzy interval. A fuzzyinterval (Dubois and Prade, 1988) is an interval having ill-defined boundaries such that some elements might havepartial membership to it. A fuzzy interval is represented by its membership function. A trapezoidal function is usuallysufficient to express practical knowledge. In such case, the fuzzy interval is fully defined by four numbers (δ, m, M, θ)as shown inFig. 1. [m, M] is the interval of values that fully belongs to the fuzzy interval. [m − δ, M + θ] is the intervaloutside of which any value is out of the fuzzy interval. The membership degree of the values betweenm − δ andmare linearly interpolated between 0 and 1, 0 expressing non-membership and 1 full membership. Similarly, the valuesbetweenM andM + θ have a membership degree interpolated between 1 and 0.

Due to the presence of a fuzzy interval, a constraint might be partially satisfied by the values taken by the variables.The degree of satisfaction of the constraint by the values (α1, . . ., αn) of its n variablesvi is computed on the [0, 1]scale as the membership of

∑ni=1βiαi in the fuzzy intervalF as illustrated inFig. 1.

A constraint can apply to a single variable, in which case it directly defines the domain for the variable (the rangeof values that this variable can take). The crop stage constraints are of this type. For instance, for a heat demandingvariety (e.g. Trust, Twin or Conchita cultivars), the average daylight temperature in stage 1 is defined as (2, 20, 24, 4).A constraint can also apply to several variables, thus describing the relation between these variables. The constraintsused in the knowledge bases are of this type. For instance, the difference between the daylight and night averagetemperatures must be greater than 2◦C in northern France locations and lower than 6◦C when the forecasted radiationlevel is high. These limits yield a constraint which states that the difference between the daylight and night average

Fig. 1. Fuzzy intervalF.

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temperature must belong to the domain (0, 2, 6, 0), a crisp domain in this case. The degree of satisfaction of a solution,that is an assignment of all the variables, is defined as the minimum of the degrees of satisfaction of all the constraintsby this assignment. Taking the minimum expresses that the overall satisfaction is equal to the degree of satisfactionof the least satisfied constraint. The degree of satisfaction of a solution is equal to 1 when all the constraints are fullysatisfied.

Note that crisp (non fuzzy) constraints are special cases of fuzzy constraints. For instance, an order relation (v1 < v2)or equality (v1 = v2) between two variables can be represented as:

• Inequality:v1 < v2 ⇔ v2 − v1 ∈ (0, 0, +∞, 0). The fuzzy intervalF is here the crisp set of positive or null values.• Equality:v1 = v2 ⇔ v2 − v1 ∈ (0, 0, 0, 0). The fuzzy intervalF is here the crisp set constituted by the singleton{0}.

The degree of satisfaction of a crisp constraint is equal to either 1 (full satisfaction of the constraint) or 0 (violationof the constraint).

2.5. Set point determination procedure

The knowledge bases and crop stage constraints are basically constraints parameterised by contextual information.Every day the constraints that have to be taken into account are derived as a function of the current data about thecrop–greenhouse system. This process is discussed in Section2.5.1. Once formulated as a set of flexible constraints,the greenhouse management problem can be solved by using a constraint satisfaction solver that searches for assign-ments of the variables that do not violate any constraint. The principles of constraint satisfaction and the softwareused in SERRISTE are outlined in Section2.5.2. Finally, since the constraint solver may return a set of acceptablesolutions, a selection procedure is used to find the best one according to context-dependent criteria described inSection2.5.3.

2.5.1. From general knowledge to active constraintsFig. 2schematically represents the process by which the constraints to be taken into account are derived every day.

The relevant knowledge base and the relevant crop stage constraints have to be selected and merged. The so-obtainedset of parameterised constraints has to be specialised in the sense that all the involved parameters have to be assignedvalues by taking into account data specific to the situation at hand. The process ultimately yields the set of activeconstraints, that is, the set of constraints that have to be processed by the constraint solver.

The selection of the relevant knowledge base is done according to the following principles. Ahot day can only occurfrom March 15th to October 31st (northern hemisphere); moreover, the forecast for the maximum outside temperaturemust be above a threshold set by default at 17◦C but modifiable by the user, and the forecast for the solar radiationintensity must be high. If these conditions are met, thehot day knowledge base is chosen. Otherwise, SERRISTEcomputes the average night temperature in the greenhouse if no heating is used, using the forecast for the minimumoutside temperature. To do so, SERRISTE uses the greenhouse energy balance model developed byBoulard and Baille(1987), which takes into account the greenhouse cover type (glass, simple or double-layer plastic) and the ratio ofthe greenhouse area in contact with outside air to greenhouse floor area. This computed temperature is compared to athreshold depending on the variety and crop stage. If it is above the threshold, then themild-night knowledge base ischosen. Otherwise, SERRISTE uses thewinter knowledge base. The temperature threshold selecting the mild-nightknowledge base corresponds to the minimum average night temperature that might be desirable for this crop in thecurrent crop stage. In other words, the mild-night knowledge base is chosen when night ventilation might be required.

According toTable 2, the grower indicates the current crop stage. This information is used to select the appropriateset of constraints. The crop variety is later used to deparameterize these constraints.

2.5.2. Constraint solverA constraint satisfaction problem (CSP) is a problem formulated as a finite set of constraints restricting the possible

values of a finite set of variables (Tsang, 1993). Each variable has a domain of possible values and their associationconstitutes a unary constraint. A solution of the problem is an assignment of a value to each variable such thatnone of the constraints are violated. Depending on the problem, the objective is to determine whether a solutionexists, to find one, several or all the solutions. Many techniques have been developed in artificial intelligence to

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Fig. 2. Derivation of the constraints to be processed by the solver.

solve constraint satisfaction problems. These techniques can be classified into two categories: consistency enforcingalgorithms (filtering algorithms) and search algorithms. The most basic consistency algorithm, called arc-consistency,ensures that any legal value in the domain of a variable has a legal value in the domain of each of the variables connectedto it through constraints. All values that cannot be part of a solution are removed (or filtered) from the domains ofthe corresponding variables. The most common algorithm for performing systematic search is backtracking, whichtraverses the space of partial solutions (i.e. not all the variables are yet assigned) in a depth-first manner. At each stepthe algorithm extends a partial solution by assigning a value to one more variable. When a variable is encounteredsuch that none of the values in its domain is consistent with the partial solution, backtracking takes place and anotherassignment is tried. Backtracking can be greatly improved if used together with a consistency enforcing algorithm. Thefirst such improvement, called forward checking (FC), is to look every time an assignment is made at each unassignedvariable that is connected to the just assigned one by a constraint, and deletes in the corresponding domains thevalues that are not consistent with the value just chosen. If at any time some domain becomes empty, the algorithmimmediately backtracks. Another commonly used possibility, called full-lookahead, is to apply an arc-consistencyalgorithm after each tentative assignment. The arc-consistency algorithm is also often used before any attempt to assign avariable.

In the initial prototype version of SERRISTE, a dedicated solver was developed (Martin-Clouaire and Kovats,1993b) using an artificial intelligence software environment. For higher efficiency and easier portability, the SERRISTEconstraint satisfaction problem is solved by using the CON’FLEX tool (Rellier et al., 1996). CON’FLEX is a general

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Fig. 3. Approximation of the fuzzy intervalF by α-cut.

C++ solver that can handle fuzzy constraint problems with both finite domain variables and interval (continuous)variables. SERRISTE needs only a small part of the capabilities of the tool. The CSP techniques require that, at somepoint, the domains of the variables be discrete in order to support enumeration of candidate values. In SERRISTE thedomains are continuous and have therefore to be discretized; the grain of discretization is variable-dependent and mustbe defined by the user. This transformation is delayed for each domain until a value has to be chosen for assigningthe variable. Since the SERRISTE constraints are linear expressions over numerical variables, an efficient consistencyenforcing algorithm based on interval computation methods (Davis, 1987) can be used and is directly provided byCON’FLEX.

The SERRISTE constraints (including the variable domains) are defined with respect to fuzzy intervals. Any fuzzyinterval can be approximated by a set of nested crisp (non-fuzzy) intervals, each associated to the degree equal tothe lowest degree of membership of the values in the set. These sets are calledα-cuts (Fig. 3). The precision of theapproximation is defined by the user; typically the membership scale is decomposed in 11 levels from 0 to 1 by a stepof 0.1. Therefore, by discretizing the membership range, one can approximate a fuzzy constraint by a finite family ofweighted non-fuzzy constraints; the weight of each so-constructed constraint is the degreeα of the associatedα-cutFα of the intervalF. The degreeα associated with anyα-cut expresses that the values in this set are satisfying theconstraint at least at the degreeα. With this representation, the degree of satisfaction of a constraint by an assignments of its variables is computed as:

maxα min(α, µFα (s))

whereµFα (s) is equal to 1 ifs is in Fα and 0 otherwise.The call to the CON’FLEX solver requires that some options be specified. In particular, it is declared through this

means that the search should be done by the forward-checking algorithm and an arc-consistency filtering should beperformed before starting the search. Other options concern the discretization step of the satisfaction range and variousefficiency-related possibilities such as the order of visit of the variables, which currently is set such that the next variableto assign is the one having the smallest domain. Finally, a specific option indicates that all solutions should be searched.CON’FLEX takes as input the file of constraints derived according to Section2.5.1and returns a file containing all thesolutions having a degree of satisfaction strictly above 0. Recall that the degree of satisfaction of a solution is definedas the minimum of the degrees of satisfaction of all the constraints used.

2.5.3. Selection procedureSolving the constraint satisfaction problem derived as discussed in Section2.5.1yields a set of solutions, each

qualified by a degree of satisfaction. This set might be empty in some exceptional cases. Usually the set containsseveral solutions. The number of solutions can be very high, however in any case, only one solution must be proposedto the user. Although all solutions in the set are fit to the current case, it is possible to sort them according to context-dependent criteria to propose the solution that best applies. Before applying the context-dependent criteria, the set ofsolutions is pruned so that only those with the highest satisfaction degree are kept. In other words, the selection applies

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to the solutions that least violate the constraints corresponding to the current problem to solve. In addition, the solutionsare slightly polished to eliminate artificial precision; all values are rounded to one tenth of a degree for temperaturesand 1 daPa for the water vapour pressure deficit (VPD) set points. A higher precision would not be justified given theuncertainty in the knowledge used (and especially in the parameters involved in each knowledge base) and the limitsof the greenhouse control systems.

The selection procedure works as follows. IfBotrytis is present, thedriest solution is selected, i.e., the one with theminimum daylight ventilation set point and the maximum night-time heating set point. Otherwise, the choice dependson the vigour of the crop. If the vigour is weak, then the coldest solutions are selected, i.e., those with the minimumdaylight heating and ventilation set points. If the vigour is strong, then the hottest solutions are selected, i.e., thosewith the maximum daylight ventilation set point and night-time heating set point. If the vigour is normal, then thesolutions with the bigger day–night temperature difference are chosen among those keeping the greenhouse as closedas possible if the wind forecast is high (to limit air exchanges and energy losses). Finally, in all cases except those ofBotrytis attack, the solution with the lowest energy consumption is chosen within the remaining candidate solutions.In case of tie, the first one is chosen.

3. Knowledge base content

The following sections detail the main pieces of knowledge used in SERRISTE. Although the objective of SERRISTEis to determine set points, a significant part of the knowledge concerns the determination of the average greenhouseclimate conditions to be maintained in order to obtain the desired crop behaviour. This is where agronomy andecophysiology meet. Another part of the knowledge used deals with greenhouse physics and is used to determine theset points that will result in average climate conditions that are suitable for the crop.

3.1. Winter cases

The winter knowledge base (as well as the mild night one) is composed of the variables describing the desiredtemperature and water vapour pressure deficit to maintain in the greenhouse during each period of the day, of thevariables for the associated set points and of the relations between these variables.

3.1.1. Daily average temperatureThe daily (24 h) average temperature controls the development rate of the crop (Aung, 1976; de Koning, 1992) but

also modifies the growth rate.Seginer et al. (1994b)have shown that maximal relative growth rate can be obtainedfor several combinations of daytime and night temperatures, provided they resulted in the same daily average. Theseauthors have also shown that the optimal average temperature was a function of the crop dry matter (which increaseswith the crop age) and of the available photosynthetically active radiation (PAR), which determines the quantity ofassimilates available for growth and respiration. However, their analysis was only based on the optimisation of therelative growth rate, not on the development rate of the crop: on dull days, the optimal temperature can be very low,too low to sustain a good development rate. Hence, in SERRISTE, the optimal temperature, although related to theforecasted available radiation, is bounded by values ensuring that the development of the crop and the apparitionof new trusses are maintained at a proper rate (Fig. 4, top left). This empirical relation is defined as a linear fitbetween the minimum and maximum available radiation (RGn and RGx) and the minimum and maximum acceptablevalues for the daily temperature average (Tn and Tx). The minimum and maximum available global radiations arecomputed for the current day, based on a determination of the extraterrestrial irradiation, RGtheo, (Iqbal, 1983). Theminimum available radiation, RGn, is defined asθ × 0.132× RGtheo and the maximum available radiation, RGx, asθ × 0.686× RGtheo, whereθ is the greenhouse transmissivity of the current greenhouse. The values 0.132 and 0.686are statistical coefficients linking the extraterrestrial irradiation in a given location to the minimum and to the maximumavailable radiation measured in this location. Minimum (maximum) available radiation is here defined as the averageof the values in the first (last) decile. This statistical fit has been repeated over many locations spread all over Franceand gave fairly comparable results so that the coefficients (0.132 and 0.686) could be defined independently of thelocation of the greenhouse.

Fig. 4also shows the other elements taken into account to define the daily average temperature range.

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Fig. 4. Determination of the daily average temperature range; symbols defined in the text.

Crop variety and crop stage are taken into account. For each variety type, a table indicates the evolution of theaverage temperature during the crop life. The first crop stage has a relatively high temperature requirement to hastenthe apparition of the first trusses and the development of the root system. During crop stages 2 and 3, the fruit loadincreases drastically, therefore the average temperature must be limited to decrease the assimilate demand of fruits,in order to avoid unsuccessful fruit setting (Bertin and Gary, 1992). Afterwards, harvesting limits the fruit load of thecrop and the average temperatures can be increased to sustain fruit growth and development rate; however, it cannotbe as high as during crop stage 1.

CO2 enrichment increases the photosynthetic activity and assimilates production. Because the growth rate dependson the average temperature of the plant, increasing the average temperature may be used to allow the crop to value theavailable assimilates. Three levels of CO2 enrichments are defined, none, light (less than 600 ppm during at most halfa daylight period) and high.

The vigour of the crop can also be modified by a change in the average temperature. Higher temperatures tend toweaken the vigour while lower temperatures tend to strengthen it. As already explained (Section2.3.4), the vigour hasthree levels, weak, normal and strong.

Finally, the daily average temperature is corrected if the average temperature of the previous day is not sat-isfying. As said above, the optimal temperature is based on the forecast of the available irradiance; this canlead to apply a solution not fit for the day if the forecast is erroneous. Therefore, the measured irradiance ofthe previous day is used to determine the correct daily average temperature that should have been applied onthe previous day (Toptj−1). If the measured average greenhouse temperature during the previous day is higherthat Toptj−1, then the crop has been exposed to a higher temperature sum than necessary and, consequently, itsdevelopment has been accelerated. A negative correction for the day at hand can therefore be considered. How-ever, this correction is not systematically applied: the diagnostic is noticed to the grower who may accept it orrefuse it, depending on his own appraisal of the development and of the vegetative to reproductive balance of thecrop.

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3.1.2. Daytime and night temperaturesThe difference between daytime and night average temperatures plays a significant role in the control of the crop

behaviour (Khayat et al., 1985; Bertram, 1992). It also allows for energy savings because obtaining high temperatureduring daylight time is often cheaper than during night due to the natural heating of the sun and the higher outsidetemperature. Based on experience and on the results ofSeginer et al. (1994b), the maximum difference between daylightand night average temperatures increases with the forecasted irradiance because it allows for higher natural daytimetemperature.

Obviously, the average day temperature (24 h average), the average daylight and the average night temperatures arelinked because the daily average is the weighted average of the night and of the daylight temperatures.

3.1.3. Night-time and pre-dawn temperaturesThe average night temperature is the result of the average night-time temperature and pre-dawn temperature, which

yields a constraint similar to the previous one.It is also indicated that the night-time average temperature must be lower than the pre-dawn average temperature,

which, in turn, must be lower than the daytime average temperature.Each of these average temperatures must also belong to a fuzzy domain, which depends on the variety type and the

crop stage. These domains are the result of the expression of expert practices.

3.1.4. Temperature set pointsHeating and ventilation set points are related to the resulting average temperature. Many physical models are available

in the literature describing this relation (Bot, 1983; Boulard and Baille, 1993; Seginer et al., 1994a). However, the useof these models to find the set points resulting in a desired average temperature requires that they are calibrated for thegreenhouse at hand and that the evolution of the outside temperature and solar radiation is known in advance. Boththese conditions cannot be satisfied, because the calibration requires measurements that are not available in commercialgreenhouses and because the weather forecasts that are readily accessible without any cost only include the minimumand maximum temperatures and a declarative description of the sky cloudiness. Hence, practical approaches have beenchosen, although they are based on the knowledge of the physics of the greenhouse.

On a dull day, the energy input in the greenhouse due to the solar irradiance is low: the average temperature,Td,is close to the heating set point because the heating system will be required to compensate for the lack of solar input.On the contrary, on bright days, the solar irradiance is high and heats the greenhouse, and the average temperaturewill be closer to the ventilation set point.Fig. 5 shows how this is formalised: the ratioR defines the position of thedaylight average temperature with respect to the two heating and ventilation set points (Fig. 5A). R is determinedaccording to the forecasted global radiation, following an increasing linear function (Fig. 5B). However, the daylightventilation and heating temperature set points cannot be fully determined with this relation. The difference betweenheating and ventilation set points is also ruled by physical and biological knowledge. First, in case ofBotrytis risk oroccurrence, it is paramount to avoid low VPD. Therefore, this difference is small (0.5◦C). Second, when a suddenweather change induces a high potential evapotranspiration after a day of low potential evapotranspiration, the cropsuffers from water stress. To avoid such limitations, the differenceB is reduced to limit high temperatures and theirimpact on a water stressed crop (the VPD in the greenhouse is also adapted as detailed later). Finally, to exploit theheat provided by the solar irradiance and thus to reduce the cost of heating, the value of the differenceB increases

Fig. 5. Determination of daylight heating and ventilation set points. RGn and RGx are the minimum and maximum available radiation for the day,determined from the theoretical radiative exposure of the day at hand for the current location.

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with the forecasted solar irradiance. This yields two constraints, the first linking the average daylight temperatureTdto the daylight ventilation set point, the second linking the average daylight temperature to the daylight heating setpoint.

During night-time, the relation between the average temperature and the heating set point is more straightforwardbecause of the low outside temperature, which induces a rather permanent use of the heating. However, experience hasshown that these two values are not equal but differ more or less depending on the glazing material and on the use ofa thermal screen. Thus, the night-time heating set point is lower than the average temperature by 0–1.5◦C (0◦C forsingle layer plastic without thermal screen, 1.5 for a glass glazing with thermal screen). Since no ventilation is doneduring winter nights the night-time ventilation set point should be assigned a high enough value; it is simply takenequal to the daytime one.

3.1.5. Substrate temperature set pointLow substrate temperatures tend to weaken the vigour of the crop and its resistance to the development ofBotrytis.

On the contrary, higher substrate temperatures tend to strengthen the crop vigour. When the greenhouse has secondaryheating pipes installed close or against the rooting medium (a different heating system from the main pipes that areoften installed between two crop rows), the water temperature of these pipes is defined. A base temperature of 16◦C ismodified according to this knowledge. It is decreased to reduce the vigour from strong to normal. In case ofBotrytisrisk or occurrence, this temperature is increased to increase the vigour from weak to normal.

3.1.6. Minimum water vapour pressure deficitThe minimum VPD is determined to fulfil several goals. First, it must be high enough to preventBotrytis outbreak

or development. In case of declared risks or observed presence of the disease, the minimum VPD is increased to dryout the air. The VPD is also used to sustain crop transpiration when the available radiation is too low to maintain theminimum transpiration flux necessary to avoid calcium deficiency and blossom end rot (Aikman and Houter, 1990;Stanghellini et al., 1998). The increase in VPD is computed as follows. A simplified Penman-Monteith equation isused to estimate the transpiration rate due to the forecasted irradiance and minimum VPD. If the result is lower thanthe threshold transpiration rate, then the minimum VPD set point is increased to reach the threshold. This correctionis only realised for the daily VPD set point.

3.1.7. Maximum water vapour pressure deficitSetting a maximum VPD also fulfils several goals. It is used to modify the vigour of the crop and to protect the

crop against high potential evapotranspiration when it occurs after a day with a low potential evapotranspiration. HighVPD tend to weaken the crop vigour; such values are avoided when the crop has a weak vigour or when the vigour isgood but weakening. Along the Mediterranean coast (such as in the areas of Perpignan or Berre, France), contrastingweather can occur from one day to the next, with a humid and overcast day followed by a dry and sunny day. In suchsituations, the crop endures a sharp increase of the potential evapotranspiration and may show some signs of waterstress on the dry day. To avoid such problems, it is possible to maintain the crop in a more humid air by confining thegreenhouse by lowering the maximum VPD set point.

3.2. Mild night cases

The mild night knowledge base only differs from the winter one in the determination of the night-time temper-ature set points. In both these knowledge bases, the night-time average temperature must belong to a fuzzy domaindepending on the crop variety type and stage. In the mild night case, a thermal balance model of the greenhousehas shown that some values of this domain cannot be attained if the greenhouse remains closed, even withoutany use of heating. To reach these values (which are considered as agronomically suitable) the use of ventilationduring the night-time period is necessary. The night-time heating set point is taken as the minimum value of thefuzzy domain of the night-time average temperature. The ventilation set point must be above this value by at least2◦ to limit energy losses. In addition, this set point depends on the average night-time temperature and on the so-called natural night-time temperature (the average temperature in the greenhouse when no heating or ventilation isused).

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3.3. Hot day cases

Hot days are defined as days where the control devices of the greenhouse cannot control the temperature becauseof the high outside day (and possibly night) temperature. Therefore, this knowledge base cannot define target averagetemperatures to achieve by the application of the set points. So the knowledge directly defines the values of the setpoints. They are all defined by unary (single variable) constraints of equality type; the set points do not depend oneach other. The second consequence of this approach of the hot day cases is that the solution proposed for hot days isidentical day after day.

The heating set points are set so as to avoid low greenhouse temperatures that might hinder the growth and develop-ment of the crop, although such situations should not occur granted the forecasted outside weather that has triggeredthe activation of this knowledge base. The ventilation set points are set at values ensuring that ventilation occursas soon as possible during daylight time to limit the temperature increase in the greenhouse because of the highirradiance.

4. Software implementation

The SERRISTE application by itself is built as a shell around the CON’FLEX solver. It has been implemented inC++ and all inputs and outputs take place through files. This architecture has been chosen to separate the SERRISTEapplication from user interfaces that would use it because SERRISTE has been designed to be compatible with mostof the greenhouse climate computers available and its objective is to be integrated in these climate computers, shouldthe companies owning these systems want it.

The inputs to SERRISTE are placed in two separate files, one describing the static aspects of the situation (locationof the greenhouse; greenhouse cover type, heating system, crop variety) and one describing the dynamically evolvingaspects of the crop and of its environment (measured climate in the greenhouse, crop state, weather forecasts). Thesefiles must be prepared once a day, at the end of the pre-dawn period, so that SERRISTE, which works off-line oncea day, can output the solution for the coming day, spanning from the daylight period to the next pre-dawn period.Climatic data are output by the grower’s climate computer through some sort ofreport function. User supplied data(especially crop state) correspond to slowly moving variables and may be supplied either in the early morning, justbefore the run of SERRISTE, or during the previous evening.

Using the information in these files, the application follows the procedure described in Section2.5.1 to selectand deparameterize constraints that will form the active constraints set (bottom rectangle,Fig. 2) describing thecurrent situation. These constraints are written in a file and the CON’FLEX program, launched by the SERRISTEapplication, searches for solutions and writes them in a new file. Upon termination of the CON’FLEX program,the SERRISTE application continues by reading the file containing the CON’FLEX outputs. The selection pro-cedure described in Section2.5.3 is applied to choose the most appropriate solution for the current situation.The selected solution and some more technical information are then written in the output file of the SERRISTEapplication.

This architecture also allows the application to run without the supervision of the grower, provided that the dynamic(mainly climate) file has been automatically prepared. This is possible under the hypothesis that the crop states, theonly information which requires a human observation, did not change significantly since the last observation, or thatthis information has been provided offline, sometime before the run of SERRISTE, e.g. during the previous day. Theother necessary information is either available from the climate computer (measured climate inside and outside of thegreenhouse during the previous day), or from weather forecasts sites on Internet. It is therefore possible to programSERRISTE to run daily with minimum human interaction or even with no human interaction at all. In this automaticmode, the outputs of SERRISTE can also be fed directly to the climate computer, which will then apply the new setpoints.

For experimental and demonstration purposes, a graphical user interface (GUI) has also been developed, using JAVA.The GUI (Fig. 6) is designed as a shell around the SERRISTE application. It not only allows for using SERRISTE, butalso provides facilities to organise the data (separate folders for the static and dynamic data and for the solutions) or toswitch between different units when viewing a solution (VPD is commonly expressed as a pressure, hPa, as a mixingratio, g H2O kg−1 dry air, or as relative humidity, %).

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Fig. 6. SERRISTE graphical user interface. The set point form. The upper panel offers three menus: “Commandes” (commands, allowing to viewand manage files and launch the SERRISTE application per se), “Licences” (licenses, displaying the licenses of the tools used and of SERRISTE)and “Aides” (help, allowing to display the help on SERRISTE GUI usage, the agronomical background and offering links to Internet weatherforecasts sites). On the left, two panels group the known greenhouse descriptions (“Serres”, upper part) and the daily data files (“Donnees”, lowerpart). The main panel shows the set points and the submenu to choose the unit for VPD display.

5. Experimental performance assessment

5.1. Experimental setup

To assess the performance of SERRISTE, comparisons have been carried out between the climate management ofindependent advisers and that proposed by SERRISTE. These experiments have taken place at research and development(R&D) stations located near the main tomato producing areas of France (CTIFL, Balandran, south-east; AIREL, SainteLivrade sur Lot, south-west; CATE, St Pol de Leon, Brittany). In each case, two greenhouse compartments were devotedto the experiment, and were planted on the same date with the same variety. The adopted varieties were of the beeftype and were also the most commonly cultivated one in each of the areas where the R&D stations were located, andat the time of the experiment. One of the two compartments was run by the local manager, the second by anotherperson strictly applying the set-points proposed by SERRISTE. The local manager was not aware of the set-pointsapplied in the other compartment but could of course observe the crop in it. This setup was intended to achieve themaximum possible independence between the two climate management strategies. Fertigation and pest control weremanaged according to similar rules, but adapted to the current crop state, thus possibly differing when the climatemanagement implied differences in the crop state.Table 3summarises the main cropping parameters adopted in thedifferent locations. In each compartment, temperature, VPD, irradiance and set points were recorded by the climatecomputer. Weekly measurements were performed on the crops, to measure the number (position) of the flowering andharvested trusses, the length of the plant, the vigour of the crop, and the yield (number and weight of fruits).

5.2. Comparison results

Before comparing the SERRISTE management to that of independent crop managers, the validity of the rules usedby SERRISTE can be estimated in two steps. The first step is to verify that the set-points issued by SERRISTE do yield

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Table 3Cropping parameters of the four experimental sites

Site Variety Planting date Density (plants/m2) Greenhouse type Substrate

AIREL Tellus January 16, 1996 2.37 Glass multispan RockwoolCTIFL Twin December 20, 1995 2.0 Glass multispan RockwoolCATE 1 Daniela January 30, 1996 2.8 Multispan Venlo RockwoolCATE 2 Daniela January 20, 1997 2.25 Multispan Venlo Rockwool

the desired climate (assessment of the internal consistency of the rules and of a good representation of the greenhousephysical processes). The second step is to verify that a crop grown according to SERRISTE develops properly andgives a satisfactory yield. If the set points of SERRISTE do yield the climate also selected by SERRISTE, then thissecond step can be considered as an agronomic validation of the constraints used to determine the daily solution. Inthe following, this second step is done by comparing the crop behaviour under SERRISTE management and under areference management.

5.2.1. Ability of SERRISTE to achieve the desired greenhouse climateFig. 7 compares the time course of desired and achieved daytime and night-time average temperatures in the

SERRISTE compartment, in the CTIFL site. It can be seen that, for both periods (daytime and night), desired andachieved temperatures were close, and that even when they were different they followed the same trend. During winter,achieved night temperatures were almost equal to the target temperature used to determine the heating and ventilationset-points. Later in the season, discrepancies appeared as the achieved temperature became higher than the target. Fordaytime temperatures, differences also existed during winter but did not exceed±1◦C; a pairedt-test performed onthese data did not reveal any significant difference. Later in the season, the achieved daytime temperatures tended tobe regularly higher than the target, sometimes by up to 2◦C; a pairedt-test revealed that the two target and achievedtemperatures after April 1st differ significantly and the average difference was 0.8◦C. These results were confirmedby the observations made at the other two French sites.

Fig. 8 shows the evolution of VPD in the greenhouse and the lower and upper bounds set by SERRISTE for thesame experimentation. It can be seen that, until March 14th, the daytime lower bound was not always respected, theachieved VPD being around the threshold. This situation occurred during this first period about half of the time, butin four cases out of five, the achieved VPD did not exceed the minimum threshold by more than 0.7 hPa (the fourthquintile of the difference during this period is 0.677). After this date, the daytime VPD was higher and respects betterthe lower and upper thresholds. As expected, higher vapour deficits were observed in late spring. On the contrary,night-time VPD, until April 7th, tended to exceed the maximum threshold, but was of less consequence. In late spring,the night-time VPD decreased, mainly because of a limited use of heating imposed by the local manager who decided

Fig. 7. Comparisons of the day- and night-time temperatures in the SERRISTE compartment. Solid line for expected, dashed-line for achievedtemperatures (CTIFL, 1995–1996).

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Fig. 8. Comparisons of the achieved (dashed line) water vapour deficit with the lower and upper bounds set by SERRISTE (solid lines). Uppergraph: daytime values, lower graph: night-time values.

to only use heating pipes located within the crop itself (a choice which implies limiting the maximum pipe temperatureto about 30◦C to avoid leaf burning).

5.2.2. Comparison of the SERRISTE climate against a reference managementFig. 9shows the evolution of daytime and night temperatures resulting from the SERRISTE control and from the

independent greenhouse manager decisions (called the reference), during the period where the climate can effectivelybe controlled. It can be seen that SERRISTE maintained higher differences between day and night, either by allowinghigher day temperatures (first part of the season), or by allowing lower night temperatures (end of the graph). Later, whenthe outside weather was too warm to allow real control of the greenhouse climate, the two temperature managementpolicies achieved about the same daytime and night temperatures (not shown). During the period where the climatecan effectively be controlled, the average day-to-night temperature difference under SERRISTE control was 3.4◦C,as compared to 2.7◦C under the reference control. A student test at 99% confidence level revealed that the twomanagements differed significantly on this point. The resulting daily averages were not very different (Fig. 10). Thetwo management protocols did not differ by more than 34 degree day (over more than 250 days), 28 degree day in thecase shown inFig. 9. A closer examination shows that when the crop was young (crop stages 1 and 2), SERRISTEtends to achieve higher daily averages than the reference, and lower afterwards.

Fig. 9. Day and night temperatures in the SERRISTE and reference compartments, CTIFL, 1995–1996. Upper graph compares the day to nighttemperature difference in the SERRISTE and in the reference compartments (solid line: SERRISTE, dashed-line: reference).

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Fig. 10. Time evolution of the average daily temperature integrals (solid line: SERRISTE, dashed line: reference, left axis) and of their difference(positive values when SERRISTE is higher, negative otherwise, right axis).

As shown inFig. 11, SERRISTE adopted a consistently lower VPD (higher relative humidity) than did the referencemanagement, during daytime as well as during night. However, at all three locations where SERRISTE was tested, nodifference inBotrytis outbreak was observed under the two managements. In the two southern locations, noBotrytisat all was observed, and in the CATE experiment, some plants were affected and had to be removed from the crop, butin comparable numbers and at about the same period for the two managements. Granted that high VPD in winter isachieved by the combined use of ventilation and heating (dehumidification), the higher VPD level maintained underthe reference management implies higher running costs than the SERRISTE management at the same time.Fig. 11also shows that the two managements follow the same trend over time.

5.2.3. Comparison of crop behaviours under the two climate managementsIn all experiments, the flowering rates under the two managements were almost comparable. In the SERRISTE

compartments the flowering rate was a little lower than in the reference compartment, ending up with one trussdifference after more than 40 weeks of cultivation. However, a paired Student’st-test performed on the weekly rate offlowering or on the final number of flowered trusses did not reveal any significant difference. The number of trussesat harvest followed exactly the same pattern. The fruit load (number of fruits currently on the plant) was also almost

Fig. 11. Compared evolution of VPD in the greenhouses under SERRISTE (solid line) and reference (dashed line) managements, for day (uppergraph) and night (lower graph).

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Fig. 12. Weekly and cumulated harvest under the two climate managements, in the CTIFL experiment (solid line: SERRISTE, dashed line: reference).

identical under the two management strategies at each experimental site. The length of the plants was also comparableduring these experiments (final length as well as weekly increase).

SERRISTE bases part of its rationale on the appraisal of the vigour of the crop. The evolution of this indicatorduring the experiments does not reveal any difference between the reference and the SERRISTE management. In theCTIFL experiment (south-east France), under the two managements, the vigour weakens and remains low during thefirst 5 weeks after plantation, and improves afterwards to remain good or sometimes strong. In the experiments carriedout in Brittany (CATE), the vigour and two other indicators related to it (stem diameter below the highest floweringtruss and the distance from this truss to the top of the plant) show a different pattern, but are identical under the twomanagements. In this case, the vigour started out as strong, then decreased after 7 weeks after plantation and wasmaintained at a satisfactory level for the rest of the cultivation period.

At all experimental sites, the local managers noted that the crop under the SERRISTE management had a tendencyto look more vegetative and they estimated that it probably had a higher leaf area index than did the crop under thereference management (but no measurements were taken). Moreover, during summer, the crops in the SERRISTEcompartment suffered less from high temperatures (which were identical under the two managements) and were ableto maintain a better fruit quality (bigger fruit size, less microcracks). The local managers attribute this behaviour to amore developed rooting system in the SERRISTE case, which can be the result of the extra efforts SERRISTE takesduring phase 1.

The total yield achieved with the climate control of SERRISTE was in two cases slightly higher and in one caseidentical to the reference (Fig. 12, one example). However, under the control of SERRISTE, the date of first harvest wasabout 1 week later than under the reference management. After 4–5 weeks, the cumulated harvest in each of the twomanagements became identical and remained so until the end of the cultivation period. The higher harvest achieved inthe SERRISTE case was obtained by bigger fruits (390 g/fruit with SERRISTE versus 308 g/fruit with the referencemanagement, on the average) rather than by more fruits (102 fruits/m2 with SERRISTE versus 117 fruits/m2 with thereference management).

6. Discussion

The discrepancies between expected and achieved temperatures observed inFig. 7can have several explanations.First, SERRISTE uses a very approximate model to describe the physical relation between set points, outside weatherand inside greenhouse climate. Not only is this model approximate, it is also not tuned for each greenhouse to whichSERRISTE is applied, because this is beyond what can be easily done in a commercial situation. Second, the translationbetween average temperature and set points also depends on the characteristics of the climate computer regulating thegreenhouse climate. For SERRISTE, this computer is supposed to work properly, which is not always the case.For example, at the beginning of the experiment at one of the experimental sites, the climate computer limited thetemperature of the water coming out of the boiler to a low level, which did not allow proper night temperature control

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during a cold night. This constraint resulted in an achieved night-time average temperature that was much lower than thetarget value determined by SERRISTE. After correction of the relevant climate computer parameter, this undesirablebehaviour ceased. Finally, the relation between average temperature and associated set points depends on the outsideweather and is computed, in SERRISTE, using weather forecasts. A more or less inappropriate set-point choice willresult when there is a difference between the forecasts and the actual outside weather. The better the forecast quality,the better the set-point determination. Granted these approximations, the ability of SERRISTE to achieve good averagetemperatures by its set point choice was demonstrated (Fig. 7). The same conclusion applies to the overall daily averagetemperature (data not shown).

During daytime, the VPD almost always respected the higher bound. Deviations to the lower limit, which are veryimportant to pest control, are limited to acceptable values. Unacceptable VPD values (less than 1 hPa during night-timeand less than 2 hPa during day-time) are seldom found. The tighter proposed limits of the VPD at night as comparedto the daytime limits made control more difficult. Therefore, although the set points proposed by SERRISTE do notalways result in the desired VPD, the deviations, especially during daytime, can be taken as acceptable. From theseresults, confirmed by those obtained at the other experimental sites, it can be concluded that SERRISTE is able toproduce set points that will acceptably maintain the VPD in the greenhouse within the desired range.

The knowledge included in SERRISTE, and especially the over-simplifications made to describe the physics of agreenhouse, leads to a correct choice of set points in relation to the targeted climate.

The temperature management of SERRISTE differs from the reference one because it is daily adapted to weather fore-casts and crop state. The daily averages are more variable than in the reference management against which SERRISTEwas assessed, although about the same temperature integral is achieved. SERRISTE also uses a wider temperaturerange and generally achieves higher day/night differences. One of the consequences is that the management of SER-RISTE under normal conditions saves energy, especially in winter. However, when the user requests preventive measuresagainstBotrytis development, the management of SERRISTE changes towards higher VPD and becomes more energy-expensive. Energy consumption has been estimated during these experiments using the greenhouse model ofBoulardand Baille (1987). Energy consumption measured during the experiment held in CATE in 1996–1997 show that SER-RISTE saved 6.2% energy as compared to the reference management. The savings might have been greater if the localmanager had not already monitored the SERRISTE experiment during the previous year. Based on that experience,he adopted a different management, tolerating more a humid climate in the greenhouse than he was used to, on thebasis that this was the behaviour of SERRISTE the year before and that this behaviour did not result in an increaseof Botrytis or other diseases in the greenhouse. Estimated energy savings for this same experiment amount to 4.9%(the energy model underestimates the real energy consumption because it does not take into account the energy spentby simultaneous heating and ventilation for dehumidification). On the same site, but for the previous year where thereference management consistently used a minimum pipe temperature to avoid high humidity, the model indicates 20%energy savings. Values of the estimated energy savings for the experiments held at the other sites are 8% (AIREL)and 5% (CTIFL). Granted that energy represents about 1/3 of the running costs of this production, the overall savingsamount to 2–6%, which is not negligible.

The VPD managements of SERRISTE and of the reference showed the same behaviour. However, SERRISTE isless conservative than the reference managers against whom it was assessed as it consistently maintains lower VPD(higher relative humidity), but without harm to the crop. Very low VPD (less than 2 hPa during daytime and less than1 hPa during night-time) were almost never observed, so that, although less conservative, SERRISTE avoids very riskyhumidity levels in the greenhouse. This is probably the reason thatBotrytis cases had the same null or low frequencyunder the two managements. The lower VPD maintained by SERRISTE also contributes to the low energetic cost ofits climate management.

At every experimental site, the general behaviour of the crop grown under the control of SERRISTE was appraisedas good. The local managers considered that these crops were as fit as the reference one. They even agreed that theSERRISTE-managed crops better endured hot summer conditions because of their tendency to be more vegetative andtherefore to sustain a higher transpiration. It must be stressed that although the crops in the SERRISTE compartmentswere qualified as more vegetative, they had at least the same production as the reference ones, while there is consensusamong advisers that vegetative crops waste dry matter in the leaves and stems at the expenses of the fruits. SERRISTEeven managed to produce bigger fruits than did the reference management. Although this is not a problem for beeftomato production because bigger fruits are wanted, this can be an impediment in the case of cherry tomato productionas was noted in an experiment carried out in Geneva in a commercial greenhouse. In this case, the fruits were bigger than

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usual (but the number of fruits per truss was not modified) and close to the limit size which would have excluded theproduction from being sold under the targeted commercial label. Forcing SERRISTE to adopt higher temperatures (byindicating that the achieved temperature integral was too low) during 10 days after detection of this behaviour solvedthe problem. A possible reason which has made this behaviour even clearer is that the manager regularly appraised thevigour of its crop as weak, whereas the local adviser judged it as correct. Under weak vigour indication, SERRISTEadopts lower temperatures. The crop development is slowed down, fruits take longer to mature and thus can receivemore assimilates. It must be stressed, however, that during the development of SERRISTE, its targeted production waswinter-planted beef tomatoes and not cherry tomatoes. It is therefore rather surprising that this fruit size increase wasthe only negative consequence observed in this case.

Under the management of SERRISTE, the date of the first harvest was 5–7 days late as compared to the referencemanagement. This observation was made in all experiments and continues to be verified where SERRISTE is used.SERRISTE maintains higher temperature integrals during the early stages of the crop (Fig. 10), which should maintain ahigher development rate and thus should benefit to the first harvesting date. But, at the same time, SERRISTE maintainshigher day-to-night temperature differences and lower night-time temperatures, which may explain the first harvestdelay. However, since the harvest delay is a constant result of the application of SERRISTE, it can be compensated forby moving the planting date 1 week ahead, which gives the appropriate result according to more recent observations.

7. Conclusions

The experiments described above and today’s use of SERRISTE in commercial situation, have proven the ability ofSERRISTE to provide climate set points resulting in a proper crop behaviour and suitable production. The knowledgeused in SERRISTE can be considered as valid, both in terms of greenhouse physics and agronomy. SERRISTE alsoallows for some energy savings, the level of which depends on the management to which it is compared. In northernlocations where managers tend to spend a lot of energy in humidity control, SERRISTE will most likely achievehigher savings by adopting a less conservative management, except when needed, either becauseBotrytis is observedor because the manager considers that the current weather is favourable to its development. It must be stressed thatSERRISTE was compared to expert growers and did at least as well as they did. It can be concluded that SERRISTE canbe of great benefit for low or less skilled growers or for less experienced growers who gain access to expert knowledgeand advice through SERRISTE.

The main agronomic drawback of the use of SERRISTE is the 1 week delay of the first harvest, which was ofimportance a few years ago under varying market prices but tends to be of less consequences today where the marketprices are more constant throughout the year, at least in France. However, this drawback can be easily avoided byplanting 1 week earlier.

SERRISTE has been designed to run daily and requires daily updated information to do so. Although not relevantduring the experimental phase, some have been critical of the additional daily workload. First, the main advantagesof SERRISTE are that it adapts the set point choice to the current conditions, among which are the outside weather.Only under stable conditions (weather, crop, etc.) could the daily obligation to run SERRISTE be by-passed. Second,SERRISTE saves the manager the time taken to decide on the set-points, but this thinking is often done while themanager visits the greenhouse in early morning, and he has the feeling that it does not take time to decide upon theset points. Third, SERRISTE moves the manager’s workload from deciding upon the set points to appraising the cropstates. This shift can only be beneficial to the manager. Finally, SERRISTE has been designed to produce set points thatare compatible with most of the climate computers available today. If the companies making these climate computerswanted, they could easily develop an interface between SERRISTE and their system. The only information that themanager would have to input are the crop vigour, the occurrence or risk ofBotrytis and the state of the temperatureintegral buffer. Such an interface has been developed by one company who has taken a licence to sell SERRISTE. Afterabout 1 year trial of this interface, the conclusion is that the daily use of SERRISTE is perceived, not as a hassle, but asa small time spent for a highly beneficial return. It is, however, regretful that in spite of its good results, SERRISTE isnot more widely available and that the development advisers do not see SERRISTE as a valuable tool but as a rival, soto say. The only case where SERRISTE has been adopted by a development adviser prior to its use by the growers isthe Geneva case. In this case, SERRISTE has been used by the adviser as a tool to reinforce his advices with scientificknowledge. Basing his advices on SERRISTE was also the base for a more in-depth explanation of the inter-relationsbetween the greenhouse climate and the crop behaviour.

46 M. Tchamitchian et al. / Computers and Electronics in Agriculture 50 (2006) 25–47

From the beginning of the project to the first testable prototype, it required 6 years of work, and dedicated financialsupport covered only 4 years of this period. Two additional years were expended in carrying out the tests in thedifferent French R&D stations. At that time the project ended, at least from the scientific point of view. Although nomore financial support was available, the project continued to live, at least to find and convince a greenhouse computerfirm to licence SERRISTE, because the French growers continued to express their desire to be able to use such a tool.More experiments, or more exactly test uses, have been carried out, based on the good-will of development advisersand growers themselves, as in the Geneva case. Only recently did these efforts to maintain SERRISTE pay dividends,as it has started to be used regularly by some growers and has been licensed by a greenhouse computer firm.

Acknowledgements

The authors gratefully acknowledge the contributions of other scientists involved in the SERRISTE project: BoulardT., Cros M.-J., Kovats K., Mermier M., Montbroussous B. and Reich P.

J.-P. Rellier helped in adopting the CON’FLEX solver in late versions of SERRISTE. The R&D stations involvedin the experimental validation of SERRISTE deserve special mention, namely:

• the AIREL in Sainte Livrade sur Lot;• the CATE in Saint Pol de Leon;• the CTIFL, station of Balandran;• theEcole de Saint Ilan.

The authors also wish to thank Mr Gigon of the “Office Technique de Culture Maraıchere de Geneve” and thegrowers working with him who agreed to apply SERRISTE in their greenhouses.

References

Aikman, D.P., Houter, G., 1990. Influence of radiation and humidity on transpiration: implications for calcium levels in tomato leaves. J. Hortic.Sci. 65 (3), 245–253.

Aung, L.H., 1976. Effects of photoperiod and temperature on vegetative and reproductive responses ofLycopersicon esculentum Mill. J. Am. Soc.Hortic. Sci. 101, 348–360.

Baille, A., Boulard, T., Gary, C., 1990. Les criteres d’optimisation dans la gestion du climat et de la production sous serre. C.R. Acad. Agric. Fr. 76(5), 25–36.

Bertin, N., Gary, C., 1992. Tomato fruit set and competition for assimilates, during the early production period. Acta Hortic. 303, 121–126.Bertin, N., Heuvelink, E., 1993. Dry-matter production in a tomato crop. Comparison of 2 simulation models. J. Hortic. Sci. 68 (6), 995–1011.Bertram, L., 1992. Stem elongation ofDendranthema and tomato plants in relation to day and night temperatures. Acta Hortic. 327, 61–69.Bot, G.P.A., 1983. Greenhouse climate: from physical processes to a dynamic model. Ph.D. thesis, Wageningen Agricultural University (NLD).Boulard, T., Baille, A., 1987. Analysis of thermal performance of a greenhouse as a solar collector. Energy Agric. 6, 17–26.Boulard, T., Baille, A., 1993. A simple greenhouse climate control model incorporating effects of ventilation and evaporative cooling. Agric. For.

Meteorol. 65, 145–157.Davis, E., 1987. Constraint propagation with interval labels. Artif. Intell. 32 (3), 143–157.de Koning, A.N.M., 1990. Long-term temperature integration of tomato. Growth and development under alternating temperature regimes. Scientia

Hortic. 45, 117–127.de Koning, A.N.M., 1992. Effect of temperature on development rate and length increase of tomato, cucumber and sweet pepper. Acta Hortic. 305,

51–55.Dubois, D., Prade, H., 1988. Possibility Theory: An Approach to Computerized Processing of Uncertainty. Plenum Press, New York, USA.Heuvelink, E., 1989. Influence of day and night temperature on the growth of young tomato plants. Scientia Hortic. 38, 11–22.Iqbal, M., 1983. An Introduction to Solar Radiation. Academic Press, Toronto (CDN).Khayat, E., Ravad, D., Zieslin, N., 1985. The effects of various night temperature regimes on the vegetative growth and fruit production of tomato

plants. Scientia Hortic. 27 (1), 9–13.Martin-Clouaire, R., Boulard, T., Cros, M.-J., Jeannequin, B., 1993a. Using empirical knowledge for the determination of climatic set-points: an

artificial intelligence approach. In: Hashimoto, Y., Day, W. (Eds.), The Computerized Greenhouse. Academic Press, New York, NY, USA, pp.197–224.

Martin-Clouaire, R., Kovats, K., 1993b. Satisfaction of soft constraints applied to the determination of greenhouse climate setpoints. In: ArtificialIntelligence for Agriculture and Food. Nımes, FR, pp. 211–220.

Martin-Clouaire, R., Kovats, K., Cros, M.-J., 1993c. Determination of greenhouse climate setpoints by SERRISTE—the approach and its object-oriented implementation. AI Appl. 7 (1), 1–15.

M. Tchamitchian et al. / Computers and Electronics in Agriculture 50 (2006) 25–47 47

Martin-Clouaire, R., Schotman, P.J., Tchamitchian, M., 1996. A survey of computer-based approaches for climate management. Acta Hortic. 406,409–423.

Rellier, J.-P., Vardon, F., Gaspin, C., Martin-Clouaire, R., Schiex, T., 1996. CON’FLEX: Un logiciel de resolution de problemes de satisfaction decontraintes (CSP). Technical report, INRA-BIA,http://www.inra.fr/bia/produits/logiciels/Pagehome.php, in French.

Rijsdijk, A.A., Vogelezang, J.V.M., 2000. Temperature integration on a 24-hour base: a more efficient climate control strategy. Acta Hortic. 519,163–169.

Seginer, I., Boulard, T., Bailey, B.J., 1994a. Neural network models of the greenhouse climate. J. Agric. Eng. Res. 59, 203–216.Seginer, I., Gary, C., Tchamitchian, M., 1994b. Optimal temperature regimes for a greenhouse crop with a carbohydrate pool: a modelling study.

Scientia Hortic. 60, 55–80.Stanghellini, C., van Meurs, W.T.M., Corver, F., van Dullemen, E., Simonse, L., 1998. Combined effect of climate and concentration of the nutrient

solution on a greenhouse tomato crop. II. Yield quantity and quality. Acta Hortic. 458, 231–237.Tsang, E.P., 1993. Foundations of Constraint Satisfaction. Academic Press, New York, NY, USA.Udink ten Cate, A.J., Challa, H., 1984. On optimal computer control of the crop growth system. Acta Hortic. 148 (1), 267–276.