MUMBAI, India, Aug. 17 -- Intellectual Property India has published a patent application (202641096589 A) filed by Akshaya College Of Engineering And Technology on August 10, 2026, for Ai-Enabled Vegetable Quality Detection And Sorting System.
Inventors include Mr. R. Gokul Raju; Mr. A. Balaji; Mr. R. Mani Bharathi; and Mr. S. Naveen Kumar.
The application for the patent was published on August 14, 2026, under issue no. 33/2026.
Abstract: ABSTRACT AI-ENABLED VEGETABLE QUALITY DETECTION AND SORTING SYSTEM Vegetable quality inspection is a critical process in the agricultural supply chain, yet traditional manual sorting methods are slow, inconsistent, and labor-intensive, resulting in significant post-harvest losses and inconsistent product quality. This project presents an AI-Enabled Vegetable Quality Detection and Sorting System that integrates YOLOv8 deep learning, ESP32-CAM wireless camera streaming, OpenCV dark spot analysis, Python Flask server, and Arduino Mega embedded control to automate the detection and physical sorting of potatoes and onions based on surface quality. The ESP32-CAM module captures live video streams of vegetables on the conveyor belt and transmits frames via WiFi to the Python Flask server, where the trained YOLOv8 model identifies the vegetable type above 70% confidence threshold and OpenCV performs HSV color space analysis to classify each vegetable as good or affected based on dark spot percentage thresholds. Classification results are transmitted via USB serial communication to the Arduino Mega, which manages IR sensor-based motor triggering, servo motor bin diversion directing good potatoes to Bin A and good onions to Bin B, while affected vegetables pass straight through without sorting. A real-time web dashboard displays live camera feed, detection results, confidence scores, dark spot levels, servo status, emergency stop control, and detection log history. The system achieves average detection confidence of 85% for potatoes and 78% for onions, complete sorting cycles of 3.2 seconds, and 100% IR sensor reliability, demonstrating a practical and cost-effective approach to transforming manual vegetable inspection into a smart AI-Enabled agricultural quality management system.
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