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AI-Driven Crop Disease Prediction and Management System
Subject area: Science,Engineering and Technology · Area of research: Agricultural Technology and Precision Farming
Abstract
Agriculture is vital to human civilization, providing food and contributing to the economy. Crops are often vulnerable to diseases and insects, posing significant challenges during production. Early detection of crop diseases is crucial to minimizing damage and reducing costs. Traditional methods fail to provide real-time identification, but Convolutional Neural Networks (CNNs) offer a solution by enabling accurate detection and classification of leaf diseases. This research focuses on identifying diseases in apple, grape, corn, potato, and tomato plants. A proposed deep CNN model is compared with transfer learning approaches like VGG16. The AI-based system analyzes crop images to detect diseases early and recommends management strategies, reducing crop loss and enhancing yield. Such systems have applications in agriculture and biological research.
Keywords
Agriculture Technology, Convolutional Neural Networks (CNNs), Crop Disease Detection, Early Disease Prediction
How to cite this paper
@article{1706876,
author = {Shishir Shastry BH, Aryaman Chakraborty, Abhiram Palukuru, Aishwarya M, Dr. Asha PN},
title = {AI-Driven Crop Disease Prediction and Management System},
journal = {Iconic Research And Engineering Journals},
year = {2025},
volume = {8},
number = {7},
pages = {82-86},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1706876.pdf},
abstract = {Agriculture is vital to human civilization, providing food and contributing to the economy. Crops are often vulnerable to diseases and insects, posing significant challenges during production. Early detection of crop diseases is crucial to minimizing damage and reducing costs. Traditional methods fail to provide real-time identification, but Convolutional Neural Networks (CNNs) offer a solution by enabling accurate detection and classification of leaf diseases. This research focuses on identifying diseases in apple, grape, corn, potato, and tomato plants. A proposed deep CNN model is compared with transfer learning approaches like VGG16. The AI-based system analyzes crop images to detect diseases early and recommends management strategies, reducing crop loss and enhancing yield. Such systems have applications in agriculture and biological research.},
keywords = {Agriculture Technology, Convolutional Neural Networks (CNNs), Crop Disease Detection, Early Disease Prediction},
month = {January},
}