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Forecasting pest risk level in roses greenhouse:Adaptive neuro-fuzzy inference system vs artificial neural networks

Information Processing in Agriculture 2021年第3期8卷 386-397页

作者： Ahmad Tay frederic lafont Jean-Francois Balmat University of Toulon LISUMR CNRS 7020Bt XCS 6058483041 Toulon cedex 9France

The purpose of this study is to establish a system for the prediction of the pests’risk level in a roses greenhouse by applying Artificial Neural Networks(ANNs)and an Adaptive Neuro-Fuzzy Inference System(ANFIS).Pests in roses greenhouses are known to be fatal to plants if not detected at a premature *** detection could avoid huge agronomic and eco-nomic ***,it could be a difficult task to *** complexities arising from the interactions between variables influencing the development could be a barrier to fulfill the previously mentioned *** output of the developed system represents the next day?s risk level of Western flower Thrips(WFT)(Frankliniella occidentalis)in a roses *** explanatory variables,such as internal temperature,internal humidity,today’s pest risk level and human intervention have been considered for this *** main contributions of this study are three fold;providing a daily estimate WFT risk level,reduc-ing the use of pesticides and finally mitigating yield *** obtained results were com-pared to each other and to real *** performance of the models has been evaluated by 3 statistical *** results showed conspicuous performance of both models,indicating their efficiency for pest *** novelty associated with the sys-tem is the creation of decision support tool for daily risk assessment of *** on a small number of variables,this system is a monitoring tool which contributes to help farm-ers early reveal warning *** addition,this is a first attempt to employ ANNs and ANFIS for the prediction of WFT。

关键词： Decision making Artificial neural networks ANFIS Risk assessment Integrated pest management

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