Abstract: This research paper presents a deep learning-based approach for the automated detection and classification of plant diseases through leaf image analysis. Early and accurate identification of crop diseases is crucial for sustainable agriculture and global food security. Our system leverages Convolutional Neural Networks (CNNs) to analyse images of plant leaves and identify diseases with high accuracy. The proposed model was trained on a comprehensive dataset comprising 38,000 images spanning 14 crop species and 26 diseases. Experimental results demonstrate that our CNN based system achieves an average classification accuracy of 96.7%, outperforming traditional image processing techniques and conventional machine learning approaches. The system can identify diseases at early stages, enabling timely intervention that reduces crop losses and minimizes pesticide usage. Furthermore, we have developed a mobile application interface that allows farmers to utilize this technology directly in the field, bridging the gap between advanced AI technologies and practical agricultural applications. The Convolutional Neural Network (CNN) resulted in a improved accuracy of recognition compared to the SVM approach.
Keyword: Machine Learning, Image processing, Decision Tree, Random Forest, Crop disease detection, Extreme Learning Machine, K-means Clustering
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DOI:
10.17148/IJARCCE.2025.145104
