RT Journal Article
T1 Automatic plant disease diagnosis using mobile capture devices, applied on a wheat use case
A1 Johannes, Alexander
A1 Picon, Artzai
A1 Alvarez-Gila, Aitor
A1 Echazarra, Jone
A1 Rodriguez-Vaamonde, Sergio
A1 Navajas, Ana Díez
A1 Ortiz-Barredo, Amaia
AB 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 mobile devices and more than 3500 images captured in two pilot sites in Spain and Germany during 2014, 2015 and 2016. Obtained results reveal AuC (Area under the Receiver Operating Characteristic –ROC– Curve) metrics higher than 0.80 for all the analyzed diseases on the pilot tests under real conditions.
SN 1872-7107
YR 2017
FD 2017-06-01
LA eng
NO Johannes , A , Picon , A , Alvarez-Gila , A , Echazarra , J , Rodriguez-Vaamonde , S , Navajas , A D & Ortiz-Barredo , A 2017 , ' Automatic plant disease diagnosis using mobile capture devices, applied on a wheat use case ' , Computers and Electronics in Agriculture , vol. 138 , pp. 200-209 . https://doi.org/10.1016/j.compag.2017.04.013
NO Publisher Copyright: © 2017 Elsevier B.V.
DS TECNALIA Publications
RD 3 jul 2024