Multiclass classification of grape disease analysis

Author: Satheesh Kumar B, Abirami V, Sujith Kumar B

Published On: 2024-12-13

Diseases that endanger productivity and quality are serious obstacles to grape growing. This work uses deep learning to analyze grape diseases through a multiclass classification approach. The suggested model efficiently classifies grape leaf images into several disease categories, including healthy leaves, by utilizing convolutional neural networks (CNNs), which may help with prompt intervention. To improve feature extraction, the dataset is preprocessed. To increase accuracy and decrease training time, sophisticated techniques like transfer learning are used. According to experimental data, the model is resilient and achieves excellent classification accuracy in a variety of environmental situations. The suggested system gives researchers and grape growers a dependable tool for effectively managing and monitoring vineyard health. The goal of future research is to connect this technology to the Internet of Things for real-time uses.

Key Wordsyh: Grape disease, Multiclass classification, Deep learning, CNN, Transfer learning