Crop Disease Categorizations Using Optimized Machine Learning

Detecting crop diseases is essential for agriculture to help farmers for better use of resources and enhances crop yields. propose study use a large dataset of commonly grown crops like cassava, maize, and tomato to test the diseases. Various approaches including machine learning(ML) and deep learning(DL) are being used to identify the disease in crops. The presented study uses convolutional neural network (CNN) based models Inception V3 and EfficientNet to identify diseases in crops. The proposed approach achieves a high accuracy of 97%, demonstrates how artificial intelligence (AI) can improve farming practices through precise crop monitoring and disease management. The findings show that deep learning models, especially CNNs, are highly effective for large-scale crop disease detection and can be used in agricultural applications.