Non-Destructive Detection of External Defects in Potatoes Using Hyperspectral Imaging and Machine Learning

Ping Zhao (), Xiaojian Wang, Qing Zhao, Qingbing Xu, Yiru Sun and Xiaofeng Ning
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Ping Zhao: College of Engineering, Shenyang Agricultural University, Shenyang 110866, China
Xiaojian Wang: College of Engineering, Shenyang Agricultural University, Shenyang 110866, China
Qing Zhao: College of Engineering, Shenyang Agricultural University, Shenyang 110866, China
Qingbing Xu: College of Engineering, Shenyang Agricultural University, Shenyang 110866, China
Yiru Sun: College of Engineering, Shenyang Agricultural University, Shenyang 110866, China
Xiaofeng Ning: College of Engineering, Shenyang Agricultural University, Shenyang 110866, China

Agriculture, 2025, vol. 15, issue 6, 1-21

Abstract: For potato external defect detection, ordinary spectral technology has limitations in detail detection and processing accuracy, while the machine vision method has the limitation of a long feedback time. To realize accurate and rapid external defect detection for red-skin potatoes, a non-destructive detection method using hyperspectral imaging and a machine learning model was explored in this study. Firstly, Savitzky–Golay (SG), standard normal variate transformation (SNV), multiplicative scatter correction (MSC), the normalization algorithm, and different preprocessing algorithms combined with SG were used to preprocess the hyperspectral data. Then, principal component regression (PCR), support vector machine (SVM), partial least squares regression (PLSR), and least squares support vector machine (LSSVM) algorithms were used to establish quantitative models to find the most suitable preprocessing algorithm. The successive projections algorithm (SPA) was used to obtain various characteristic wavelengths. Finally, the qualitative models were established to detect the external defects of potatoes using the machine learning algorithms of backpropagation neural network (BPNN), k-nearest neighbors (KNN), classification and regression tree (CART), and linear discriminant analysis (LDA). The experimental results showed that the SG–SNV fusion hyperspectral data preprocessing algorithm and the KNN machine learning model were the most suitable for the detection of external defects in red-skin potatoes. Moreover, multiple external defects can be detected without multiple models. For healthy potatoes, black/green-skin potatoes, and scab/mechanical-damage/broken-skin potatoes, the detection accuracy was 93%,93%, and 83%, which basically meets the production requirements. However, enhancing the prediction accuracy of the scab/mechanical-damage/broken-skin potatoes is still a challenge. The results also demonstrated the feasibility of using hyperspectral imaging technology and machine learning technology to detect potato external defects and provided new insights for potato external defect detection.

Keywords: hyperspectral imaging technique; machine learning; external defect detection; red-skin potato (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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