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@article{174728,
        author = {Soniyalakshmi K and Yogashree P and Ramya V and Saridha V and Vidhyabharathi B},
        title = {Cucumber Leaf Disease Detection and Diagnosis},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {430-435},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=174728},
        abstract = {A key harvest in the Cucurbitaceae family, Gurke (Cucumis sativus) is highly susceptible to leaf diseases and requires early detection and accurate pesticide spraying for effective management. This study suggests a hybrid scaffold combining folding folding networks (CNNs) and support vector machines (SVMs) to predict cucumber blade disease and to recommend optimized pesticide treatments. This model processes high-resolution leaf images to assess disease severity, and classifies diseases such as mold, carbonic acid, fusarium blight, bacterial altitude, prismatic prism, and grinding fluids. The system integrates weather forecasts and soil health analysis to improve forecasting and decision-making-disease decisions. Analysis of environmental factors (temperature, humidity, precipitation) and floor parameters (nutrition level, pH, moisture) creates pesticide problems with the data. This approach ensures efficient control of disease, reduces chemical overuse, promotes sustainable agriculture, and promotes health, revenue quality and environmental harvests.},
        keywords = {Cucumis Sativus, Pathogen Severity Quantification, Precision Agriculture, Smart Farming Technologies.},
        month = {March},
        }