Non-Destructive Detection of External Defects in Potatoes Using Hyperspectral Imaging and Machine Learning
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...
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MDPI AG
2025-03-01
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| Series: | Agriculture |
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| author | Ping Zhao Xiaojian Wang Qing Zhao Qingbing Xu Yiru Sun Xiaofeng Ning |
| author_facet | Ping Zhao Xiaojian Wang Qing Zhao Qingbing Xu Yiru Sun Xiaofeng Ning |
| author_sort | Ping Zhao |
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| description | 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. |
| format | Article |
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| institution | OA Journals |
| issn | 2077-0472 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
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| series | Agriculture |
| spelling | doaj-art-c532deb28a9f44d7b10d5e1da85e22f52025-08-20T02:11:11ZengMDPI AGAgriculture2077-04722025-03-0115657310.3390/agriculture15060573Non-Destructive Detection of External Defects in Potatoes Using Hyperspectral Imaging and Machine LearningPing Zhao0Xiaojian Wang1Qing Zhao2Qingbing Xu3Yiru Sun4Xiaofeng Ning5College of Engineering, Shenyang Agricultural University, Shenyang 110866, ChinaCollege of Engineering, Shenyang Agricultural University, Shenyang 110866, ChinaCollege of Engineering, Shenyang Agricultural University, Shenyang 110866, ChinaCollege of Engineering, Shenyang Agricultural University, Shenyang 110866, ChinaCollege of Engineering, Shenyang Agricultural University, Shenyang 110866, ChinaCollege of Engineering, Shenyang Agricultural University, Shenyang 110866, ChinaFor 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.https://www.mdpi.com/2077-0472/15/6/573hyperspectral imaging techniquemachine learningexternal defect detectionred-skin potato |
| spellingShingle | Ping Zhao Xiaojian Wang Qing Zhao Qingbing Xu Yiru Sun Xiaofeng Ning Non-Destructive Detection of External Defects in Potatoes Using Hyperspectral Imaging and Machine Learning Agriculture hyperspectral imaging technique machine learning external defect detection red-skin potato |
| title | Non-Destructive Detection of External Defects in Potatoes Using Hyperspectral Imaging and Machine Learning |
| title_full | Non-Destructive Detection of External Defects in Potatoes Using Hyperspectral Imaging and Machine Learning |
| title_fullStr | Non-Destructive Detection of External Defects in Potatoes Using Hyperspectral Imaging and Machine Learning |
| title_full_unstemmed | Non-Destructive Detection of External Defects in Potatoes Using Hyperspectral Imaging and Machine Learning |
| title_short | Non-Destructive Detection of External Defects in Potatoes Using Hyperspectral Imaging and Machine Learning |
| title_sort | non destructive detection of external defects in potatoes using hyperspectral imaging and machine learning |
| topic | hyperspectral imaging technique machine learning external defect detection red-skin potato |
| url | https://www.mdpi.com/2077-0472/15/6/573 |
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