Disease Detection from Grape Plant Leaves Using Transfer Learning Methods

Abstract

Today, the agricultural sector faces significant challenges due to population growth and limited resources. Enhancing productivity and minimizing losses is of great importance for the sustainability of agriculture. Therefore, leveraging technological advancements plays a critical role, particularly in the development of sustainable farming practices. Among these advancements, artificial intelligence (AI) stands out with its potential to contribute significantly to agricultural production. The primary objective of this study is to provide farmers with fast and accurate information regarding plant health, thereby preventing the spread of diseases and optimizing agricultural output. In line with this goal, AI-based image processing techniques were employed. Specifically, this study focuses on detecting grapevine leaf diseases namely powdery mildew ($Erysiphe$ $necator$), downy mildew ($Plasmopara$ $viticola$), and grapevine rust mite ($Eriophyes$ $vitis$) using AI. Disease detection was carried out using leaf images, which were then used for classification. A hybrid dataset was constructed using a combination of publicly available images and manually collected samples captured via smartphone cameras in vineyards, fields, and gardens. This diverse and balanced dataset was used to train several CNN-based transfer learning models, including AlexNet, DarkNet53, Inception-ResNet-V2, Inception-V3, MobileNet-V3, ResNet50, ResNet101, VGG16, and VGG19 architectures. Among these, Inception-ResNet-V2 achieved the best performance with an accuracy of 97.45%, a training loss of 8.19%, a test accuracy of 93.00%, and a test loss of 20.60%. These results demonstrate that the model performs well in detecting diseases from grapevine leaves during both training and testing phases.

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