Classification of Different Maturity Stages of Camellia Oleifera Fruit
Using Hyperspectral Imaging Technique
YUAN Wei-dong1, 2, JU Hao2, JIANG Hong-zhe1, 2, LI Xing-peng2, ZHOU Hong-ping1, 2*, SUN Meng-meng1, 2
1. Jiangsu Co-Innovation Center of Efficient Processing and Utilization of Forest Resources, Nanjing Forestry University, Nanjing 210037, China
2. College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China
Abstract：Camellia oleifera fruit is widely planted in hilly and mountainous areas in southern China. The harvest time of Camellia oleifera fruit is currently decided by solar terms and experience, and the prematurity or too late picking will bring economic losses. This study aimed to explore the feasibility of hyperspectral imaging (HSI) technology to identify the maturity stages of Camellia oleifera fruit accurately. The HSI system with a spectral range of 400～1 000 nm was applied to collect hyperspectral images of 480 Camellia oleifera fruit samples at different maturity stages. PLS-DA and PSO-SVM models were individually developed based on spectra preprocessed with five different pretreatments including SNV, SNV-detrend, SG, first-order derivative and second-order derivative. The optimal preprocessing method was selected and further used in feature wavelength screening. Consequently, it was found that the simplified model built by feature wavelengths selected using CARS gave better performance compared to SPA. The classification accuracies of CARS-PLS-DA and CARS-PSO-SVM models in the prediction set were 92.5% and 89.2%, respectively, and the kappa coefficients were above 0.86. Furthermore, color features were extracted from the hyperspectral images by color moment approach, and PLS-DA and PSO-SVM models were built based on the combination of color features and feature wavelengths. Then, the performance of the models built by feature wavelengths screened by CARS was still found to be the best with classification accuracies of 94.2% and 93.3% for CARS+color-PLS-DA and CARS+color-PSO-SVM models in the prediction set, respectively. The models developed by combination features showed better classification results than models based on wavelengths alone, and the classification accuracies were improved by 1.7% and 4.1% in the prediction set, respectively. The optimal CARS+color-PLS-DA model gave the best predicted performance with its Kappa coefficient of 0.923 1. As a result, our work indicates that the application of HSI technology combined with chemometric methods can be used to identify the maturity stages of Camellia oleifera fruit, which provides a rapid, nondestructive and accurate way in Camellia oleifera fruit maturity detection.
袁伟东，鞠 皓，姜洪喆，李兴鹏，周宏平，孙梦梦. 基于高光谱成像技术的油茶果不同成熟阶段判别[J]. 光谱学与光谱分析, 2023, 43(11): 3419-3426.
YUAN Wei-dong, JU Hao, JIANG Hong-zhe, LI Xing-peng, ZHOU Hong-ping, SUN Meng-meng. Classification of Different Maturity Stages of Camellia Oleifera Fruit
Using Hyperspectral Imaging Technique. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3419-3426.
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