Smart Agriculture: A CNN-Based Approach for Crop Disease Classification and Detection

Agriculture is the backbone of every nation's economy. With the global population steadily increasing, meeting the rising demand for food has become a major challenge. In India, crop production is heavily impacted by plant diseases. Given the variety of diseases and environmental factors that affect the quality and quantity of crop yields, early diagnosis of these diseases is crucial. This study introduces a new method for identifying multiple crop diseases through a robust Convolutional Neural Network (CNN) architecture. The model is tailored to analyze complex visual patterns in crop leaves, allowing for precise identification of different diseases. It strikes a balance between computational efficiency and high accuracy. Experiments were done using publicly available datasets, such as Plantvillage. The results, assessed on a comprehensive dataset of crop leaf images, highlight the strength of the proposed model. It has outperformed traditional methods, achieving an average accuracy of 99.17 %. This research emphasizes the promise of deep learning in agricultural applications by providing a dependable tool for the early detection of crop diseases. Such innovations enhance crop management practices and improve productivity and sustainability.