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@article{177667,
        author = {Arnav Saxena and Aditya Chachan and Akash Raj and Dr. Narendra Kumar Sura},
        title = {Crop Health Disease Detection},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {1408-1412},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=177667},
        abstract = {Plant health and disease detection is a critical aspect of modern agriculture, directly influencing crop yield, quality, and sustainability. The accurate and timely identification of plant diseases is essential to mitigate losses and ensure food security. Traditional methods of disease detection, which often rely on manual inspection by experts, are time consuming, subjective, and limited by human error. This work explores the application of advanced technologies, including computer vision, machine learning, and sensor-based systems, for automated plant disease detection. By leveraging high-resolution imagery and real-time data from IoT-enabled sensors, it becomes possible to identify diseases at an early stage with high accuracy. Deep learning models, such as convolutional neural networks (CNNs), play a pivotal role in analyzing complex patterns and symptoms like leaf discoloration, spots, or wilting. Additionally, spectral imaging and chemical analysis enhance the diagnostic process by detecting changes invisible to the naked eye.},
        keywords = {Plant Disease Detection, Agricultural Technology, Image Classification, Machine Learning Applications.},
        month = {May},
        }