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@article{192403,
        author = {R.A. Gore and A.B. Khilari and T.D. Jadhav and A.V. Dange and N.A. Pondhe},
        title = {Real Time Plant Disease Detection Using Deep Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {5647-5653},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=192403},
        abstract = {Plant diseases contribute greatly to the decreased agricultural productivity and financial loss particularly in the developing countries where early diagnosis is often lacking. Traditional methods of detecting diseases depend on the manual inspection of the experts and are laborious, subjective, and im- possible with large-scale farming. To overcome these limitations, this paper introduces a real-time plant disease detection system, which is based on deep learning.
The proposed system relies on a dataset captured with the help of a camera in a real field environment and is directed at three crops: rice, wheat, and maize. To extract the discriminative information of the plant leaves images automatically, as well as classify the different disease types and normal samples, a Convolutional Neural Network (CNN) is applied. The model can withstand real-world agricultural conditions since the dataset has the variability in the terms of lighting, background, or disease severity.
Based on experimental results, the proposed CNN-based approach has a total classification error of approximately 92 percent. The system enables timely intervention, reduces the use of manual monitoring, and allows the detection of diseases at an early stage. This paper is the foundation of real-time implementation in precision farming applications and shows how intelligent and sustainable agriculture is a prospect of deep learning-based solutions.},
        keywords = {Plant disease detection, deep learning, convolutional neural networks, image processing, real- time agriculture, crop health monitoring},
        month = {April},
        }