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@article{196836,
        author = {M. Menaka and N. Arun Kumar and V. Mathavan},
        title = {AI-Driven Plant Disease Diagnosis for Modern Agriculture},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {5345-5349},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=196836},
        abstract = {Crops are critical to an economy's strength and, as a result, diseases can severely affect agricultural output and profitability. Detecting diseases in crops as soon and as accurately as possible is required for effective long-term agriculture. Most conventional techniques for finding crop diseases rely on human inspection, which can be slow and imprecise, and include several different methods of detection. This paper introduces an artificial intelligence-based approach to detecting crop disease through image-editing and machine learning processes— specifically, deep-learning algorithms. The system recognizes and identifies the disease type from leaf image data taken with a camera or mobile device. A CNN is trained on images of healthy and diseased crops, enabling it to classify leaf images by the type of crop disease present in them with very high accuracy automatically. Additionally, the system creates a profile of the pathogen involved to assist farmers with recommendations for treatment or prevention of diseased crops; this assists farmers in reducing their utilization of pesticides and/or crop-damage risk. Our results highlight significant improvements in both crop productivity and the promotion of sustainable agricultural practices using AI as a means for achieving smart agriculture.},
        keywords = {CNN, Deep Learning, Image Processing, Crop Disease Detection, Artificial Intelligence, and Smart Agriculture.},
        month = {April},
        }