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@article{178869,
        author = {Nihal C Anil and Ken John James and Royal Mahesh and Ande Yogendra Reddy and Dr Taranath NL},
        title = {AI-driven Crop disease prediction and management},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {6757-6763},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=178869},
        abstract = {Agriculture is extremely important to human civilization, providing food and contributing to the economy. Plants are often susceptible to diseases and insects that have considerable challenges during production. Early detection of harvest diseases is important to minimize damage and reduce costs. While traditional methods do not provide real-time identification, foldable neuronal networks (CNNs) provide a solution by allowing to accurate detection and classification of leaf disease. This study focuses on identifying diseases in plants such as apples, grapes, corn, potatoes and tomatoes. The proposed deep CNN model is compared to a transfer learning approach, such as VGG16. AI-based systems analyze plant images to recognize diseases at the early stages and recommend management strategies, loss of harvests and improved yields. Such systems have applications in agriculture and biological research. Based on this, the paper will introduces possible challenges for practical application in deep learning- based plant diseases and pests detection. Additionally, possible solutions and research ideas concerning the challenges will be proposed, and some suggestions are offered. Finally, this study will give the analysis and prospect of the future trend of plant diseases and pests detection based on deep learning.},
        keywords = {susceptible, detection, pest detection, diseases.},
        month = {May},
        }