Detection of surface defects on wet-blue leather is much more challenging than raw-hide leather. Since wet-blue leather turns blue and contains moisture after pre-treatment, it is a semi-product of the cowhide processing. At present, the defect detection of wet-blue leather is mostly carried out manually and is time-consuming and labor-intensive for professional inspectors. This paper is the first to use hyperspectral imaging (HSI) to implement the surface inspection of five wet-blue leather defects including brand masks, rotten grain, rupture, insect bites, and scratches in the pixel level detection. Hyperspectral Leather Defect Detection Algorithm (HLDDA) including Hyperspectral Target Detection (HTD) and Deep Learning (DL) techniques was proposed in this paper. In HTD, Weighted Background Suppression Constrained Energy Minimization (WBS-CEM) and WBS-Hierarchical CEM (WBS-hCEM) were developed in this paper by using weighting to suppress the background and enhance the contrast between the target and background. Experimental results showed that the overall performance of WBS was better than the original CEM. In the DL part, 1D-Convolutional Neural Network (CNN), 2D-Unet and 3D-UNet architectures were designed to segment defect areas. For various characteristics of defects, 1D-CNN emphasizes on defects with spectral features, 2D-Unet emphasizes on defects with spatial features, and 3D-Unet can simultaneously process spatial and spectral information in HSI. The experimental results verified that the proposed HLDDA could effectively quantify and estimate the size of the defect, thereby accelerating the leather inspection process by professional inspectors and develop an automated leather grading system towards Industry 4.0.

The defective beans of coffee are categorized into black beans, fermented beans, moldy beans, insect damaged beans, parchment beans, and broken beans, and insect damaged beans are the most frequently seen type. In the past, coffee beans were manually screened and eye strain would induce misrecognition. This paper used a push-broom visible-near infrared (VIS-NIR) hyperspectral sensor to obtain the images of coffee beans, and further developed a hyperspectral insect damage detection algorithm (HIDDA), which can automatically detect insect damaged beans using only a few bands and one spectral signature. First, by taking advantage of the constrained energy minimization (CEM) developed band selection methods, constrained energy minimization-constrained band dependence minimization (CEM-BDM), minimum variance band prioritization (MinV-BP), maximal variance-based bp (MaxV-BP), sequential forward CTBS (SF-CTBS), sequential backward CTBS (SB-CTBS), and principal component analysis (PCA) were used to select the bands, and then two classifier methods were further proposed. One combined CEM with support vector machine (SVM) for classification, while the other used convolutional neural networks (CNN) and deep learning for classification where six band selection methods were then analyzed. The experiments collected 1139 beans and 20 images, and the results demonstrated that only three bands are really need to achieve 95% of accuracy and 90% of kappa coefficient. These findings show that 850–950 nm is an important wavelength range for accurately identifying insect damaged beans, and HIDDA can indeed detect insect damaged beans with only one spectral signature, which will provide an advantage in the process of practical application and commercialization in the future.

Leaf maturation from initiation to senescence is a phenological event of plants that results from the influences of temperature and water availability on physiological activities during a life cycle. Detection of newly grown leaves (NGL) is therefore useful for the diagnosis of tree growth, tree stress, and even climatic change. This paper applies Constrained Energy Minimization (CEM), which is a hyperspectral target detection technique to spot grown leaves in a UAV multispectral image. According to the proportion of NGL in different regions, this paper proposes three innovative CEM based detectors: Subset CEM, Sliding Window-based CEM (SW CEM), and Adaptive Sliding Window-based CEM (AWS CEM). AWS CEM can especially adjust the window size according to the proportion of NGL around the current pixel. The results show that AWS CEM improves the accuracy of NGL detection and also reduces the false alarm rate. In addition, the results of the supervised target detection depend on the appropriate signature. In this case, we propose the Optimal Signature Generation Process (OSGP) to extract the optimal signature. The experimental results illustrate that OSGP can effectively improve the stability and the detection rate.