A web-based system for fungus disease risk assessment in greenhouses: System development

Abstract

Nowadays, there is a great need and public concern for reduced use of hazardous pesticides in horticulture and implementation of alternative approaches for rational pests and diseases suppression. This goal is approached by integrating crop protection measures and non-chemical measures or applying crop protection products only when needed, thus eliminating unnecessary applications. In addition, preventive farm management methods such as optimal management of the aerial environment in greenhouses with sufficient installed technology may provide adequate disease suppression, as the grower can avoid conditions that favour pests and fungus development. Aim of this study is the development of a web-based decision support system (DSS) that estimates the risk for a fungi disease outbreak in a greenhouse. The disease development risk assessment is based on disease models already available in the literature that correlate the rate of disease development to crop microclimate conditions and cultivation practices. Up to now such models were mainly used for the disease development risk assessment based on past or historical data. A strong point of the work is related to the fact that the DSS makes use of the outside climate forecast to predict the greenhouse microclimate conditions during a set of days that will follow. Then, based on the predicted microclimate inside the greenhouse the Botrytis disease development models are used to assess the potential risk for disease development in a greenhouse tomato crop during the following days. The greenhouse microclimate is estimated based on the outside weather forecast for the region of the greenhouse, the greenhouse energy and vapour balance, the greenhouse control concept and methodology, the climate control equipment and the greenhouse climate set points. The last part of the DSS, contains a tactical and strategical tool that, according to the risk assessment, suggests climate control actions to prevent fungi development for preventive and optimal disease control. Such a DSS can then be included in disease management systems to assist growers to decide when to proceed to a certain action in order to modify the conditions that favour the development of a disease, or if necessary, when to apply the proper permitted plant protection products. In this work, the development of the DSS and the results of the validation process of the microclimate predicted values are presented.