Nondestructive detection for SSC and firmness of plums by hyperspectral imaging and artificial neural network

Jing Shang; Qing-long Meng; Ren-shuai Huang; Yan Zhang

doi:10.1117/12.2589078

18 March 2021 Nondestructive detection for SSC and firmness of plums by hyperspectral imaging and artificial neural network

Jing Shang, Qing-long Meng, Ren-shuai Huang, Yan Zhang

Author Affiliations +

Proceedings Volume 11780, Global Intelligent Industry Conference 2020; 117800I (2021) https://doi.org/10.1117/12.2589078
Event: Global Intelligent Industry Conference 2020, 2020, Guangzhou, China

Abstract

Hyperspectral imaging technique and artificial neural network were used to investigate the feasibility of the nondestructive prediction for firmness and soluble solids content (SSC) of “Red” and “Green” plums. And the standard normal variation (SNV) was adopted to preprocess original spectral reflectance of region of interests. Then 5 and 28 characteristic wavelengths were selected from 256 full wavelengths by the methods of successive projection algorithm (SPA) and competitive adaptive reweighted sampling (CARS), respectively. An error back propagation (BP) network model was proposed based on selected characteristic variables to predict firmness and SSC of plums. The SSC prediction accuracy of CARS-BP model in calibration set (r_c= 0.989, RMESC = 0.451 °Brix) was slightly higher than SPA-BP model (r_c= 0.978, RMESC = 0.589 °Brix), while the SSC prediction accuracy of SPA-BP model in prediction set (r_p = 0.964, RMESP = 0.778 °Brix) was slightly higher than CARS-BP model (rp = 0.955, RMESP = 0.851 °Brix).

Citation Download Citation

Jing Shang, Qing-long Meng, Ren-shuai Huang, and Yan Zhang "Nondestructive detection for SSC and firmness of plums by hyperspectral imaging and artificial neural network", Proc. SPIE 11780, Global Intelligent Industry Conference 2020, 117800I (18 March 2021); https://doi.org/10.1117/12.2589078

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