The impact of changing light levels and fruit load on the pattern of tomato yields

Adams, Sally; Valdeés, V.M.; Cave, Carol R. J.; Fenlon, J. S.

doi:10.1080/14620316.2001.11511379

Cited by 28 publications

(9 citation statements)

References 9 publications

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“…Flushing makes it difficult to estimate labour requirements for harvest and to provide a steady supply of fresh produce to the markets. Greenhouse tomatoes also exhibit flushes in yield and it has been suggested that such flushes are associated with hastening tomato maturation (Adams et al 2001;Adams 2002). …”

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Neural network modelling to predict weekly yields of sweet peppers in a commercial greenhouse

Lin¹,

Hill²

2008

Can. J. Plant Sci.

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Lin, W. C. and Hill, B. D. 2008. Neural network modelling to predict weekly yields of sweet peppers in a commercial greenhouse. Can. J. Plant Sci. 88: 531Á536. The production of greenhouse-grown sweet pepper (Capsicum annuum L.) is irregular with a peak-and-valley pattern of weekly yields. We monitored the yields and environment in a commercial greenhouse in British Columbia over six (2000Á2005) growing seasons. Light was defined as cumulative light over the current week, with L_1, L_2, L_3, L_4, L_5 and L_6 representing light over previous weeks. Temperature (AvgT) was defined as the current weekly average of 24-h air temperatures, with T_1, T_2 and T_3 representing temperatures over previous weeks. Inputs were also created for the current weekly yield (Y) and previous weekly yields (Y_1, Y_2, Y_3 and Y_4). Neural network (NN) modelling with up to 21 inputs was used to predict yields 1 wk (Y'1) and 2 wk (Y'2) in advance of the actual fruit harvest. Data for five different years were combined for model training with the year to be predicted held separate as a validation set. The best models used 13 inputs to predict Y'1 with an average R 2 of 0.66 over the 6 yr. Y_4, Y-Y_1, Y_1, L_1, Y, Y_3, Y-Y_3 and wk (of the year) were important inputs. The environmental inputs were of lesser importance, which suggests that the cyclic nature of pepper yields is inherent in the pepper biology. Predicting Y'2 was more difficult with an average R 2 of 0.59 over the 6 yr. NN have good potential for predicting pepper yields.Key words: Capsicum annuum L., flushing, fruit, greenhouse production, neural networks Lin, W. C. et Hill, B. D. 2008. Pre´vision du rendement du poivron dans une serre commerciale par mode´lisation sur re´seau neuronal. Can. J. Plant Sci. 88: 531Á536. La culture du poivron (Capsicum annuum L.) en serre donne des re´sultats irre´guliers avec un rendement hebdomadaire en dents de scie. Les auteurs ont e´tudie´le rendement et l'environnement de cette culture dans une serre commerciale de la Colombie-Britannique durant six saisons ve´ge´tatives (de 2000 a`2005). Pour la lumie`re, les auteurs ont utilise´la luminosite´cumulative de la semaine courante, L_1, L_2, L_3, L_4, L_5 et L_6 correspondant a`cette valeur pour les semaines ante´rieures. La tempe´rature (AvgT) e´tait e´gale a`la moyenne de la tempe´rature ambiante pendant 24 h pour la semaine en cours, T_1, T_2 et T_3 correspondant a`cette valeur pour les semaines ante´rieures. Les auteurs ont aussi cre´e´des intrants pour le rendement de la semaine courante (Y) et de la semaine ante´rieure (Y_1, Y_2, Y_3 et Y_4). La mode´lisation sur re´seau neuronal avec un maximum de 21 parame`tres permet de pre´voir le rendement des poivrons une semaine (Y'1) et deux semaines (Y'2) avant la re´colte proprement dite. Les auteurs ont combine´les donne´es de cinq anne´es pour former le mode`le, l'anne´e pour lesquelles les pre´visions devaient eˆtre calcule´es servant a`la validation. Les meilleurs re´sultats sont obtenus avec 13 parame`tres et le mode`le a pre´vu Y'1 avec ...

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Neural network modelling to predict weekly yields of sweet peppers in a commercial greenhouse

Lin¹,

Hill²

2008

Can. J. Plant Sci.

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“…From the point of view of vegetable cultivation, it would be advisable to create mobile shading systems to regulate the PAR on crops according to their needs. Mobile shading, when applied only during sunny periods, has been proven to be less harmful than fixed shading to tomato production [72].…”

Section: Introductionmentioning

confidence: 99%

A Photovoltaic Greenhouse with Passive Variation in Shading by Fixed Horizontal PV Panels

Moretti

Marucci

2019

Energies

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The traditional shading systems that greenhouses use cause some of the solar radiation that is reflected or absorbed to be lost and, therefore, not used by the plants under cultivation. An interesting solution to these problems is to position photovoltaic (PV) panels on the roofs of greenhouses. All of the photovoltaic greenhouses that have been realized in Mediterranean areas are characterized by a fixed position of the PV panels and excessive shading, especially in autumn and winter. The purpose of this study is to describe a prototype of a photovoltaic greenhouse with both fixed and horizontal PV panels that exploit the natural variation in the elevation angle of the sun’s rays during the year to allow for “passive” variation in shading. The considerable variation in the elevation angle of the sun’s rays (from 24.4° to 71.1°) results in a high variation in shading (from 39.4% to 72.6%), with the highest values in the summer months and the lowest values in the winter months. This trend is favorable for meeting the photosynthetically active radiation (PAR) needs of greenhouse plants. If the plants under cultivation require more solar energy, it is necessary to increase the distance between the panels. We implement a specific mathematical relationship to define the precise distance to be assigned to the photovoltaic panels on the roof pitch.

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“…Attempts to remedy this "flushing" have not been completely successful. Fruit pruning did not stabilize the fruit production patterns of tomatoes and peppers (Adams et al 2001;Heuvelink et al 2004). Adams et al (2001) suggested that flushing was a consequence of hastening fruit maturation in tomatoes.…”

Section: Mots Clés: Serriculture Fruit Coloration Numérisation Rémentioning

confidence: 99%

“…Fruit pruning did not stabilize the fruit production patterns of tomatoes and peppers (Adams et al 2001;Heuvelink et al 2004). Adams et al (2001) suggested that flushing was a consequence of hastening fruit maturation in tomatoes. Heuvelink and Korner (2001) reduced flushing in sweet peppers by growing seedless fruits parthenocarpically.…”

Section: Mots Clés: Serriculture Fruit Coloration Numérisation Rémentioning

confidence: 99%

Neural network modelling of fruit colour and crop variables to predict harvest dates of greenhouse-grown sweet peppers

Lin¹,

Hill²

2007

Can. J. Plant Sci.

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Lin, W. C. and Hill, B. D. 2007. Neural network modelling of fruit colour and crop variables to predict harvest dates of greenhouse-grown sweet peppers. Can. J. Plant Sci. 87: 137-143. Sweet peppers (Capsicum annuum L.) grown in the greenhouse have irregular yields. Modelling colouration of individual fruit could help growers predict the number of fully coloured peppers that will be ready to harvest within a routine harvest period. We monitored the red, green and blue colour intensities of developing pepper fruit via digital image processing. These colour measurements together with crop phenology and environmental variables were used as inputs into neural network (NN) models to predict days-to-harvest (D-to-H) for individual fruit. When 18 inputs were evaluated, a typical "best" NN model needed only five of the inputs to predict D-to-H (range 0 to 28 d) for red peppers with a R 2 of 0.79, a root mean square error (RMSE) of 3.4 d, and an average absolute error (AAE) of 2.5 d. D-to-H were more difficult to predict for yellow peppers, with the "best" model using eight inputs to achieve a R 2 of 0.69, a RMSE of 4.4 d, and an AAE of 3.4 d. Light and temperature made little contribution to predictions of D-to-H. NN models with only three inputs (Julian day, nodal position of the target fruit and ratio of red:green intensities) could still make useful predictions of harvest maturity. For both red and yellow peppers, the R 2 values of NN models were higher than the corresponding R 2 a (R 2 adjusted) values derived from multiple linear regression models. It is concluded that NN have potential to assist greenhouse operators to predict D-to-H of sweet peppers. Modéliser la coloration des fruits individuels aiderait les producteurs à prévoir le nombre de fruits entièrement colorés qu'ils pourront cueillir au cours d'une période donnée. Les auteurs ont établi l'intensité des couleurs rouge, verte et bleue des fruits en développement par numérisation des images. La quantification des couleurs, les stades phénologiques de la culture et des variables environnementales ont servi à créer un modèle de type « réseau neuronal » (RN) permettant de prévoir le nombre de jours avant la récolte (JAR) de chaque fruit. Sur 18 paramètres évalués, le meilleur modèle RN n'en a besoin que de cinq pour prévoir le JAR (fourchette de 0 à 28 jours) des poivrons rouges, avec une valeur R 2 de 0,79, une erreur-type de 3,4 jours et une erreur absolue moyenne de 2,5 jours. Le JAR est plus difficile à établir pour les poivrons jaunes, le « meilleur » modèle recourant à huit paramètres pour donner une R 2 de 0,69, une erreur-type de 4,4 jours et une erreur absolue moyenne de 3,4 jours. La luminosité et la température concourent peu à la prévision du JAR. Les modèles RN n'intégrant que trois paramètres (jour julien, emplacement du fruit par rapport au noeud et rapport entre l'intensité du rouge et du vert) pourraient tout de même donner une prévision utile de la maturité à la cueillette. Pour les poivrons rouges et jaunes, les modèles RN donnent une val...

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The impact of changing light levels and fruit load on the pattern of tomato yields

Cited by 28 publications

References 9 publications

Neural network modelling to predict weekly yields of sweet peppers in a commercial greenhouse

Neural network modelling to predict weekly yields of sweet peppers in a commercial greenhouse

A Photovoltaic Greenhouse with Passive Variation in Shading by Fixed Horizontal PV Panels

Neural network modelling of fruit colour and crop variables to predict harvest dates of greenhouse-grown sweet peppers

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