Smart Defect Detection and Sortation through Image Processing for Corn

Abstract

This research aims to develop smart defect detection and sortation for corn with the use of image processing. This project helps to speed up manual inspection to improve productivity and reduce the time consumed by farmers in sorting. The defect detection process is done through image processing using an open computer vision library and Python. The corn is manually placed in the roller conveyor, passing under a camera acquiring the real-time image. The sorting process eliminates the damaged corn from those that were in good condition. The system acquires real-time image data from a camera feed to a computer for analyzing purposes. Images were scanned as the corn ear was traveling through the conveyor. A program that effectively analyzes acquired corn images' required features was developed in Python using the Open Computer Vision (OCV) library. A conveyor that comes with a built-in corn sortation mechanism was controlled by Raspberry Pi that directs the corn to its desired group. On the evaluation of the accuracy of the system, a series of trials were conducted. The result of the evaluation yielded a 92% success rate in terms of defect detection and sortation. Revisions were made after the initial testing of the project. Problems and its causes were identified to improve the performance of the whole system.