In the wagyu industry worldwide, high-quality marbling beef is produced by promoting intramuscular fat deposition during cattle fattening stage through dietary vitamin A control. Thus, however, cattle become susceptible to either vitamin A deficiency or excess state, not only influencing cattle performance and beef quality, but also causing health problems. Researchers have been exploring eye photography monitoring methods for cattle blood vitamin A levels based on the relation between vitamin A and retina colour changes. But previous endeavours cannot realise real-time monitoring and their prediction accuracy still need improvement in a practical sense. This study developed a handheld camera system capable of capturing cattle fundus images and predicting vitamin A levels in real time using deep learning. 4000 fundus images from 50 Japanese Black cattle were used to train and test the prediction algorithms, and the model achieved an average 87%, 83%, and 80% accuracy for three levels of vitamin A deficiency classification (particularly 87% for severe level), demonstrating the effectiveness of camera system in vitamin A deficiency prediction, especially for screening and early warning. More importantly, a new method was exemplified to utilise visualisation heatmap for colour-related DNNs tasks, and it was found that chromatic features extracted from LRP heatmap highlighted-ROI could account for 70% accuracy for the prediction of vitamin A deficiency. This system can assist farmers in blood vitamin A level monitoring and related disease prevention, contributing to precision livestock management and animal well-being in wagyu industry.

Calorific value and moisture content are the key indices to evaluate Caragana pellet fuel’s quality and combustion characteristics. Calorific value is the key index to measure the energy released by energy plants during combustion, which determines energy utilization efficiency. But at present, the determination of solid fuel is still carried out in the laboratory by oxygen bomb calorimetry. This has seriously hindered the ability of large-scale, rapid detection of fuel particles in industrial production lines. In response to this technical challenge, this study proposes using hyperspectral imaging technology combined with various chemometric methods to establish quantitative models for determining moisture content and calorific value in Caragana korshinskii fuel. A hyperspectral imaging system was used to capture the spectral data in the 935–1720 nm range of 152 samples from multiple regions in Inner Mongolia Autonomous Region. For water content and calorific value, three quantitative detection models, partial least squares regression (PLSR), random forest regression (RFR), and extreme learning machine (ELM), respectively, were established, and Monte Carlo cross-validation (MCCV) was chosen to remove outliers from the raw spectral data to improve the model accuracy. Four preprocessing methods were used to preprocess the spectral data, with standard normal variate (SNV) preprocessing performing best on the quantitative moisture content detection model and Savitzky–Golay (SG) preprocessing performing best on the calorific value detection method. Meanwhile, to improve the prediction accuracy of the model to reduce the redundant wavelength data, we chose four feature extraction methods, competitive adaptive reweighted sampling (CARS), successive pojections algorithm (SPA), genetic algorithm (GA), iteratively retains informative variables (IRIV), and combined the three models to build a quantitative detection model for the characteristic wavelengths of moisture content and calorific value of Caragana korshinskii fuel. Finally, a comprehensive comparison of the modeling effectiveness of all methods was carried out, and the SNV-IRIV-PLSR modeling combination was the best for water content prediction, with its prediction set determination coefficient $\left(R_P^2\right)$, root mean square error of prediction (RMSEP), and relative percentage deviation (RPD) of 0.9693, 0.2358, and 5.6792, respectively. At the same time, the moisture content distribution map of Caragana fuel particles is established by using this model. The SG-CARS-RFR modeling combination was the best for calorific value prediction, with its $R_P^2$, RMSEP, and RPD of 0.8037, 0.3219, and 2.2864, respectively. This study provides an innovative technical solution for Caragana fuel particles’ value and quality assessment.

In order to perform quantitative analysis and predict the wear of the forming channel, this study makes biomass pellets as experimental samples. The surface morphology was scanned by a scanning electron microscope (SEM), and the surface morphology data and images were collected by a roughness instrument. Then, we collected the data of arithmetical average deviation Ra, density $\rho$, as well as hardness HD, and further calculated the fractal dimension D, which help to study the influencing factors of fractal dimension on the circular surface morphology of biomass pellet. The results show that, the density $\rho$ and hardness HD of biomass pellets both decreased and with the increase in diameter d, the arithmetical average deviation Ra increased with the diameter d, the quality of the pellet is reduced, meanwhile, the fractal dimension D also shows a downward trend. Using the value, trend of fractal dimension D to analyze the quality of biomass pellet can predict the wear status and life of forming channel in biomass briquette machine. It is concluded that, the fractal expression of surface morphology in biomass pellet relates to inner surface morphology of forming channel in biomass pelleting machine. Additionally, fractal dimension of the surface morphology of biomass pellet can be the basis of preliminary research for friction and wear detection and prediction of forming channel.

Due to their characteristics, Caragana Korshinskii bars are prone to be crushed by kneaded crushed material, and the kneaded crushed material is prone to clogging the sieve, thus affecting the crushing effect. In this paper, we use the Caragana Korshinskii special hammer crusher to carry out the Caragana Korshinskii cutting and crushing test to study the influence of Caragana Korshinskii diameter, cutting length, and moisture content on the crushing effect of Caragana Korshinskii, and obtain the best process parameters through the response surface method. The results were as follows: When the diameter of Caragana Korshinskii strips was in the range of 6–9 mm, the length of the cut section was in the range of 4–8 cm, and the moisture content of Caragana Korshinskii strips was in the range of 10–19%, the Caragana Korshinskii strips crushing effect was the best, and the kneading situation was the lowest. The optimal Caragana Korshinskii crushing effect was achieved when the diameter of the Caragana Korshinskiibar was 6.5 mm, the length of the cut section was 8.4 cm, the moisture content was 19%, the sieve rate of 2 mm was 46.15%, the sieve rate of 4 mm was 66.32%, and the length of the kneaded wire was 3.3 cm.