Field Grading of Longan SSC via Vis-NIR and Improved BP Neural Network

Jun Li, Meiqi Zhang, Kaixuan Wu, Hengxu Chen, Zhe Ma, Juan Xia and Guangwen Huang ()
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Jun Li: College of Engineering, South China Agricultural University, Guangzhou 510642, China
Meiqi Zhang: College of Engineering, South China Agricultural University, Guangzhou 510642, China
Kaixuan Wu: College of Engineering, South China Agricultural University, Guangzhou 510642, China
Hengxu Chen: College of Engineering, South China Agricultural University, Guangzhou 510642, China
Zhe Ma: College of Engineering, South China Agricultural University, Guangzhou 510642, China
Juan Xia: College of Engineering, South China Agricultural University, Guangzhou 510642, China
Guangwen Huang: College of Engineering, South China Agricultural University, Guangzhou 510642, China

Agriculture, 2024, vol. 14, issue 12, 1-22

Abstract: Soluble solids content (SSC) measurements are crucial for managing longan production and post-harvest handling. However, most traditional SSC detection methods are destructive, cumbersome, and unsuitable for field applications. This study proposes a novel field detection model (Brix-back propagation neural network, Brix-BPNN), designed for longan SSC grading based on an improved BP neural network. Initially, nine preprocessing methods were combined with six classification algorithms to develop the longan SSC grading prediction model. Among these, the model preprocessed with Savitzky–Golay smoothing and the first derivative (SG-D1) demonstrated a 7.02% improvement in accuracy compared to the original spectral model. Subsequently, the BP network structure was refined, and the competitive adaptive reweighted sampling (CARS) algorithm was employed for feature wavelength extraction. The results show that the improved Brix-BPNN model, integrated with the CARS, achieves the highest prediction performance, with a 2.84% increase in classification accuracy relative to the original BPNN model. Additionally, the number of wavelengths is reduced by 92% compared to the full spectrum, making this model both lightweight and efficient for rapid field detection. Furthermore, a portable detection device based on visible-near-infrared (Vis-NIR) spectroscopy was developed for longan SSC grading, achieving a prediction accuracy of 83.33% and enabling fast, nondestructive testing in field conditions.

Keywords: nondestructive testing; longan; soluble solids content; visible-near-infrared spectroscopy; brix-back propagation neural network (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
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