The Fusion of Focused Spectral and Image Texture Features: A New Exploration of the Nondestructive Detection of Degeneration Degree in Pleurotus geesteranus

Yifan Jiang, Jin Shang, Yueyue Cai, Shiyang Liu, Ziqin Liao, Jie Pang, Yong He and Xuan Wei ()
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Yifan Jiang: College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou 350108, China
Jin Shang: College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou 350108, China
Yueyue Cai: College of Food Science, Fujian Agriculture and Forestry University, Fuzhou 350002, China
Shiyang Liu: College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou 350108, China
Ziqin Liao: College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou 350108, China
Jie Pang: College of Food Science, Fujian Agriculture and Forestry University, Fuzhou 350002, China
Yong He: College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
Xuan Wei: College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou 350108, China

Agriculture, 2025, vol. 15, issue 14, 1-19

Abstract: The degradation of edible fungi can lead to a decrease in cultivation yield and economic losses. In this study, a nondestructive detection method for strain degradation based on the fusion of hyperspectral technology and image texture features is presented. Hyperspectral and microscopic image data were acquired from Pleurotus geesteranus strains exhibiting varying degrees of degradation, followed by preprocessing using Savitzky–Golay smoothing (SG), multivariate scattering correction (MSC), and standard normal variate transformation (SNV). Spectral features were extracted by the successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS), and principal component analysis (PCA), while the texture features were derived using gray-level co-occurrence matrix (GLCM) and local binary pattern (LBP) models. The spectral and texture features were then fused and used to construct a classification model based on convolutional neural networks (CNN). The results showed that combining hyperspectral and image texture features significantly improved the classification accuracy. Among the tested models, the CARS + LBP-CNN configuration achieved the best performance, with an overall accuracy of 95.6% and a kappa coefficient of 0.96. This approach provides a new technical solution for the nondestructive detection of strain degradation in Pleurotus geesteranus .

Keywords: Pleurotus geesteranus; hyperspectral imaging; feature fusion; nondestructive detection (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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