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Mostly Agriculture stands as a cornerstone of economic development, yet faces formidable challenges including labor scarcity, erratic climate patterns, and prevalent plant diseases. Addressing these issues is critical amidst global population growth and escalating food demands. This study focuses on leveraging advanced Convolutional Neural Network (CNN) techniques for early detection and classification of diseases affecting tomato plants. Utilizing the AgroDeep application, the research gathers real-world datasets of diseased tomato leaf images, enabling the development of a robust model. Attaining a remarkable 97% accuracy, the CNN model proves highly effective in correctly identifying and categorizing different tomato plant diseases. By enhancing disease detection at early stages, this research not only boosts agricultural productivity but also supports sustainable farming practices. The application of CNNs in disease diagnosis empowers farmers with proactive management strategies, potentially minimizing crop losses and increasing overall yields. Ultimately, this research contributes significantly to agricultural advancement, optimizing resource allocation and fostering profitability while bolstering global food security.

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A Novel Approach for Analysis and Prediction of Disease in Tomato Leaf Using Machine Learning Algorithm

  • 会议论文

  • 发表日期：

    2026年

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  • Surya Dev Singh Sajwan,
  • C. Saravanan

摘要

Mostly Agriculture stands as a cornerstone of economic development, yet faces formidable challenges including labor scarcity, erratic climate patterns, and prevalent plant diseases. Addressing these issues is critical amidst global population growth and escalating food demands. This study focuses on leveraging advanced Convolutional Neural Network (CNN) techniques for early detection and classification of diseases affecting tomato plants. Utilizing the AgroDeep application, the research gathers real-world datasets of diseased tomato leaf images, enabling the development of a robust model. Attaining a remarkable 97% accuracy, the CNN model proves highly effective in correctly identifying and categorizing different tomato plant diseases. By enhancing disease detection at early stages, this research not only boosts agricultural productivity but also supports sustainable farming practices. The application of CNNs in disease diagnosis empowers farmers with proactive management strategies, potentially minimizing crop losses and increasing overall yields. Ultimately, this research contributes significantly to agricultural advancement, optimizing resource allocation and fostering profitability while bolstering global food security.