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AI Driven Crop Disease Prediction and Management System

Author

Listed:
  • R. Karthikeyan
  • Bainaboina Nandhini
  • Avula Mounika
  • Siddhavatam Venkatesh
  • Mopuri Sandeep

Abstract

Agriculture is extremely important to human civilization, providing food and contributing to the economy. Plants are often susceptible to diseases and insects that have considerable challenges during production. Early detection of harvest diseases is important to minimize damage and reduce costs. While traditional methods do not provide real-time identification, foldable neuronal networks (CNNs) provide a solution by allowing for accurate detection and classification of leaf disease. This study focuses on identifying diseases in plants such as apples, grapes, corn, potatoes and tomatoes. The proposed deep CNN model is compared to a transfer learning approach, such as VGG16. AI-based systems analyze plant images to recognize diseases at the early stages and recommend management strategies, loss of harvests and improved yields. Such systems have applications in agriculture and biological research.

Suggested Citation

  • R. Karthikeyan & Bainaboina Nandhini & Avula Mounika & Siddhavatam Venkatesh & Mopuri Sandeep, 2025. "AI Driven Crop Disease Prediction and Management System," International Journal of Scientific Research in Computer Science, Engineering and Information Technology, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 11(2), pages 3347-3351, March.
    • Handle: RePEc:jbh:ijsrcs:v11:y2025:i2:id:1380
    DOI: 10.32628/CSEIT25112817
    Note: Article URL: https://ijsrcseit.com/home/article/view/CSEIT25112817
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