An automated cucumber inspection system based on neural network

Abstract

Precision agriculture and smart farming have been gaining importance in recent years due to the coupled breakthrough of deep learning algorithms in machine vision. This paper aims to develop an end-to-end automatic agricultural food grading system based on its visual appearance. The target object considered herein is cucumber as it is one of the vegetables that can be grown in many countries around the world. Particularly, the developed system incorporates both the software and hardware components, in which the geometric properties of a moving cucumber on a conveyor belt can be computed. Concretely, an industrial camera is employed to capture the image of a cucumber. Then, three individual detection systems that perform the cucumber identification, geometry properties approximation, and defect detection, are designed. Finally, if the cucumber is found defective, the PLC motor control will be activated to separate the cucumber into an alternative container. As a result, the proposed algorithms yield promising performances when experimenting on a self-collected data set, namely “Cuc-70” that consists of a total of 4620 images. The cucumber identification generates an average WIoU of 93%, volume approximation accuracy of 98%, and defect detection WIoU of 92%. In addition, comprehensive analysis is conducted in order to validate the robustness of the proposed system and the compelling performance executed can be evidenced from the quantitative and qualitative results reported. In the future, this system can be integrated into online automatic sorting and grading for effective manufacturing and production.