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@article{185139,
        author = {K. Divya Dharshini and Mrs .P. Jasmine Lois Ebenezer},
        title = {PLANT DISEASE DETECTION USING CONVOLUTIONAL NEURAL NETWORKS},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {5},
        pages = {544-546},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=185139},
        abstract = {Agriculture is the foundation of global food security, yet it continues to face one of its greatest challenges: crop and fruit diseases. These diseases not only reduce the quantity of agricultural yield but also impact its quality, leading to significant economic losses and food shortages. Traditionally, disease detection has relied on human experts who manually inspect plants for visible symptoms. While effective in some cases, this process is slow, costly, subjective, and prone to human error, especially when symptoms appear similar across different diseases.
To overcome these challenges, this study introduces a computer-aided system for detecting plant diseases using Machine Learning (ML) and advanced image processing techniques. The proposed framework follows a structured pipeline consisting of image acquisition, preprocessing, feature extraction, classification, and performance evaluation. Several algorithms—Support Vector Machines (SVM), Random Forests, k-Nearest Neighbors (KNN), and Convolutional Neural Networks (CNNs)—were implemented and compared.
The experimental results highlight that CNN-based architectures outperform traditional ML methods, achieving an accuracy of over 95%. Such a system can play a vital role in precision agriculture by enabling early detection of plant diseases, minimizing crop loss, and supporting farmers in improving productivity.},
        keywords = {Plant disease detection, Machine learning, CNN, Precision farming},
        month = {October},
        }