Assessment of downy mildew in grapevine using computer vision and fuzzy logic. Development and validation of a new method
Metadatos
Mostrar el registro completo del ítemEditorial
International Viticulture & Enology Society
Materia
Non-invasive sensing technologies Plant disease detection Plasmopara viticola Precision viticulture
Fecha
2022-07-01Referencia bibliográfica
Hernández, I... [et al.] (2022). Assessment of downy mildew in grapevine using computer vision and fuzzy logic. Development and validation of a new method. OENO One, 56(3), 41–53. [https://doi.org/10.20870/oeno-one.2022.56.3.5359]
Patrocinador
European Commission 828940; Spanish Government PID2020-119478GB-I00; Universidad de La Rioja 1150/2020 Gobierno de La RiojaResumen
Downy mildew is a major disease of grapevine. Conventional methods for assessing crop
diseases are time-consuming and require trained personnel. This work aimed to develop and
validate a new method to automatically estimate the severity of downy mildew in grapevine
leaves using fuzzy logic and computer vision techniques. Leaf discs of two grapevine varieties
were inoculated with Plasmopara viticola and subsequently, RGB images were acquired under
indoor conditions. Computer vision techniques were applied for leaf disc location in Petri
dishes, image pre-processing and segmentation of pre-processed disc images to separate the
pixels representing downy mildew sporulation from the rest of the leaf. Fuzzy logic was applied
to improve the segmentation of disc images, rating pixels with a degree of infection according
to the intensity of sporulation. To validate the new method, the downy mildew severity was
visually evaluated by eleven experts and averaged score was used as the reference value. A
coefficient of determination (R2) of 0.87 and a root mean squared error (RMSE) of 7.61 %
was observed between the downy mildew severity obtained by the new method and the visual
assessment values. Classification of the severity of the infection into three levels was also
attempted, achieving an accuracy of 86 % and an F1 score of 0.78. These results indicate that
computer vision and fuzzy logic can be used to automatically estimate the severity of downy
mildew in grapevine leaves. A new method has been developed and validated to assess the
severity of downy mildew in grapevine. The new method can be adapted to assess the severity
of other diseases and crops in agriculture.