AI-Powered Image Processing Techniques for Grapevine Disease Detection in Agriculture

This study investigates the application of artificial intelligence, specifically deep learning-based image processing techniques, for the detection of grapevine diseases in agricultural settings. Leveraging a publicly available dataset from Kaggle, the project focuses on classifying grape leaves as either healthy or affected by one of three common diseases: Black Rot, Esca (Black Measles), and Leaf Blight. Three machine learning models were developed and evaluated: Convolutional Neural Networks (CNN), Deep Neural Networks (DNN), and Transfer Learning. Each model was trained and tested using the same dataset to ensure a fair comparison. Among the models, the CNN achieved an accuracy of 97.40%, while the DNN model showed significantly lower performance at 31.41%. Transfer Learning outperformed the others, reaching a peak accuracy of 98.84%. The results underscore the potential of deep learning, particularly transfer learning, in automating disease identification processes in viticulture

Such AI-driven systems can enhance precision agriculture by enabling early detection and prompt intervention, ultimately contributing to improved crop yield and quality. [Publisher's Text]