Title:Agrishield-AI-Driven Crop Disease Prediction and Management System


Authors:Harshit Porwal, Nidhi Nigam, Chanchal Bansal


Published in: Volume 2 Issue 2 July-December 2025, Page No. 19-26


DOI: https://doi.org/10.63844/IJAITR.v2.i2.2025.19-26
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Keywords:Crop Disease Prediction, Machine Learning, Deep Learning, Image Processing, Convolutional Neural Networks, Precision Agriculture, Automated Diagnosis, Sustainable Farming


Abstract: Agriculture plays a crucial role in global food security, yet crop diseases pose a significant threat, leading to substantial yield losses and economic challenges for farmers. Traditional disease detection methods rely on manual inspections, making them inefficient and inaccurate. This work, titled quot;Agrishield,quot; focuses on developing an automated solution to classify crop diseases using machine learning (ML) and deep learning (DL) techniques. By leveraging advanced algorithms such as decision trees, random forests, and convolutional neural networks (CNNs), the system analyzes leaf images to identify diseases based on visible symptoms like spots and discoloration. Agrishield provides an efficient, realtime diagnostic solution, enabling farmers to detect and manage crop diseases early. This system aims to enhance agricultural productivity, reduce financial losses, and promote sustainable farming practices [1]. Index Terms: Crop Disease Prediction, Machine Learning, Deep Learning, Image Processing, Convolutional Neural Networks, Precision Agriculture, Automated Diagnosis, Sustainable Farming.


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