MobileNetV3-Large-Based Grapevine Disease Detection for Precision Agriculture

Abstract

The need to enhance crop yield, mitigate losses and ensure sustainable viticulture requires that grapevine diseases be identified accurately and at an early stage. In this paper, an effective deep learning based grapevine leaf classification framework with a fine-tuned MobileNetV3Large model is proposed. The data set used for the experiment was an image data set of 500 grapevine leaves, which included 5 different categories of Buzgulu, Ala Idris, Nazli, Dimnit, and Ak. Numerous preprocessing techniques have been used on the dataset, such as resizing of the image, image normalization, and data augmentation to improve the robustness and generalization of the model. The method of transfer learning was applied by taking MobileNetV3Large pre-trained on the ImageNet dataset and then training the layers that perform multi-class classification. Finally, the model was tested based on the following performance measures; accuracy, precision, recall, F1 score, and confusion matrix. According to the results obtained from the experiment, the precision rate was 96%, and the recall rates were 100%. The convergence and prevention of overfitting by early stopping is effectively indicated by training and validation performance curves. The results demonstrate the appropriateness of deep learning lightweight structures to real-world farming tasks and verify the possibility of the offered model as a dependable instrument to automatize the classification of grapevine diseases and aid precision farming and sustainable vineyard management.