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

Abstract

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.

Influence of Nitrogen on Grapevine Susceptibility to Downy Mildew

“…This type of analysis is time consuming, since it must be performed at the microscope after detaching the sporangia from the leaves. Image analysis, on the contrary, is quite fast and simple because it is based on a picture of the sporulating leaf or leaf disc that is processed with a common image analysis software, it can be automated, and it provides more precise and reliable data than the traditional visual assessment methods [ 31 ].…”

Pathogen Adaptation to American (Rpv3-1) and Eurasian (Rpv29) Grapevine Loci Conferring Resistance to Downy Mildew

“…Infection frequency (IF), a measure of disease incidence, was calculated as the proportion of sporulating sites over the total inoculated droplets [ 27 ]. Disease severity was estimated by image analysis of the leaf area covered by sporulation and expressed as a percentage of the sporulating area (PSA) [ 41 ]. The reduction in PSA on B and MG compared to PN (RP, expressed as a percentage) was calculated at 13 dpi as where x indicates either B or MG. RP values were used to identify strains that are able to overcome B and MG resistance (adapted strains) following this scheme: the non-adapted strains (i.e., those showing no or limited growth on resistant cultivars) should possess the highest RP values, while the adapted strains should possess the least RP values.…”

Using artificial intelligence (AI) for grapevine disease detection based on images

“…Finally, the estimation of the downy mildew severity, obtained from the evaluation of the sporulation located in each image, was compared against scores obtained by a visual assessment performed by experts. A detailed description of the protocol and model is presented in Hernández et al [6].…”