Maturity Stage Discrimination of Camellia oleifera Fruit Using Visible and Near-Infrared Hyperspectral Imaging

Abstract

The maturity of Camellia oleifera fruit is one of the most important indicators to optimize the harvest day, which, in turn, results in a high yield and good quality of the produced Camellia oil. A hyperspectral imaging (HSI) system in the range of visible and near-infrared (400–1000 nm) was employed to assess the maturity stages of Camellia oleifera fruit. Hyperspectral images of 1000 samples, which were collected at five different maturity stages, were acquired. The spectrum of each sample was extracted from the identified region of interest (ROI) in each hyperspectral image. Spectral principal component analysis (PCA) revealed that the first three PCs showed potential for discriminating samples at different maturity stages. Two classification models, including partial least-squares discriminant analysis (PLS-DA) and principal component analysis discriminant analysis (PCA-DA), based on the raw or pre-processed full spectra, were developed, and performances were compared. Using a PLS-DA model, based on second-order (2nd) derivative pre-processed spectra, achieved the highest results of correct classification rates (CCRs) of 99.2%, 98.4%, and 97.6% in the calibration, cross-validation, and prediction sets, respectively. Key wavelengths selected by PC loadings, two-dimensional correlation spectroscopy (2D-COS), and the uninformative variable elimination and successive projections algorithm (UVE+SPA) were applied as inputs of the PLS-DA model, while UVE-SPA-PLS-DA built the optimal model with the highest CCR of 81.2% in terms of the prediction set. In a confusion matrix of the optimal simplified model, satisfactory sensitivity, specificity, and precision were acquired. Misclassification was likely to occur between samples at maturity stages two, three, and four. Overall, an HSI with effective selected variables, coupled with PLS-DA, could provide an accurate method and a reference simple system by which to rapidly discriminate the maturity stages of Camellia oleifera fruit samples.

Maturity Grading and Identification of Camellia oleifera Fruit Based on Unsupervised Image Clustering

“…For dry seed weight and moisture content, there were significant differences among the maturity stages. This conclusion was in agreement with the previous findings of Jiang et al [ 31 ]. According to the above, the physical properties of Camellia oleifera fruit samples with different maturity stages automatically graded by DeepCluster were basically consistent with previous studies [ 62 ].…”

“…The seed oil content decreased amounted to 1.63% from the ripe to the overripe stage. This conclusion is consistent with the findings of Jiang et al [ 31 ]. In addition, the seed-soluble protein content gradually increased as the fruit matured, which was consistent with the results of previous studies [ 11 ].…”

“…They reported that the overall accuracy of identification for the best model for oil palm maturity identification was 100%. Jiang et al [ 31 ] conducted a study in which they used a hyperspectral imaging system to access the maturity of Camellia oleifera fruit. The Camellia oleifera fruit maturity identification results indicated that their model was 81.2% accurate.…”

Spectral Camouflage Characteristics and Recognition Ability of Targets Based on Visible/Near-Infrared Hyperspectral Images

“…In Experiment 1, the camouflage can be better recognized after 680 nm in the visible light band, and there is obvious spectral difference in the near-infrared band; in Experiment 2, camouflage cannot be effectively identified in visible and near-infrared bands, which indicates that "painting camouflage" can avoid visible and near-infrared bands. Through the analysis and thinking of the experimental research and conclusions, this paper proposes the following prospects: (1) In terms of camouflage materials, the current camouflage materials cannot meet the requirements of visible light, near-infrared and other multi band compatible stealth, so the research and development of new spectral camouflage materials is an urgent problem to be solved. (2) In terms of imaging level, the development of near-infrared hyperspectral imaging technology is not yet mature, and the key lies in the limitation of near-infrared detectors.…”

“…Imaging spectrum technology, also known as hyperspectral imaging technology, is a new technology that integrates imaging technology and spectral analysis technology. It is widely used in agriculture [1], mineral detection [2], environmental detection [3] and other fields. Hyperspectral images can break the restriction of two-dimensional image space, expand object information to the spectral dimension, and effectively improve the accuracy and precision of target classification and detection.…”