Crop growth in greenhouses is basically determined by the climate variables in the environment and by the amounts of water and fertilizers supplied by irrigation.The management of these factors depends on the expertis...Crop growth in greenhouses is basically determined by the climate variables in the environment and by the amounts of water and fertilizers supplied by irrigation.The management of these factors depends on the expertise of agricultural technicians and farmers,usually assisted by control systems installed within the greenhouse.In this context,decision support features enable us to incorporate invaluable human experience so thatwe can take quick and effective decisions to ensure efficient crop growth.This work describes a real-time decision support system for greenhouse tomatoes that supports decisions at three stages–the supervision stage identifies climate sensor faults,the control stage maintains climate variables at setpoints,and the strategic stage identifies diseases affecting the crop and changes climate variables accordingly to minimize damage.The DSS was implemented by integrating a real-time rule-based tool into the control system.Experimental results show that the system increases climate control effectiveness,while providing support in preventing diseaseswhich are difficult to eradicate.The system was tested by simulating the appearance of the disease and observing the real systemresponse.The main contribution has been to demonstrate that production rules,which aremature and well-known in the artificial intelligence domain,can act as a shared technology for the whole system.This means that fault detection,temperature control and disease monitoring features are not dealt with in isolation.展开更多
基金This research was funded by the Spanish Ministry of Science and Innovation as well as by EUERDF funds under Grant DPI2014-55932-C2-1-RThis work has been also developed within the framework of the Project IoF2020-Internet of Food and Farm 2020,funded by the Horizon 2020 Framework Programme of the European Union,Grant Agreement no.731884The authors would also like to thankfully acknowledge the contribution of the Fundacio´n Cajamar Experimental Station.
文摘Crop growth in greenhouses is basically determined by the climate variables in the environment and by the amounts of water and fertilizers supplied by irrigation.The management of these factors depends on the expertise of agricultural technicians and farmers,usually assisted by control systems installed within the greenhouse.In this context,decision support features enable us to incorporate invaluable human experience so thatwe can take quick and effective decisions to ensure efficient crop growth.This work describes a real-time decision support system for greenhouse tomatoes that supports decisions at three stages–the supervision stage identifies climate sensor faults,the control stage maintains climate variables at setpoints,and the strategic stage identifies diseases affecting the crop and changes climate variables accordingly to minimize damage.The DSS was implemented by integrating a real-time rule-based tool into the control system.Experimental results show that the system increases climate control effectiveness,while providing support in preventing diseaseswhich are difficult to eradicate.The system was tested by simulating the appearance of the disease and observing the real systemresponse.The main contribution has been to demonstrate that production rules,which aremature and well-known in the artificial intelligence domain,can act as a shared technology for the whole system.This means that fault detection,temperature control and disease monitoring features are not dealt with in isolation.
