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Novel method for selecting the regions of interest in hyperspectral images of apples with random poses on the sorting line

文献类型： 外文期刊

作者： Qi, Kangkang 1 ; Wu, Wenbin 2 ; Wang, Shuai 1 ; Mu, Yuanjie 1 ; Song, Qian 2 ; Wang, Fengyun 1 ; Fan, Yangyang 1 ; Feng, Wenjie 1 ; Li, Rui 1 ; Fu, Longsheng 3 ;

作者机构： 1.Shandong Acad Agr Sci, Agr Informat & Econ Inst, Jinan 250100, Peoples R China

2.Chinese Acad Agr Sci, Inst Agr Resources & Reg Planning, State Key Lab Efficient Utilizat Arid & Semiarid A, Beijing 100081, Peoples R China

3.Northwest A&F Univ, Coll Mech & Elect Engn, Yangling 712100, Shaanxi, Peoples R China

关键词： apple; hyperspectral imaging; nondestructive detection; region of interest; sugar content

期刊名称：INTERNATIONAL JOURNAL OF AGRICULTURAL AND BIOLOGICAL ENGINEERING （ 影响因子：2.2； 五年影响因子：2.4 ）

ISSN： 1934-6344

年卷期： 2025 年 18 卷 1 期

页码：

收录情况： SCI

摘要： Recent approaches to the internal quality inspection of apples with the application of hyperspectral imaging technology are highly cost-intensive because of labor involvement for the data collection on a fixed posture and manual selection of the region of interest (RoI). In addition, several studies have repeated the data acquisition for the same apple. Current methods cannot meet the automation requirements of the sorting line. Therefore, this study proposed a novel method for automatically selecting RoI in hyperspectral images of apples with random poses. Firstly, the preliminary RoI selection of apple hyperspectral image was carried out, followed by the performance of histogram statistics of each pixel with spectral intensity at 700 nm wavelength. The top 40% area of the spectral intensity was reserved to obtain the magnitude relationship of the spectral intensity of each pixel point and a morphological erosion operation. Original apple RoI was acquired and overexposed pixels were removed with spectral intensity greater than 3900 (maximum 4095) in the reserved area at 700 nm. Secondly, the relationship between apple size and prediction accuracy was measured for the in-depth RoI analysis. A partial least square regression (PLSR) model was established between the average spectrum and apple sugar content of RoI with different sizes. Finally, the established model with the top 70% of the spectral intensity achieved the best prediction accuracy. Non-destructive estimation of apple sugar content was performed through hyperspectral imaging technology with reference to the proposed RoI selection method. A competitive adaptive reweighted sampling algorithm along the PLSR (CARS-PLSR) model was established after black-and-white correction and standard normal transformation (SNV) preprocessing and obtained the highest prediction accuracy. The determination coefficient of cross-validation (R-cv) and root mean square error of cross- validation (RMSECV) were 0.9595 and 0.3203 degrees Brix, respectively. The determination coefficient of prediction (R-p) was 0.9308, and the root mean square error of prediction (RMSEP) was 0.4681 degrees Brix. Results proved that the auto-selection of RoI is an efficient and accurate method, which can provide a foundation in practical application for online apple grading systems with hyperspectral imaging technology.

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