RECOGNITION OF LEAF ILLNESS DETECTION

Nayan Naik M, R. R
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Abstract

Plant disease early detection has a major impact on crop quality and financial stability, makingit essential for India's agriculture. The precision of current detection techniques is lacking, which puts yields at risk. In order to identify cotton leaf illnesses and classify leaves as healthy, unhealthy, or sick, this study suggests a prediction method. This focused strategy enables focused therapies and aids in the prevention of disease spread. The framework can also be modified to identify diseases that affect tomato plants with the goal of increasing agricultural yields, lowering costs, and promoting environmentally friendly farming methods. Keyword: Leaf, Diseases, Support Vector Machine (SVM), Convolutional Neural Networks(CNN).
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叶病检测识别
植物病害的早期检测对作物质量和财政稳定有重大影响,因此对印度农业至关重要。目前的检测技术精度不够,导致产量受到威胁。为了识别棉花叶片的病害并将叶片分为健康、不健康或病态,本研究提出了一种预测方法。这种有针对性的策略可以实现有针对性的治疗,并有助于防止疾病传播。该框架还可用于识别影响番茄植株的疾病,从而提高农业产量、降低成本并推广环保型耕作方法。关键词叶片 疾病 支持向量机(SVM) 卷积神经网络(CNN)
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