Yield Prediction for Tomato Greenhouse Using EFuNN

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. 1. Introduction Greenhouse production systems require implementing computer-based climate control systems, including carbon dioxide ( ) supplementation. The sort of systems we are concerned with here are normally in use all year-round so as to maximize product and thus are typically applied in scenarios where the greenhouse crops have a long growing cycle. The technological advances and the sophistication of greenhouse crop production control systems do not mean that greenhouse operation does not rely on human expertise to decide on the optimum values for yield weekly amount. Practiced greenhouse tomato growers and researchers evaluate plant responses and growth mode by observations of the plant morphology. Tomato growers use this information in decision making depending on climate conditions and crop management practices to shift the plant growth toward a “balanced” growth mode, or to be able to accurately predict regular crops amounts each year. One of the dynamic and complex systems is tomato crop growth, and few models have studied it previously. 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