Abstract
In the area of greenhouse operation, yield prediction still relies heavily on human expertise. This paper proposes an automatic tomato yield predictor to assist the human operators in anticipating more effectively weekly fluctuations and avoid problems of both overdemand and overproduction if the yield cannot be predicted accurately. The parameters used by the predictor consist of environmental variables inside the greenhouse, namely, temperature, CO2, vapour pressure deficit (VPD), and radiation, as well as past yield. Greenhouse environment data and crop records from a large scale commercial operation, Wight Salads Group (WSG) in the Isle of Wight, United Kingdom, collected during the period 2004 to 2008, were used to model tomato yield using an Intelligent System called “Evolving Fuzzy Neural Network” (EFuNN). Our results show that the EFuNN model predicted weekly fluctuations of the yield with an average accuracy of 90%. The contribution suggests that the multiple EFUNNs can be mapped to respective task-oriented rule-sets giving rise to adaptive knowledge bases that could assist growers in the control of tomato supplies and more generally could inform the decision making concerning overall crop management practices.
Highlights
Greenhouse production systems require implementing computer-based climate control systems, including carbon dioxide (CO2) supplementation
We have described how Evolving Fuzzy Neural Network” (EFuNN) can be applied in the domain of horticulture, especially in the challenging area of deciding support for yield prediction, which leads to the production of well-determined amounts
Some of the advantages of the neural network model implemented in this study include the following: (1) The input parameters of the model are currently recorded by most growers, which makes the model easy to implement; (2) the model can “learn” from datasets with new scenarios; (3) less-experienced growers could use the system because the decision-making process of the most experienced growers is captured by the data used in the trained networks, and production could thereby become more consistent
Summary
Greenhouse production systems require implementing computer-based climate control systems, including carbon dioxide (CO2) supplementation. Two of the dynamic growth models are TOMGRO [1, 2] and TOMSIM [3, 4] Both models depend on physiological processes, and they model biomass dividing, crop growth, and yield as a function of several climate and physiological parameters. Their use is limited, especially for practical application by growers, by their complexity, and by the difficulty in obtaining the initial condition parameters required for implementation [3]. The pedagogical approaches treat the neural network as a black box [20] and use the neural network only to generate test data for the rule generation algorithm
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