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


Authors:Hemant Rajput, Kamlesh Patidar, Himanshu Gendhar, Shilpa Bhalerao, Nidhi Nigam, Chanchal Bansal


Published in: Volume 3 Issue 1 Jan June 2026, Page No.329-335


Keywords:Agriculture, Artificial intelligence, Convolutional neural networks, crop disease detection, machine learning, sustainable farming


Abstract:Agriculture plays an important role in maintaining the global economy and food security, but crop diseases remain an important challenge, leading to significant yield deficits. Traditional methods of detection of the disease depend on manual inspection, which is time-consuming, labor-intensive, and often interferes with early detections. It presents the paper agargard, which is the prediction and management system of the AI- managed crop disease designed to solve these challenges. The sys- tem takes advantage of firm nervous networks (CNN) to analyze environmental parameters such as temperature, humidity, and soil moisture as well as high-resolution crop images, providing actionable recommendations for early detection and treatment of crop diseases. Experimental results suggest that agriguard acquires 93.7 accuracy in identifying normal crop diseases in various agricultural settings. The user-friendly web interface of the system ensures access to farmers with different technical expertise, making it a practical solution to increase agricultural production, reduce crop losses and promote permanent farming practices.


Download PDF