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.

Design and Experimentation of a Machine Vision-Based Cucumber Quality Grader

“…This not only reduces the manual labor intensity but also ensures consistency in product quality. Haiyan Cen et al [14] utilized hyperspectral images and PCA methods to detect cucumbers with internal hollow defects in the food processing line, achieving an accuracy of In the research on grading systems and equipment for rod-shaped fruits and vegetables, Yee-Siang Gan et al [20] combined deep learning to design an online cucumber grading system. After visual detection, cucumbers were graded by blocking the rolling cucumbers with a baffle.…”

Study of an auxiliary trimming device for cracked carrots

“…When using trimming devices and auxiliary devices to trim carrots, machine vision technology was required for detecting and locating crack defects. Machine vision technology can extract information of agricultural products (including the area, texture, color, greyscale, and the aspect ratio) for grading or defect identification (Sivaranjani et al, 2022;Zhang, Cheng, & Chen, 2022) of cucumbers (Gan et al, 2022), peppers (Zhao et al, 2020) and sugar cane (Modi et al, 2023), etc. Similarly, carrot defect identification has been studied by a large number of scholars.…”

Development of an algorithm to select bananas and quinces according to their state of maturity through artificial vision applied in the industry.

“…Otro aporte importante en el desarrollo de reconocimiento usando una red neuronal ocurrió en las Universidades de Taiwán y en Malasia. Investigadores diseñaron un sistema automatizado capaz de inspeccionar a los pepinos basándose en su apariencia [10]. Usaron elementos de software y hardware que desarrollan las características geométricas de la hortaliza, todo por medio de la visualización de una cámara.…”