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@article{204124,
        author = {Rutuja V. Mhaske and DR. M.R.Bendre and Prof. V. S. Chaudhar},
        title = {Result Analysis of Crop Leaf Disease Detection Severity Using IP & ML Techniques with Remote Sensing},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {1},
        pages = {746-750},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=204124},
        abstract = {Here suggested system aims to create an intelligent solution for detecting and monitoring agricultural leaf diseases through image processing and machine learning techniques. The primary goal is to properly identify numerous types of diseases in crop leaves and estimate their severity in order to facilitate prompt intervention and crop management. A huge collection of diseased and healthy crop leaf photos is used for training, allowing the system to identify distinct patterns and symptoms associated with various diseases. Using modern Convolutional Neural Network (CNN) methods, the model can achieve high classification accuracy and offer accurate results even for complicated leaf images. The system is built as a Python-based online application, allowing farmers, agronomists, and researchers to upload leaf photos and receive quick results. Image processing techniques are used to improve features, segment sick areas, and extract useful data for machine learning models. The incorporation of remote sensing data adds environmental context, improves disease prediction, and enables precision agriculture operations. Overall, this research brings together modern AI and IoT-enabled agriculture technologies to create a real-time, scalable, and user-friendly platform for crop leaf disease monitoring. It not only aids in early identification but also guides farmers in implementing preventive steps, therefore boosting crop health, productivity, and sustainability.},
        keywords = {Crop Leaf Disease Detection, Image Processing, Machine Learning, Convolutional Neural Network (CNN), Remote Sensing, Severity Monitoring, Python Web Application, Precision Agriculture, etc.},
        month = {June},
        }