Plant diseases and pests cause a large loss of world agricultural production. Downy mildew is a major disease in grapevine. Conventional techniques for plant diseases evaluations are time-consuming and require expert personnel. This work investigates novel sensing technologies and artificial intelligence applications for assessing downy mildew in grapevine under laboratory conditions. In our methodology, machine vision is applied to assess downy mildew sporulation, while hyperspectral imaging is used to explore its potential capability towards early detection of this disease. Image analysis applied to RGB leaf disc images is used to estimate downy mildew (Plamopara viticola) severity in grapevine (Vitis vinifera L. cv Tempranillo). A determination coefficient (R²) of 0.76 ** and a root mean square error (RMSE) of 20.53% are observed in the correlation between downy mildew severity by computer vision and expert’s visual assessment. Furthermore, an accuracy of 81% is achieved to detect downy mildew early using hyperspectral images. These results indicate that non-invasive sensing technologies and computer vision can be applied for assessing and quantify sporulation of downy mildew in grapevine leaves. The severity of this key disease is evaluated in grapevine under laboratory conditions. In conclusion, computer vision, hyperspectral imaging and machine learning could be applied for important disease detection in grapevine.

Yield assessment has been identified as critical topic for grape and wine industry. Computer vision has been applied for assessing yield, but the accuracy was greatly affected by fruit occlusion affected by leaves and other plant organs. The objective of this work was the consistent, continuous evaluation of the impact of leaf occlusions in different commercial vineyard plots at different defoliation stages. RGB (red, green and blue) images from five Tempranillo (Vitis vinifera L.) vineyards were manually acquired using a digital camera under field conditions at three different levels of defoliation: no defoliation, partial defoliation and full defoliation. Computer vision was used for the automatic detection of different canopy features, and for the calibration of regression equations for the prediction of yield computed per vine segment. Leaf occlusion rate (berry occlusion affected by leaves) was computed by machine vision in no defoliated vineyards. As occlusion rate increased, R² between bunch pixels and yield was gradually reduced, ranging from 0.77 in low occlusion, to 0.63.

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.