Abstract
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.
Highlights
Plant diseases, pests and weeds cause large losses of food production in agriculture.Traditionally, plant diseases are identified by visual observations by the growers in the field of biological techniques in the laboratory [1]
Computer vision was applied to assess downy mildew severity, while hyperspectral imaging was employed for early detection
Regarding early detection of downy mildew using hyperspectral imaging, classification accuracy between control and downy mildew inoculated discs after several days post inoculation (DPI) using different machine learning models is showed in Figure 5 and
Summary
Pests and weeds cause large losses of food production in agriculture. Plant diseases are identified by visual observations by the growers in the field of biological techniques in the laboratory [1]. Machine learning and artificial intelligence technologies can be applied to identify, classify and quantify crop diseases in data-driven agriculture [5,6,7,8]. The evaluation of this disease has been based mostly on visual assessment of leaves in the vineyards or histological analyses at the laboratory [9]. ComThis work examines non-invasive imaging technologies artificial intelligence puter vision was applied to evaluate downy mildew severity,and while hyperspectral imagapplications for assessing downy mildew in grapevine under laboratory conditions. Puter vision was applied to evaluate downy mildew severity, while hyperspectral imaging was used to detect this disease early in grapevine
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