Assessment of downy mildew in grapevine using computer vision and fuzzy logic. Development and validation of a new method

OENO One

Giuliana Maddalena¹  Silvia Laura Toffolatti²  Salvador Gutiérrez³  Javier Tardaguila⁴  María Paz Diago⁴  Sara Ceballos⁴  Inés Hernández⁴  Fernando Palacios⁴ 

[1] Department of Agricultural and Environmental Sciences (DiSAA). University of Milan. 20122.Milan, Italy;Department of Agricultural and Environmental Sciences (DiSAA). University of Milan. Milan, Italy;Department of Computer Science and Artificial Intelligence, University of Granada, 18071, Granada, Spain;Televitis Research Group, University of La Rioja, 26006 Logroño, Spain - Institute of Grapevine and Wine Sciences (University of La Rioja, Consejo Superior de Investigaciones Científicas, Gobierno de La Rioja), 26007 Logroño, Spain;

关键词: Non-invasive sensing technologies;    plant disease detection;    precision viticulture;    Plasmopara viticola;   

DOI  :  10.20870/oeno-one.2022.56.3.5359

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown