Online Detection of Dry Matter in Potatoes Based on Visible Near-Infrared Transmission Spectroscopy Combined with 1D-CNN

Yalin Guo, Lina Zhang, Zhenlong Li, Yakai He, Chengxu Lv, Yongnan Chen, Huangzhen Lv and Zhilong Du ()
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Yalin Guo: Chinese Academy of Agricultural Mechanization Sciences Group Co., Ltd., Beijing 100083, China
Lina Zhang: Chinese Academy of Agricultural Mechanization Sciences Group Co., Ltd., Beijing 100083, China
Zhenlong Li: Chinese Academy of Agricultural Mechanization Sciences Group Co., Ltd., Beijing 100083, China
Yakai He: Key Laboratory of Agricultural Products Processing Equipment, Ministry of Agriculture and Rural Affairs, Beijing 100083, China
Chengxu Lv: Chinese Academy of Agricultural Mechanization Sciences Group Co., Ltd., Beijing 100083, China
Yongnan Chen: College of International Education, Beijing University of Agriculture, Beijing 102206, China
Huangzhen Lv: Chinese Academy of Agricultural Mechanization Sciences Group Co., Ltd., Beijing 100083, China
Zhilong Du: Chinese Academy of Agricultural Mechanization Sciences Group Co., Ltd., Beijing 100083, China

Agriculture, 2024, vol. 14, issue 5, 1-14

Abstract: More efficient resource utilization and increased crop utilization rate are needed to address the growing demand for food. The efficient quality testing of key agricultural products such as potatoes, especially the rapid testing of key nutritional indicators, has become an important strategy for ensuring their quality and safety. In this study, visible and near infrared (Vis/NIR) transmittance spectroscopy (600–900 nm) was used for the online analysis of multiple quality parameters in potatoes. The study concentrated on comparing three one-dimensional convolutional neural network (1D-CNN) models, specifically, the fine-tuned DeepSpectra, the fine-tuned 1D-AlexNet, and classic CNN, with UVE-PLS (uninformative variable elimination–partial least squares) models. These models utilized spectral data for the real-time detection of dry matter (DM) content in potatoes. To address the challenges posed by limited data from Vis/NIR, this study strategically implemented data augmentation techniques. This approach significantly enhanced the robustness and generalization capabilities of the models. The 1D-AlexNet and DeepSpectra models achieved 0.934 and 0.913 R 2 P and 0.0603 and 0.0695 g/100 g RMSEP for DM, respectively. Compared to UVE-PLS, the R 2 P value improved by 21.31% (0.770 to 0.934) for the 1D-AlexNet model and 18.64% (0.770 to 0.913) for the DeepSpectra model. The RMSEP value was reduced by 47.31% (0.114 to 0.0603) for 1D-AlexNet, and 39.30% (0.114 to 0.0695) for the DeepSpectra model. As a result, this study would be helpful for researching the online Vis/NIR transmission determination of potato DM using deep learning. These results highlighted the immense potential of employing specific spectral features in deep-learning models for a more precise and efficient online assessment of agricultural quality. This advancement provided some insight and reference for further contributing to the evolution of more targeted and efficient quality assessment methods in agricultural products.

Keywords: potato; transmission spectroscopy; dry matter; online; 1D-CNN (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: 2024
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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