SERRISTE: A daily set point determination software for glasshouse tomato production

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

SERRISTE: A daily set point determination software for glasshouse tomato production M. Tchamitchian a,∗ , R. Martin-Clouaire b , J. Lagier c , B. Jeannequin c , S. Mercier d a

´ Ecod´ eveloppement, INRA Domaine St Paul, 84914 Avignon Cedex 9, France b UBIA, INRA Toulouse, BP 27, 31326 Castanet Tolosan, France c SAD, INRA Domaine du Mas Blanc, 66200 Alenya, France d PSH, Bˆ at 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 is based on the mathematical formalisation of expert practices and scientific knowledge, as a constraint satisfaction problem. The structure of SERRISTE is presented, as well as the knowledge used to describe the relationship between the crop behaviour and the greenhouse climate, and the relationship between set points and the resulting greenhouse climate. The performances of the system have been tested in three different locations in France by applying a blind reference management and SERRISTE management to two identical greenhouse compartments at each location. The main results are that SERRISTE maintains higher day to night temperature differences and lower vapour pressure deficit than the reference management, and leads to energy savings in the range of 5–20%. The SERRISTE crop yields at least the same harvest as the reference one. Moreover, the crop behaviour in summer is enhanced 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. When properly equipped with climate control devices, the greenhouse becomes a factory for intensive crop production with high running costs (as compared to production under tunnels with little control equipment or in the open field). The management of the greenhouse is therefore a significant activity for the grower in which he has to assign priorities between 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 cascade of three levels (Udink ten Cate and Challa, 1984; Baille et al., 1990). At the highest level (level 2) the grower decides upon the crop to be planted (species and variety), the timing of the production, etc. He sets up the configuration for the production. 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: [email protected], [email protected] (M. Tchamitchian), [email protected] (R. Martin-Clouaire), [email protected] (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. Decisions about the technical operations performed directly on the crop (pruning, training, etc.) are also made at this level. The final level (level 0) is where actions are taken to bring the system (the greenhouse and the crop) into the intended state specified at level 1. The commercial climate computers used in greenhouses work at this level because they regulate the climate and the fertigation so as to comply with the set points fixed by the grower. The decisions made at levels 2 and 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 weekly activity, which must be undertaken with great care. The consequences of the decisions are not all immediately visible and are hardly reversible in many cases. For example, a change in the daily mean temperature will result in a change in the rate of new organ production, which will only be perceivable within a week or two; it will also result in a change in crop vigour (the concept of crop vigour will be defined and discussed later) 2 or 3 days later. This example also shows that a given decision about the environmental conditions may have impacts on different goals, positively or negatively. The goals might be partially conflicting. To make an appropriate choice of set points the grower must first define the goals that he assigns to the crop and their order of priority; this should be done within the frame set by the decisions made at level 2. It is also necessary to take into account the weather forecasts because outside conditions strongly influence inside conditions and the way to control them. Finally, the grower must have some knowledge of the 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 obtains access to a wider knowledge. The adviser is often more educated that the grower and updates continually his knowledge as part of his job. He also has a wider experience in so far that he is counsel to many growers, thus multiplying the number of cases he knows and analyses. Several attempts have been made to exploit scientific knowledge and especially crop growth models to determine the optimal 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, still in the scientific development phase, and not ready for testing or dissemination. Since this survey, the literature has reported very few works that have matured enough to be used by growers. Rijsdijk and Vogelezang (2000) describe an algorithm to regulate the temperature in the greenhouse which optimises the hourly set point based on the estimated cost of heating (using weather forecasts) and on a 24 h mean temperature goal. The average temperature and the lower/upper bounds during night and day are provided by the grower. This algorithm has been implemented in one commercial greenhouse computer and is available to growers. However, it does not provide a full support for the climate management of the greenhouse because the choice of the daily average temperature is left to the grower; a choice that does 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 management of the greenhouse, dedicated to tomato production. SERRISTE (greenhouse grower in French) provides the grower with a proposal of daily greenhouse climatic set points. Basically, the set points are generated every morning by processing an agronomic knowledge base in function of the specific data concerning the current state of the crop and the weather forecasts for the next 24 h. In Section 2, the system is described through its objectives and the computational approach that it implements. Section 3 presents the knowledge that is processed by the system. Section 4 describes how SERRISTE has been implemented and the graphical user interface. Section 5 reports on the experiments carried out to assess the performance of SERRISTE. Section 6 is devoted to an analysis of the agronomic results. The paper concludes 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 advisers or expert growers to manage the greenhouse climate can profitably be encapsulated and exploited in a set point determination software (Martin-Clouaire et al., 1993a,c). Indeed, the growers and their advisers do have to decide weekly 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 selflearning, that SERRISTE aims at capturing and using to automate the decision-making process of greenhouse climate management. Hence, SERRISTE is based on the analysis of the discourse of some selected experts about their own way of choosing the set points. These experts were chosen for their knowledge of the practices that were widespread among French 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 production of a tomato crop grown under greenhouse in soil-less conditions and planted in autumn (October to December in European locations). Although restrictive, these conditions are those of the largest part of the French greenhouse tomato production, which is the first vegetable grown under greenhouse in France (and in Europe). Climatic set points are defined to be compatible with most commercial greenhouse computers. Therefore, they consist of the needed values to regulate air temperature, air humidity and the substrate temperature (more precisely the temperature of the pipes located directly against the growing medium, when they exist). However, commercial greenhouse computers do not regulate the climate to a given value (the true meaning of a set point), but between bounds. They define the heating set point and the ventilation 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 the crop such as disease outbreak prevention or production and development rates. Goals on production and development rates are not directly expressed by the grower who rather uses crop vigour as an indicator of the current rates. SERRISTE also has to deal with knowledge belonging to several domains such as ecophysiology, to integrate the crop responses to environmental conditions, agronomy, to exploit empirically-based knowledge or practices, and physics, to be able to 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 current state 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 at a time span shorter than a day. In most ecophysiologic models, crop growth and development are determined once a day, at which scale it is possible to balance the day’s photosynthetic activity and the consumption of assimilates by the respiration, 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. The term solution is used because the climate management is seen as a problem to solve. 2.3. Useful structures and data 2.3.1. Knowledge bases Greenhouse 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 almost impossible in hot situations like the Mediterranean summer. The knowledge used by SERRISTE must deal with these contrasted cases. To tackle this problem, the corpus used by SERRISTE is divided into three knowledge bases, each fit to a particular type of outside environmental conditions: • The winter knowledge base applies when the power of the control equipment allows achieving any desired greenhouse climate, without the need for night ventilation. • The mild-night knowledge base applies when the night weather is mild, possibly requiring the use of ventilation at night to maintain low temperatures in the greenhouse. • The hot-day knowledge base applies when the weather (day and night) is too hot for the greenhouse control devices to be able to regulate the climate. The rules used to determine which knowledge base applies to the current situation are given in Section 2.5.1.

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

Agronomic goals

Climate management

1

Reinforce rooting Sustain vegetative balance Ease fruit setting

Maintain air vs. soil temperature balance Maintain air temperature vs. solar radiation balance Avoid low air humidity

2

Ease fruit growth Sustain vegetative balance Prevent Botrytis outbreak

Maintain solar radiation vs. air and soil temperature Avoid high air humidity

3

Sustain vegetative balance Prevent Botrytis outbreak Care for development rate and photosynthetic activity

Maintain solar radiation vs. air and soil temperature Adapt air temperature to solar radiation Insist on dehumidification by heating and ventilation Protect plants from sudden weather change

4

Sustain fruit growth Sustain vegetative balance Sustain transpiration to provide enough minerals

Avoid excess air temperature Avoid low air humidity at day time Adapt air humidity to expected crop transpiration

2.3.2. Crop stages During 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 in terms 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 of objectives and means to reach them. They are presented in Table 1. As can be seen the goals shift from an emphasis on preparing the plants to sustain production, with good root system and vegetative part, to plants producing new fruits and growing the existing ones. For an autumn tomato crop, these stages correspond to key development states as shown in 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 division The 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 and night. 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 and daytime 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 (the greenhouse often stays closed at night and plants still transpire, even if at a low rate) and the air, crop and greenhouse material temperatures are at their lowest values, as is the outside air temperature. Therefore, risks of water condensation on leaves or glazing are high. Specific actions must be taken to prevent this condensation and thus avoid the associated risks of disease outbreak. In addition to make the crop able to respond to the sunlight that will come soon, it is necessary to increase canopy temperature so that photosynthetic activity will not be impaired, and to allow an increase in growth rate as soon as the assimilates are made available. Table 2 Crop stages and crop development states Stage

Crop initial development state

Estimated duration

1 2 3 4

Plantation Flowering 3rd truss Flowering 5th truss Harvest

At least 3 weeks 4–5 weeks 4 weeks 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 to dusk. Night-time lasts from dusk to the start of pre-dawn. Pre-dawn ends when the next daytime period starts. Pre-dawn therefore 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 the following, the word day means the 24 h day; the word daytime (or daylight time) will be reserved for the period of the day where there is light. The word night will designate the period where there is no daylight. The night is therefore composed of the two periods named night-time and pre-dawn. 2.3.4. Required data about the crop–greenhouse system Three 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 constant during the complete cropping season and allow determination of the physical properties of the greenhouse, especially the cost and efficiency of any heating policy. Although crop responses to temperature are determined by the same processes for all cultivars, a given average temperature does not imply the same intensity in the response of different cultivars. In SERRISTE, varieties are not individually addressed: a number of types have been defined, which group varieties with common behaviour with respect to temperature. For example, varieties requiring high temperature levels all along their development such as some beef cultivars are grouped in the heat demanding type. At the opposite, varieties with low temperature requirements are grouped in the cold tolerant type. The second type of information needed concerns dynamically evolving aspects of the system. This information consists of the measured past climate, inside and outside, of an appraisal of the crop state in terms of vigour and risk or occurrence of Botrytis 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 the climate that SERRISTE would have decided upon using measured data (of the outside weather) instead of forecasts to produce the solution. Any discrepancy in the greenhouse daily (24 h) average temperature due to inaccuracy of weather forecasts can be corrected. Indeed Heuvelink (1989) and de Koning (1990) have shown that the crop can compensate over a few days for deviations from an optimal average temperature. In other words, it is possible to compensate low temperatures (with respect to the supposed optimal temperature) by increasing the next day temperature, and vice versa, so that the achieved average over these 2 days is very close to the average of the optimal temperatures of these 2 days (see also Section 3.1.1). The second purpose in the use of past climate measurements is to enable computation of 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 previous period modifies that of the next. Their simple thermal balance model is used to estimate the energy requirement of the solution found by SERRISTE. It is written: Q = γRG − U1 (Tg − To ) − U2 (Tg − Tg,−1 ) where Q is the energy needed to maintain a greenhouse temperature of Tg , Tg,−1 is the greenhouse set-point during the previous time period, RG is the outside solar radiation, To the outside temperature, γ is the solar efficiency, U1 the overall heat loss coefficient and U2 the heat storage coefficient. The crop state is defined by two variables, one qualifying its vigour, the second its health status with respect to Botrytis. The crop vigour is a concept widely used and shared by growers and horticultural advisers in France to appraise the ability of the crop to sustain a good or high fruit yield while continuing to set new leaves and trusses at a pace ensuring an adequate future yield. The crop vigour therefore also gives an indication about the balance between vegetative and generative organs on the plant, as well as an indication about the ability of the crop to achieve sustained photosynthesis and water and mineral absorption rates. The appraisal of the vigour includes several visible aspects of the crop. The head of the plant is observed with respect to its bearing, colour, elongation and stem diameter. The overall colour of the crop is also considered, with the impression the crop gives of being etiolated or vegetatively unbalanced. Weak vigour is associated with plants seemingly etiolated, with stems of low diameter and long internodal distance, with a head of a light green with possibly a little shade of yellow, with weak trusses and a relatively small vegetative development. Such plants have a low potential for sustained production and are imbalanced because of a low photosynthetic activity compared to the demand in assimilates by growth and development. Strong vigour is associated with 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 imbalanced because they use too much assimilates for the vegetative development, thus limiting fruit growth. SERRISTE also needs 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 determine the energy cost of the solution found by SERRISTE. Solar radiation is also used to determine an adequate range for the 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 characterises a property that is relevant in the greenhouse climate management problem, such as the average temperature for a given 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 (MartinClouaire and Kov´ats, 1993b) that allows representation of fuzzy (flexible) constraints. More formally, all constraints have the general form: n


βi vi ∈ F

i=1

where βi is a rational coefficient, vi is a variable, n is the number of variables (n ≥ 1) and F is a fuzzy interval. A fuzzy interval (Dubois and Prade, 1988) is an interval having ill-defined boundaries such that some elements might have partial membership to it. A fuzzy interval is represented by its membership function. A trapezoidal function is usually sufficient to express practical knowledge. In such case, the fuzzy interval is fully defined by four numbers (δ, m, M, θ) as shown in Fig. 1. [m, M] is the interval of values that fully belongs to the fuzzy interval. [m − δ, M + θ] is the interval outside of which any value is out of the fuzzy interval. The membership degree of the values between m − δ and m are linearly interpolated between 0 and 1, 0 expressing non-membership and 1 full membership. Similarly, the values between M and M + 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 variables vi is computed on the [0, 1] scale as the membership of ni=1 βi αi in the fuzzy interval F as illustrated in Fig. 1. A constraint can apply to a single variable, in which case it directly defines the domain for the variable (the range of values that this variable can take). The crop stage constraints are of this type. For instance, for a heat demanding variety (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 constraints used in the knowledge bases are of this type. For instance, the difference between the daylight and night average temperatures must be greater than 2 ◦ C in northern France locations and lower than 6 ◦ C when the forecasted radiation level is high. These limits yield a constraint which states that the difference between the daylight and night average

Fig. 1. Fuzzy interval F.

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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 constraints by this assignment. Taking the minimum expresses that the overall satisfaction is equal to the degree of satisfaction of the least satisfied constraint. The degree of satisfaction of a solution is equal to 1 when all the constraints are fully satisfied. 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 interval F is here the crisp set of positive or null values. • Equality: v1 = v2 ⇔ v2 − v1 ∈ (0, 0, 0, 0). The fuzzy interval F 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 (violation of 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 the crop–greenhouse system. This process is discussed in Section 2.5.1. Once formulated as a set of flexible constraints, the greenhouse management problem can be solved by using a constraint satisfaction solver that searches for assignments of the variables that do not violate any constraint. The principles of constraint satisfaction and the software used in SERRISTE are outlined in Section 2.5.2. Finally, since the constraint solver may return a set of acceptable solutions, a selection procedure is used to find the best one according to context-dependent criteria described in Section 2.5.3. 2.5.1. From general knowledge to active constraints Fig. 2 schematically 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-obtained set of parameterised constraints has to be specialised in the sense that all the involved parameters have to be assigned values by taking into account data specific to the situation at hand. The process ultimately yields the set of active constraints, 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. A hot day can only occur from March 15th to October 31st (northern hemisphere); moreover, the forecast for the maximum outside temperature must be above a threshold set by default at 17 ◦ C but modifiable by the user, and the forecast for the solar radiation intensity must be high. If these conditions are met, the hot day knowledge base is chosen. Otherwise, SERRISTE computes the average night temperature in the greenhouse if no heating is used, using the forecast for the minimum outside temperature. To do so, SERRISTE uses the greenhouse energy balance model developed by Boulard and Baille (1987), which takes into account the greenhouse cover type (glass, simple or double-layer plastic) and the ratio of the greenhouse area in contact with outside air to greenhouse floor area. This computed temperature is compared to a threshold depending on the variety and crop stage. If it is above the threshold, then the mild-night knowledge base is chosen. Otherwise, SERRISTE uses the winter knowledge base. The temperature threshold selecting the mild-night knowledge base corresponds to the minimum average night temperature that might be desirable for this crop in the current crop stage. In other words, the mild-night knowledge base is chosen when night ventilation might be required. According to Table 2, the grower indicates the current crop stage. This information is used to select the appropriate set of constraints. The crop variety is later used to deparameterize these constraints. 2.5.2. Constraint solver A 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 association constitutes a unary constraint. A solution of the problem is an assignment of a value to each variable such that none of the constraints are violated. Depending on the problem, the objective is to determine whether a solution exists, 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 enforcing algorithms (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 connected to it through constraints. All values that cannot be part of a solution are removed (or filtered) from the domains of the corresponding variables. The most common algorithm for performing systematic search is backtracking, which traverses the space of partial solutions (i.e. not all the variables are yet assigned) in a depth-first manner. At each step the algorithm extends a partial solution by assigning a value to one more variable. When a variable is encountered such that none of the values in its domain is consistent with the partial solution, backtracking takes place and another assignment is tried. Backtracking can be greatly improved if used together with a consistency enforcing algorithm. The first such improvement, called forward checking (FC), is to look every time an assignment is made at each unassigned variable that is connected to the just assigned one by a constraint, and deletes in the corresponding domains the values that are not consistent with the value just chosen. If at any time some domain becomes empty, the algorithm immediately backtracks. Another commonly used possibility, called full-lookahead, is to apply an arc-consistency algorithm after each tentative assignment. The arc-consistency algorithm is also often used before any attempt to assign a variable. In the initial prototype version of SERRISTE, a dedicated solver was developed (Martin-Clouaire and Kov´ats, 1993b) using an artificial intelligence software environment. For higher efficiency and easier portability, the SERRISTE constraint 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 interval F 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 some point, the domains of the variables be discrete in order to support enumeration of candidate values. In SERRISTE the domains are continuous and have therefore to be discretized; the grain of discretization is variable-dependent and must be defined by the user. This transformation is delayed for each domain until a value has to be chosen for assigning the variable. Since the SERRISTE constraints are linear expressions over numerical variables, an efficient consistency enforcing algorithm based on interval computation methods (Davis, 1987) can be used and is directly provided by CON’FLEX. The SERRISTE constraints (including the variable domains) are defined with respect to fuzzy intervals. Any fuzzy interval can be approximated by a set of nested crisp (non-fuzzy) intervals, each associated to the degree equal to the lowest degree of membership of the values in the set. These sets are called α-cuts (Fig. 3). The precision of the approximation is defined by the user; typically the membership scale is decomposed in 11 levels from 0 to 1 by a step of 0.1. Therefore, by discretizing the membership range, one can approximate a fuzzy constraint by a finite family of weighted non-fuzzy constraints; the weight of each so-constructed constraint is the degree α of the associated α-cut Fα of the interval F. The degree α associated with any α-cut expresses that the values in this set are satisfying the constraint at least at the degree α. With this representation, the degree of satisfaction of a constraint by an assignment s of its variables is computed as: maxα min(α, µFα (s)) where µFα (s) is equal to 1 if s 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 be performed before starting the search. Other options concern the discretization step of the satisfaction range and various efficiency-related possibilities such as the order of visit of the variables, which currently is set such that the next variable to 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 Section 2.5.1 and returns a file containing all the solutions having a degree of satisfaction strictly above 0. Recall that the degree of satisfaction of a solution is defined as the minimum of the degrees of satisfaction of all the constraints used. 2.5.3. Selection procedure Solving the constraint satisfaction problem derived as discussed in Section 2.5.1 yields a set of solutions, each qualified by a degree of satisfaction. This set might be empty in some exceptional cases. Usually the set contains several solutions. The number of solutions can be very high, however in any case, only one solution must be proposed to the user. Although all solutions in the set are fit to the current case, it is possible to sort them according to contextdependent criteria to propose the solution that best applies. Before applying the context-dependent criteria, the set of solutions 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 solutions are slightly polished to eliminate artificial precision; all values are rounded to one tenth of a degree for temperatures and 1 daPa for the water vapour pressure deficit (VPD) set points. A higher precision would not be justified given the uncertainty in the knowledge used (and especially in the parameters involved in each knowledge base) and the limits of the greenhouse control systems. The selection procedure works as follows. If Botrytis is present, the driest solution is selected, i.e., the one with the minimum daylight ventilation set point and the maximum night-time heating set point. Otherwise, the choice depends on the vigour of the crop. If the vigour is weak, then the coldest solutions are selected, i.e., those with the minimum daylight heating and ventilation set points. If the vigour is strong, then the hottest solutions are selected, i.e., those with the maximum daylight ventilation set point and night-time heating set point. If the vigour is normal, then the solutions with the bigger day–night temperature difference are chosen among those keeping the greenhouse as closed as possible if the wind forecast is high (to limit air exchanges and energy losses). Finally, in all cases except those of Botrytis 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 SERRISTE is to determine set points, a significant part of the knowledge concerns the determination of the average greenhouse climate conditions to be maintained in order to obtain the desired crop behaviour. This is where agronomy and ecophysiology meet. Another part of the knowledge used deals with greenhouse physics and is used to determine the set 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 desired temperature and water vapour pressure deficit to maintain in the greenhouse during each period of the day, of the variables for the associated set points and of the relations between these variables. 3.1.1. Daily average temperature The 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 obtained for several combinations of daytime and night temperatures, provided they resulted in the same daily average. These authors have also shown that the optimal average temperature was a function of the crop dry matter (which increases with the crop age) and of the available photosynthetically active radiation (PAR), which determines the quantity of assimilates available for growth and respiration. However, their analysis was only based on the optimisation of the relative 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 the forecasted available radiation, is bounded by values ensuring that the development of the crop and the apparition of new trusses are maintained at a proper rate (Fig. 4, top left). This empirical relation is defined as a linear fit between the minimum and maximum available radiation (RGn and RGx) and the minimum and maximum acceptable values for the daily temperature average (Tn and Tx). The minimum and maximum available global radiations are computed for the current day, based on a determination of the extraterrestrial irradiation, RGtheo, (Iqbal, 1983). The minimum available radiation, RGn, is defined as θ × 0.132 × RGtheo and the maximum available radiation, RGx, as θ × 0.686 × RGtheo, where θ is the greenhouse transmissivity of the current greenhouse. The values 0.132 and 0.686 are statistical coefficients linking the extraterrestrial irradiation in a given location to the minimum and to the maximum available radiation measured in this location. Minimum (maximum) available radiation is here defined as the average of the values in the first (last) decile. This statistical fit has been repeated over many locations spread all over France and gave fairly comparable results so that the coefficients (0.132 and 0.686) could be defined independently of the location of the greenhouse. Fig. 4 also 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 the average temperature during the crop life. The first crop stage has a relatively high temperature requirement to hasten the apparition of the first trusses and the development of the root system. During crop stages 2 and 3, the fruit load increases 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 the crop and the average temperatures can be increased to sustain fruit growth and development rate; however, it cannot be as high as during crop stage 1. CO2 enrichment increases the photosynthetic activity and assimilates production. Because the growth rate depends on the average temperature of the plant, increasing the average temperature may be used to allow the crop to value the available assimilates. Three levels of CO2 enrichments are defined, none, light (less than 600 ppm during at most half a daylight period) and high. The vigour of the crop can also be modified by a change in the average temperature. Higher temperatures tend to weaken the vigour while lower temperatures tend to strengthen it. As already explained (Section 2.3.4), the vigour has three levels, weak, normal and strong. Finally, the daily average temperature is corrected if the average temperature of the previous day is not satisfying. As said above, the optimal temperature is based on the forecast of the available irradiance; this can lead to apply a solution not fit for the day if the forecast is erroneous. Therefore, the measured irradiance of the previous day is used to determine the correct daily average temperature that should have been applied on the previous day (Toptj−1 ). If the measured average greenhouse temperature during the previous day is higher that Toptj−1 , then the crop has been exposed to a higher temperature sum than necessary and, consequently, its development has been accelerated. A negative correction for the day at hand can therefore be considered. However, this correction is not systematically applied: the diagnostic is noticed to the grower who may accept it or refuse it, depending on his own appraisal of the development and of the vegetative to reproductive balance of the crop.

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3.1.2. Daytime and night temperatures The 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 temperature during daylight time is often cheaper than during night due to the natural heating of the sun and the higher outside temperature. Based on experience and on the results of Seginer et al. (1994b), the maximum difference between daylight and night average temperatures increases with the forecasted irradiance because it allows for higher natural daytime temperature. Obviously, the average day temperature (24 h average), the average daylight and the average night temperatures are linked because the daily average is the weighted average of the night and of the daylight temperatures. 3.1.3. Night-time and pre-dawn temperatures The 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 points Heating 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 use of these models to find the set points resulting in a desired average temperature requires that they are calibrated for the greenhouse at hand and that the evolution of the outside temperature and solar radiation is known in advance. Both these conditions cannot be satisfied, because the calibration requires measurements that are not available in commercial greenhouses and because the weather forecasts that are readily accessible without any cost only include the minimum and maximum temperatures and a declarative description of the sky cloudiness. Hence, practical approaches have been chosen, 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 temperature will be closer to the ventilation set point. Fig. 5 shows how this is formalised: the ratio R defines the position of the daylight average temperature with respect to the two heating and ventilation set points (Fig. 5A). R is determined according to the forecasted global radiation, following an increasing linear function (Fig. 5B). However, the daylight ventilation and heating temperature set points cannot be fully determined with this relation. The difference between heating and ventilation set points is also ruled by physical and biological knowledge. First, in case of Botrytis risk or occurrence, it is paramount to avoid low VPD. Therefore, this difference is small (0.5 ◦ C). Second, when a sudden weather change induces a high potential evapotranspiration after a day of low potential evapotranspiration, the crop suffers from water stress. To avoid such limitations, the difference B is reduced to limit high temperatures and their impact on a water stressed crop (the VPD in the greenhouse is also adapted as detailed later). Finally, to exploit the heat provided by the solar irradiance and thus to reduce the cost of heating, the value of the difference B 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 temperature Td to the daylight ventilation set point, the second linking the average daylight temperature to the daylight heating set point. During night-time, the relation between the average temperature and the heating set point is more straightforward because of the low outside temperature, which induces a rather permanent use of the heating. However, experience has shown that these two values are not equal but differ more or less depending on the glazing material and on the use of a thermal screen. Thus, the night-time heating set point is lower than the average temperature by 0–1.5 ◦ C (0 ◦ C for single layer plastic without thermal screen, 1.5 for a glass glazing with thermal screen). Since no ventilation is done during winter nights the night-time ventilation set point should be assigned a high enough value; it is simply taken equal to the daytime one. 3.1.5. Substrate temperature set point Low substrate temperatures tend to weaken the vigour of the crop and its resistance to the development of Botrytis. On the contrary, higher substrate temperatures tend to strengthen the crop vigour. When the greenhouse has secondary heating pipes installed close or against the rooting medium (a different heating system from the main pipes that are often installed between two crop rows), the water temperature of these pipes is defined. A base temperature of 16 ◦ C is modified according to this knowledge. It is decreased to reduce the vigour from strong to normal. In case of Botrytis risk or occurrence, this temperature is increased to increase the vigour from weak to normal. 3.1.6. Minimum water vapour pressure deficit The minimum VPD is determined to fulfil several goals. First, it must be high enough to prevent Botrytis outbreak or development. In case of declared risks or observed presence of the disease, the minimum VPD is increased to dry out the air. The VPD is also used to sustain crop transpiration when the available radiation is too low to maintain the minimum 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 is used to estimate the transpiration rate due to the forecasted irradiance and minimum VPD. If the result is lower than the threshold transpiration rate, then the minimum VPD set point is increased to reach the threshold. This correction is only realised for the daily VPD set point. 3.1.7. Maximum water vapour pressure deficit Setting 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. High VPD tend to weaken the crop vigour; such values are avoided when the crop has a weak vigour or when the vigour is good but weakening. Along the Mediterranean coast (such as in the areas of Perpignan or Berre, France), contrasting weather can occur from one day to the next, with a humid and overcast day followed by a dry and sunny day. In such situations, the crop endures a sharp increase of the potential evapotranspiration and may show some signs of water stress on the dry day. To avoid such problems, it is possible to maintain the crop in a more humid air by confining the greenhouse 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 temperature set points. In both these knowledge bases, the night-time average temperature must belong to a fuzzy domain depending on the crop variety type and stage. In the mild night case, a thermal balance model of the greenhouse has shown that some values of this domain cannot be attained if the greenhouse remains closed, even without any use of heating. To reach these values (which are considered as agronomically suitable) the use of ventilation during the night-time period is necessary. The night-time heating set point is taken as the minimum value of the fuzzy domain of the night-time average temperature. The ventilation set point must be above this value by at least 2◦ to limit energy losses. In addition, this set point depends on the average night-time temperature and on the socalled natural night-time temperature (the average temperature in the greenhouse when no heating or ventilation is used).

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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 because of the high outside day (and possibly night) temperature. Therefore, this knowledge base cannot define target average temperatures to achieve by the application of the set points. So the knowledge directly defines the values of the set points. They are all defined by unary (single variable) constraints of equality type; the set points do not depend on each other. The second consequence of this approach of the hot day cases is that the solution proposed for hot days is identical day after day. The heating set points are set so as to avoid low greenhouse temperatures that might hinder the growth and development of the crop, although such situations should not occur granted the forecasted outside weather that has triggered the activation of this knowledge base. The ventilation set points are set at values ensuring that ventilation occurs as soon as possible during daylight time to limit the temperature increase in the greenhouse because of the high irradiance.

4. Software implementation The SERRISTE application by itself is built as a shell around the CON’FLEX solver. It has been implemented in C++ and all inputs and outputs take place through files. This architecture has been chosen to separate the SERRISTE application from user interfaces that would use it because SERRISTE has been designed to be compatible with most of the greenhouse climate computers available and its objective is to be integrated in these climate computers, should the companies owning these systems want it. The inputs to SERRISTE are placed in two separate files, one describing the static aspects of the situation (location of the greenhouse; greenhouse cover type, heating system, crop variety) and one describing the dynamically evolving aspects of the crop and of its environment (measured climate in the greenhouse, crop state, weather forecasts). These files must be prepared once a day, at the end of the pre-dawn period, so that SERRISTE, which works off-line once a 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 of report function. User supplied data (especially crop state) correspond to slowly moving variables and may be supplied either in the early morning, just before the run of SERRISTE, or during the previous evening. Using the information in these files, the application follows the procedure described in Section 2.5.1 to select and deparameterize constraints that will form the active constraints set (bottom rectangle, Fig. 2) describing the current situation. These constraints are written in a file and the CON’FLEX program, launched by the SERRISTE application, 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 procedure described in Section 2.5.3 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 SERRISTE application. 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, the only information which requires a human observation, did not change significantly since the last observation, or that this information has been provided offline, sometime before the run of SERRISTE, e.g. during the previous day. The other necessary information is either available from the climate computer (measured climate inside and outside of the greenhouse during the previous day), or from weather forecasts sites on Internet. It is therefore possible to program SERRISTE to run daily with minimum human interaction or even with no human interaction at all. In this automatic mode, the outputs of SERRISTE can also be fed directly to the climate computer, which will then apply the new set points. 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, but also provides facilities to organise the data (separate folders for the static and dynamic data and for the solutions) or to switch between different units when viewing a solution (VPD is commonly expressed as a pressure, hPa, as a mixing ratio, g H2 O 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 view and 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 weather forecasts sites). On the left, two panels group the known greenhouse descriptions (“Serres”, upper part) and the daily data files (“Donn´ees”, lower part). 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 of independent 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, Sainte Livrade sur Lot, south-west; CATE, St Pol de L´eon, Brittany). In each case, two greenhouse compartments were devoted to the experiment, and were planted on the same date with the same variety. The adopted varieties were of the beef type and were also the most commonly cultivated one in each of the areas where the R&D stations were located, and at the time of the experiment. One of the two compartments was run by the local manager, the second by another person strictly applying the set-points proposed by SERRISTE. The local manager was not aware of the set-points applied in the other compartment but could of course observe the crop in it. This setup was intended to achieve the maximum possible independence between the two climate management strategies. Fertigation and pest control were managed according to similar rules, but adapted to the current crop state, thus possibly differing when the climate management implied differences in the crop state. Table 3 summarises the main cropping parameters adopted in the different locations. In each compartment, temperature, VPD, irradiance and set points were recorded by the climate computer. Weekly measurements were performed on the crops, to measure the number (position) of the flowering and harvested 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 used by 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 3 Cropping parameters of the four experimental sites Site

Variety

Planting date

Density (plants/m2 )

Greenhouse type

Substrate

AIREL CTIFL CATE 1 CATE 2

Tellus Twin Daniela Daniela

January 16, 1996 December 20, 1995 January 30, 1996 January 20, 1997

2.37 2.0 2.8 2.25

Glass multispan Glass multispan Multispan Venlo Multispan Venlo

Rockwool Rockwool Rockwool Rockwool

the desired climate (assessment of the internal consistency of the rules and of a good representation of the greenhouse physical processes). The second step is to verify that a crop grown according to SERRISTE develops properly and gives a satisfactory yield. If the set points of SERRISTE do yield the climate also selected by SERRISTE, then this second step can be considered as an agronomic validation of the constraints used to determine the daily solution. In the following, this second step is done by comparing the crop behaviour under SERRISTE management and under a reference management. 5.2.1. Ability of SERRISTE to achieve the desired greenhouse climate Fig. 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 and achieved 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 ventilation set-points. Later in the season, discrepancies appeared as the achieved temperature became higher than the target. For daytime temperatures, differences also existed during winter but did not exceed ±1 ◦ C; a paired t-test performed on these data did not reveal any significant difference. Later in the season, the achieved daytime temperatures tended to be regularly higher than the target, sometimes by up to 2 ◦ C; a paired t-test revealed that the two target and achieved temperatures after April 1st differ significantly and the average difference was 0.8 ◦ C. These results were confirmed by 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 the same experimentation. It can be seen that, until March 14th, the daytime lower bound was not always respected, the achieved VPD being around the threshold. This situation occurred during this first period about half of the time, but in four cases out of five, the achieved VPD did not exceed the minimum threshold by more than 0.7 hPa (the fourth quintile of the difference during this period is 0.677). After this date, the daytime VPD was higher and respects better the 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 achieved temperatures (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). Upper graph: 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 temperature to about 30 ◦ C to avoid leaf burning). 5.2.2. Comparison of the SERRISTE climate against a reference management Fig. 9 shows 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 effectively be controlled. It can be seen that SERRISTE maintained higher differences between day and night, either by allowing higher day temperatures (first part of the season), or by allowing lower night temperatures (end of the graph). Later, when the outside weather was too warm to allow real control of the greenhouse climate, the two temperature management policies achieved about the same daytime and night temperatures (not shown). During the period where the climate can 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 two managements differed significantly on this point. The resulting daily averages were not very different (Fig. 10). The two management protocols did not differ by more than 34 degree day (over more than 250 days), 28 degree day in the case shown in Fig. 9. A closer examination shows that when the crop was young (crop stages 1 and 2), SERRISTE tends 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 night temperature 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 in Fig. 11, SERRISTE adopted a consistently lower VPD (higher relative humidity) than did the reference management, during daytime as well as during night. However, at all three locations where SERRISTE was tested, no difference in Botrytis outbreak was observed under the two managements. In the two southern locations, no Botrytis at all was observed, and in the CATE experiment, some plants were affected and had to be removed from the crop, but in comparable numbers and at about the same period for the two managements. Granted that high VPD in winter is achieved by the combined use of ventilation and heating (dehumidification), the higher VPD level maintained under the reference management implies higher running costs than the SERRISTE management at the same time. Fig. 11 also shows that the two managements follow the same trend over time. 5.2.3. Comparison of crop behaviours under the two climate managements In 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 truss difference after more than 40 weeks of cultivation. However, a paired Student’s t-test performed on the weekly rate of flowering or on the final number of flowered trusses did not reveal any significant difference. The number of trusses at 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 (upper graph) 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 comparable during 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 indicator during the experiments does not reveal any difference between the reference and the SERRISTE management. In the CTIFL experiment (south-east France), under the two managements, the vigour weakens and remains low during the first 5 weeks after plantation, and improves afterwards to remain good or sometimes strong. In the experiments carried out in Brittany (CATE), the vigour and two other indicators related to it (stem diameter below the highest flowering truss and the distance from this truss to the top of the plant) show a different pattern, but are identical under the two managements. In this case, the vigour started out as strong, then decreased after 7 weeks after plantation and was maintained 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 tendency to look more vegetative and they estimated that it probably had a higher leaf area index than did the crop under the reference management (but no measurements were taken). Moreover, during summer, the crops in the SERRISTE compartment suffered less from high temperatures (which were identical under the two managements) and were able to maintain a better fruit quality (bigger fruit size, less microcracks). The local managers attribute this behaviour to a more developed rooting system in the SERRISTE case, which can be the result of the extra efforts SERRISTE takes during phase 1. The total yield achieved with the climate control of SERRISTE was in two cases slightly higher and in one case identical to the reference (Fig. 12, one example). However, under the control of SERRISTE, the date of first harvest was about 1 week later than under the reference management. After 4–5 weeks, the cumulated harvest in each of the two managements became identical and remained so until the end of the cultivation period. The higher harvest achieved in the SERRISTE case was obtained by bigger fruits (390 g/fruit with SERRISTE versus 308 g/fruit with the reference management, on the average) rather than by more fruits (102 fruits/m2 with SERRISTE versus 117 fruits/m2 with the reference management). 6. Discussion The discrepancies between expected and achieved temperatures observed in Fig. 7 can have several explanations. First, SERRISTE uses a very approximate model to describe the physical relation between set points, outside weather and inside greenhouse climate. Not only is this model approximate, it is also not tuned for each greenhouse to which SERRISTE is applied, because this is beyond what can be easily done in a commercial situation. Second, the translation between average temperature and set points also depends on the characteristics of the climate computer regulating the greenhouse 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 the temperature 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 the target value determined by SERRISTE. After correction of the relevant climate computer parameter, this undesirable behaviour ceased. Finally, the relation between average temperature and associated set points depends on the outside weather and is computed, in SERRISTE, using weather forecasts. A more or less inappropriate set-point choice will result 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 average temperatures by its set point choice was demonstrated (Fig. 7). The same conclusion applies to the overall daily average temperature (data not shown). During daytime, the VPD almost always respected the higher bound. Deviations to the lower limit, which are very important to pest control, are limited to acceptable values. Unacceptable VPD values (less than 1 hPa during night-time and less than 2 hPa during day-time) are seldom found. The tighter proposed limits of the VPD at night as compared to the daytime limits made control more difficult. Therefore, although the set points proposed by SERRISTE do not always result in the desired VPD, the deviations, especially during daytime, can be taken as acceptable. From these results, confirmed by those obtained at the other experimental sites, it can be concluded that SERRISTE is able to produce 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 a greenhouse, 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 forecasts and crop state. The daily averages are more variable than in the reference management against which SERRISTE was assessed, although about the same temperature integral is achieved. SERRISTE also uses a wider temperature range and generally achieves higher day/night differences. One of the consequences is that the management of SERRISTE under normal conditions saves energy, especially in winter. However, when the user requests preventive measures against Botrytis development, the management of SERRISTE changes towards higher VPD and becomes more energyexpensive. Energy consumption has been estimated during these experiments using the greenhouse model of Boulard and Baille (1987). Energy consumption measured during the experiment held in CATE in 1996–1997 show that SERRISTE saved 6.2% energy as compared to the reference management. The savings might have been greater if the local manager 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 the basis that this was the behaviour of SERRISTE the year before and that this behaviour did not result in an increase of 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 spent by simultaneous heating and ventilation for dehumidification). On the same site, but for the previous year where the reference 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 savings amount to 2–6%, which is not negligible. The VPD managements of SERRISTE and of the reference showed the same behaviour. However, SERRISTE is less 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 than 1 hPa during night-time) were almost never observed, so that, although less conservative, SERRISTE avoids very risky humidity levels in the greenhouse. This is probably the reason that Botrytis cases had the same null or low frequency under the two managements. The lower VPD maintained by SERRISTE also contributes to the low energetic cost of its climate management. At every experimental site, the general behaviour of the crop grown under the control of SERRISTE was appraised as good. The local managers considered that these crops were as fit as the reference one. They even agreed that the SERRISTE-managed crops better endured hot summer conditions because of their tendency to be more vegetative and therefore to sustain a higher transpiration. It must be stressed that although the crops in the SERRISTE compartments were qualified as more vegetative, they had at least the same production as the reference ones, while there is consensus among advisers that vegetative crops waste dry matter in the leaves and stems at the expenses of the fruits. SERRISTE even managed to produce bigger fruits than did the reference management. Although this is not a problem for beef tomato production because bigger fruits are wanted, this can be an impediment in the case of cherry tomato production as 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 the production from being sold under the targeted commercial label. Forcing SERRISTE to adopt higher temperatures (by indicating that the achieved temperature integral was too low) during 10 days after detection of this behaviour solved the problem. A possible reason which has made this behaviour even clearer is that the manager regularly appraised the vigour of its crop as weak, whereas the local adviser judged it as correct. Under weak vigour indication, SERRISTE adopts lower temperatures. The crop development is slowed down, fruits take longer to mature and thus can receive more assimilates. It must be stressed, however, that during the development of SERRISTE, its targeted production was winter-planted beef tomatoes and not cherry tomatoes. It is therefore rather surprising that this fruit size increase was the 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 reference management. 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 a higher development rate and thus should benefit to the first harvesting date. But, at the same time, SERRISTE maintains higher day-to-night temperature differences and lower night-time temperatures, which may explain the first harvest delay. However, since the harvest delay is a constant result of the application of SERRISTE, it can be compensated for by 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 of SERRISTE to provide climate set points resulting in a proper crop behaviour and suitable production. The knowledge used in SERRISTE can be considered as valid, both in terms of greenhouse physics and agronomy. SERRISTE also allows for some energy savings, the level of which depends on the management to which it is compared. In northern locations where managers tend to spend a lot of energy in humidity control, SERRISTE will most likely achieve higher savings by adopting a less conservative management, except when needed, either because Botrytis is observed or because the manager considers that the current weather is favourable to its development. It must be stressed that SERRISTE was compared to expert growers and did at least as well as they did. It can be concluded that SERRISTE can be of great benefit for low or less skilled growers or for less experienced growers who gain access to expert knowledge and advice through SERRISTE. The main agronomic drawback of the use of SERRISTE is the 1 week delay of the first harvest, which was of importance a few years ago under varying market prices but tends to be of less consequences today where the market prices are more constant throughout the year, at least in France. However, this drawback can be easily avoided by planting 1 week earlier. SERRISTE has been designed to run daily and requires daily updated information to do so. Although not relevant during the experimental phase, some have been critical of the additional daily workload. First, the main advantages of 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 the manager visits the greenhouse in early morning, and he has the feeling that it does not take time to decide upon the set points. Third, SERRISTE moves the manager’s workload from deciding upon the set points to appraising the crop states. This shift can only be beneficial to the manager. Finally, SERRISTE has been designed to produce set points that are compatible with most of the climate computers available today. If the companies making these climate computers wanted, they could easily develop an interface between SERRISTE and their system. The only information that the manager would have to input are the crop vigour, the occurrence or risk of Botrytis and the state of the temperature integral buffer. Such an interface has been developed by one company who has taken a licence to sell SERRISTE. After about 1 year trial of this interface, the conclusion is that the daily use of SERRISTE is perceived, not as a hassle, but as a small time spent for a highly beneficial return. It is, however, regretful that in spite of its good results, SERRISTE is not more widely available and that the development advisers do not see SERRISTE as a valuable tool but as a rival, so to say. The only case where SERRISTE has been adopted by a development adviser prior to its use by the growers is the Geneva case. In this case, SERRISTE has been used by the adviser as a tool to reinforce his advices with scientific knowledge. Basing his advices on SERRISTE was also the base for a more in-depth explanation of the inter-relations between the greenhouse climate and the crop behaviour.

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From the beginning of the project to the first testable prototype, it required 6 years of work, and dedicated financial support covered only 4 years of this period. Two additional years were expended in carrying out the tests in the different French R&D stations. At that time the project ended, at least from the scientific point of view. Although no more financial support was available, the project continued to live, at least to find and convince a greenhouse computer firm 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 advisers and 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: Boulard T., Cros M.-J., Kov´ats 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 involved in the experimental validation of SERRISTE deserve special mention, namely: • • • •

the AIREL in Sainte Livrade sur Lot; the CATE in Saint Pol de L´eon; the CTIFL, station of Balandran; ´ the Ecole de Saint Ilan.

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