An AI-Based Plant Disease Detection and Agri E-Commerce System using Flask and MongoDB
DOI:
https://doi.org/10.15662/IJEETR.2026.0802060Keywords:
Plant Disease Detection, YOLOv8, Deep Learning, Agri E-Commerce, Flask, MongoDB, Precision Agriculture, SMS Notification, Twilio API, Object Detection, Convolutional Neural NetworksAbstract
Agriculture plays a vital role in the economic development of many countries, yet farmers often face significant challenges in identifying crop diseases and accessing quality agricultural products in a timely manner. This paper proposes AgroNest, an AI-Based Plant Disease Detection and Agri E-Commerce System that integrates modern deep learning technology with secure online commerce to support farmers in both crop health management and agricultural product purchasing. The platform provides a web-based interface where farmers can browse, search, and securely purchase agricultural inputs such as seeds, fertilizers, and farming tools. A deep learning-based plant disease detection module allows users to upload images of plant leaves for automated analysis using the YOLOv8 object detection model, which accurately identifies and classifies up to 28 distinct plant disease categories, returning annotated results with bounding boxes and confidence scores. The platform incorporates complete e-commerce functionality including a shopping cart, order management, and payment processing, alongside an automated SMS notification feature powered by the Twilio API that dispatches treatment reminders 48 hours after disease detection. The backend is developed using Flask (Python), MongoDB for flexible NoSQL data management, and Redis for caching and task queuing. Security is enforced through Werkzeug password hashing, JWT-based session management, and role-based access control. Experimental evaluation on 3,000+ labeled leaf images confirmed a mean Average Precision (mAP@0.5) exceeding 91%, while user testing with 30 farmers over three weeks demonstrated high usability, secure transaction handling, and effective notification delivery. The results demonstrate that AI can be practically and affordably integrated into agricultural platforms, contributing meaningfully to precision farming, food security, and sustainable rural development
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