Potato Quality Grading Based on Depth Imaging and Convolutional Neural Network

Abstract

As a cost-effective and nondestructive detection method, the machine vision technology has been widely applied in the detection of potato defects. Recently, the depth camera which supports range sensing has been used for potato surface defect detection, such as bumps and hollows. In this study, we developed a potato automatic grading system that uses a depth imaging system as a data collector and applies a machine learning system for potato quality grading. The depth imaging system collects 3D potato surface thickness distribution data and stores depth images for the training and validation of the machine learning system. The machine learning system, which is composed of a softmax regression model and a convolutional neural network model, can grade a potato tube into six different quality levels based on tube appearance and size. The experimental results indicate that the softmax regression model has a high accuracy in sample size detection, with a 94.4% success rate, but a low success rate in appearance classification (only 14.5% for the lowest group). The convolutional neural network model, however, achieved a high success rate not only in size classification, at 94.5%, but also in appearance classification, at 91.6%, and the overall quality grading accuracy was 86.6%. The quality grading based on the depth imaging technology shows its potential and advantages in nondestructive postharvesting research, especially for 3D surface shape-related fields.

The Need for Machines for the Nondestructive Quality Assessment of Potatoes with the Use of Artificial Intelligence Methods and Imaging Techniques

“…The 3D camera can work stably under LED or fluorescent lighting, because the light source of the 3D camera is infrared radiation. It was noticed that the camera can be successfully used to detect unevenness, damage, and depressions, but it is not sensitive enough to identify surface sprouts [ 97 ].…”

Simulation analysis and experimental testing of the grading performance of small potato grading device

“…Additionally, some researchers have embraced cutting‐edge technologies such as machine vision, spectral analysis, and neural networks to conduct precise analyses and identification of potatoes. This enables the potato shape, quality, and damage conditions to be assessed with remarkable accuracy, facilitating more refined and efficient grading processes (Su et al, 2017; Su et al, 2018; Su et al, 2020; Zhang et al, 2019).…”

Potato Surface Defect Detection Based on Deep Transfer Learning

“…In 2015, potatoes were promoted as a staple food in China, with a target of 30% of the total potatoes consumed as a staple food by 2020 [2], which drives potatoes to become the World's fourth-largest food product after wheat, rice, and corn. After harvest, the potato surface is often subject to sprouting and mechanical damage due to human or other external factors [3]. Therefore, grading based on surface quality is crucial to classify products into different categories, which can greatly improve packing, storage, transportation, and other post-harvest operations.…”

“…A variety of devices, such as CCD camera, ultraviolet camera, hyperspectral camera, and X-ray CT, have been utilized and demonstrated their efficacy in capturing essential features of potato surface quality [10]. Sensory images generated by these devices can be used to build estimators to detect a potato's physical size and internal and external defects [3,11]. However, early automated grading systems have extensively utilized image processing algorithms and relied on manually defined image features to build classifiers [12][13][14][15], limiting the robustness and generalization [16] of detection performance due to the variance of potato types, appearances, and damage defects [17].…”