Automatic plant disease diagnosis using mobile capture devices, applied on a wheat use case
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Author/sJohannes, Alexander; Picon, Artzai; Alvarez-Gila, Aitor; Echazarra, Jone; Rodriguez-Vaamonde, Sergio; [et al.]
Mobile capture devices
Disease diagnosis based on the detection of early symptoms is a usual threshold taken into account for integrated pest management strategies. Early phytosanitary treatment minimizes yield losses and increases the efficacy and efficiency of the treatments. However, the appearance of new diseases associated to new resistant crop variants complicates their early identification delaying the application of the appropriate corrective actions. The use of image based automated identification systems can leverage early detection of diseases among farmers and technicians but they perform poorly under real field conditions using mobile devices. A novel image processing algorithm based on candidate hot-spot detection in combination with statistical inference methods is proposed to tackle disease identification in wild conditions. This work analyses the performance of early identification of three European endemic wheat diseases – septoria, rust and tan spot. The analysis was done using 7 ...