Early Apple Bruise Detection Based on Near Infrared Spectroscopy and Near Infrared Camera Multi-Band Imaging
YANG Zeng-rong1, 2, WANG Huai-bin1, 2, TIAN Mi-mi1, 2, LI Jun-hui1, 2, ZHAO Long-lian1, 2*
1. College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
2. Key Laboratory of Modern Precision Agriculture System Integration Research Ministry of Education, Beijing 100083, China
Abstract：Early light bruising in apples is an important factor affecting apple quality. Since early minor bruises cannot be identified by the naked eye in visible light, and in order to control the time of bruise production, a red Fuji apple was selected as the research object, and different degrees of apple bruises were artificially created through an inverted pendulum device. To find an efficient method to identify early minor apple bruises, Fourier transforms near-infrared spectrometer was first used to collect near-infrared diffuse reflectance spectra of 80 undamaged samples, 60 lightly damaged samples, and 60 heavily damaged samples at 0, 10, 20 and 30 min post-damage, respectively. SNV is used as the spectral data preprocessing method. The spectral range is 4 000~9 000 cm-1, and the number of principal components is 9. The “Nondestructive-damage” classification model is established by partial least squares -discriminant analysis (PLS-DA) and support vector machine (SVM) respectively. The average recognition rate of the prediction set was 85.00% and 89.80%, respectively, and the model recognition effect needs to be improved. Based on the above experimental results, the NIR images of apples with no damage, light bruise, moderate bruise, and severe bruise of 100 apples were acquired by using NIR cameras with a wavelength range of 1 000 to 2 350 nm. The NIR images of these apples were acquired again with the addition of 1 150 and 1 400 nm filters, respectively. 1 200 images of apples in 3 bands and 4 bruise levels were acquired. The image absorbance information was extracted, and KNN, SVM, and DT classification models were built respectively. The highest recognition rates of 99.00% and 94.67% were achieved by the “nondestructive-damage” classification model and the “nondestructive-mild damage-severe damage” classification model using the DT method, respectively. Compared with the NIR spectroscopy method, the NIR camera multi-band imaging method has higher recognition accuracy in applying both early bruises and bruise degree classification on apple surfaces. At the same time, the NIR camera imaging method is convenient for determining the location of the bruise, which provides a fast and efficient new idea for real-time online detection and classification of bruises on apple surfaces.
Key words：Near infrared spectroscopy; Near-infrared camera imaging; Apple; Early bruise
杨增荣，王怀彬，田密密，李军会，赵龙莲. 苹果早期瘀伤的近红外光谱和多波段相机成像检测[J]. 光谱学与光谱分析, 2024, 44(05): 1364-1371.
YANG Zeng-rong, WANG Huai-bin, TIAN Mi-mi, LI Jun-hui, ZHAO Long-lian. Early Apple Bruise Detection Based on Near Infrared Spectroscopy and Near Infrared Camera Multi-Band Imaging. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(05): 1364-1371.
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