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Green plums have produced significant economic benefits because of their nutritional and medicinal value. However, green plums are affected by factors such as plant diseases and insect pests during their growth, picking, transportation, and storage, which seriously affect the quality of green plums and their products, reducing their economic and nutritional value. At present, in the detection of green plum defects, some researchers have applied deep learning to identify their surface defects. However, the recognition rate is not high, the types of defects identified are singular, and the classification of green plum defects is not detailed enough. In the actual production process, green plums often have more than one defect, and the existing detection methods ignore minor defects. Therefore, this study used the vision transformer network model to identify all defects on the surfaces of green plums. The dataset was classified into multiple defects based on the four types of defects in green plums (scars, flaws, rain spots, and rot) and one type of feature (stem). After the permutation and combination of these defects, a total of 18 categories were obtained after the screening, combined with the actual situation. Based on the VIT model, a fine-grained defect detection link was added to the network for the analysis layer of the major defect hazard level and the detection of secondary defects. The improved network model has an average recognition accuracy rate of 96.21% for multiple defect detection of green plums, which is better than that of the VGG16 network, the Desnet121 network, the Resnet18 network, and the WideResNet50 network.

Engineering bamboo is a type of cheap and good-quality, easy-to-process material, which is widely used in construction engineering, bridge engineering, water conservancy engineering and other fields; however, crack defects lead to reduced reliability of the engineered bamboo. Accurate identification of the crack tip position and crack propagation length can improve the reliability of the engineered bamboo. Digital image correlation technology and high-quality images have been used to measure the crack tip damage zone of engineered bamboo, but the improvement of image quality with more-advanced optical equipment is limited. In this paper, we studied an application based on deep learning providing a super-resolution reconstruction method in the field of engineered bamboo DIC technology. The attention-dense residual and generative adversarial network (ADRAGAN) model was trained using a comprehensive loss function, where network interpolation was used to balance the network parameters to suppress artifacts. Compared with the super resolution generative adversarial network (SRGAN),super resolution ResNet (SRResNet), and bicubic B-spline interpolation, the superiority of the ADRAGAN network in super-resolution reconstruction of engineered bamboo speckle images was verified through assessment of both objective evaluation indices (PSNR and SSIM) and a subjective evaluation index (MOS). Finally, the images generated by each algorithm were imported into the DIC analysis software, and the crack propagation length was calculated and compared. The obtained results indicate that the proposed ADRAGAN method can reconstruct engineered bamboo speckle images with high quality, obtaining a crack detection accuracy of 99.65%.

Solid wood floors are very common in interior decoration, and their color is an important indicator of product quality, selected in order to achieve the overall aesthetic needed to ensure color consistency. In order to realize the sorting of solid wood floors based on color depth, so that the colors of solid wood floors could be freely graded, one image acquisition system was built to collect 108 solid wood floor images and a set of fast sorting methods for solid wood floor color depth was developed. Among these, 10 solid wood floor images were used as the test set and therefore not sorted, and 98 solid wood floor images were sorted by color depth. Among these, 80 original images were expanded 13 times to 1040, for use as a training set, and 18 were used as a validation set. The color characteristics of solid wood floors in RGB, HSV and Lab color space were extracted, and LightGBM was used to realize the color depth sorting of the solid wood floors. At the same time, two deep learning algorithms, the Vision Transformer as well as Densenet121, improved by means of an adaptive pooling layer, were used to realize the color depth sorting of solid wood floor images of different sizes. The final ranking results showed that the color ranking method using LightGBM to regress the color features exhibited the most harmonious final results.

In industrial applications, bolt connections are simple and economical, contributing to their popularity for use in wood packing boxes. However, they can easily fail when subjected to a continuous vibrational load under usual working conditions such as transportation and hoisting. Based on an ultrasonic technique, nondestructive evaluation can be used to quickly detect large-scale structures, but the complex propagation properties in wood limit its application. To solve this problem, a time-reversal method was adopted to predict the residual preload on bolted connections by focusing on the signals collected by wood structures, which helps to assess the structures’ reliability. In this study, the residual preload of bolted connections in wood structures was predicted using the deep-learning method, LSTM, one-dimensional Resnet and Densenet, and tree classification models. It was confirmed that the use of the time-reversal method for ultrasonic detection focused on the signals transmitted in bolted connections of wood structures and deep-learning methods are a feasible way to predict an ultrasonic transmission model.

The acidity of green plum has an important influence on the fruit’s deep processing. Traditional physical and chemical analysis methods for green plum acidity detection are destructive, time-consuming, and unable to achieve online detection. In response, a rapid and non-destructive detection method based on hyperspectral imaging technology was studied in this paper. Research on prediction performance comparisons between supervised learning methods and unsupervised learning methods is currently popular. To further improve the accuracy of component prediction, a new hyperspectral imaging system was developed, and the kernel principle component analysis—linear discriminant analysis—extreme gradient boosting algorithm (KPCA-LDA-XGB) model was proposed to predict the acidity of green plum. The KPCA-LDA-XGB model is a supervised learning model combined with the extreme gradient boosting algorithm (XGBoost), kernel principal component analysis (KPCA), and linear discriminant analysis (LDA). The experimental results proved that the KPCA-LDA-XGB model offers good acidity predictions for green plum, with a correlation coefficient (R) of 0.829 and a root mean squared error (RMSE) of 0.107 for the prediction set. Compared with the basic XGBoost model, the KPCA-LDA-XGB model showed a 79.4% increase in R and a 31.2% decrease in RMSE. The use of linear, radial basis function (RBF), and polynomial (Poly) kernel functions were also compared and analyzed in this paper to further optimize the KPCA-LDA-XGB model.

The green plum is rich in amino acids, lipids, inorganic salts, vitamins, and trace elements. It has high nutritional value and medicinal value and is very popular among Chinese people. However, green plums are susceptible to collisions and pests during growth, picking, storage, and transportation, causing surface defects, affecting the quality of green plums and their products and reducing their economic value. In China, defect detection and grading of green plum products are still performed manually. Traditional manual classification has low accuracy and high cost, which is far from meeting the production needs of green plum products. In order to improve the economic value of green plums and their products and improve the automation and intelligence level of the product production process, this study adopted deep learning methods based on a convolutional neural network and cost-effective computer vision technology to achieve efficient classification of green plum defects. First, a camera and LEDs were used to collect 1240 green plum images of RGB, and the green plum experimental classification standard was formulated and divided into five categories, namely, rot, spot, scar, crack, and normal. Images were randomly divided into a training set and test set, and the number of images of the training set was expanded. Then, the stochastic weight averaging (SWA) optimizer and w-softmax loss function were used to improve the VGG network, which was trained and tested to generate a green plum defect detection network model. The average recognition accuracy of green plum defects was 93.8%, the test time for each picture was 84.69 ms, the recognition rate of decay defect was 99.25%, and the recognition rate of normal green plum was 95.65%. The results were compared with the source VGG network, resnet18 network, and green lemon network. The results show that for the classification of green plum defects, the recognition accuracy of the green plum defect detection network increased by 9.8% and 16.6%, and the test speed is increased by 1.87 and 6.21 ms, respectively, which has certain advantages.