Accurate road extraction and recognition of roadside fruit in complex orchard environments are essential prerequisites for robotic fruit picking and walking behavioral decisions. In this study, a novel algorithm was proposed for unstructured road extraction and roadside fruit synchronous recognition, with wine grapes and nonstructural orchards as research objects. Initially, a preprocessing method tailored to field orchards was proposed to reduce the interference of adverse factors in the operating environment. The preprocessing method contained 4 parts: interception of regions of interest, bilateral filter, logarithmic space transformation and image enhancement based on the MSRCR algorithm. Subsequently, the analysis of the enhanced image enabled the optimization of the gray factor, and a road region extraction method based on dual-space fusion was proposed by color channel enhancement and gray factor optimization. Furthermore, the YOLO model suitable for grape cluster recognition in the wild environment was selected, and its parameters were optimized to enhance the recognition performance of the model for randomly distributed grapes. Finally, a fusion recognition framework was innovatively established, wherein the road extraction result was taken as input, and the optimized parameter YOLO model was utilized to identify roadside fruits, thus realizing synchronous road extraction and roadside fruit detection. Experimental results demonstrated that the proposed method based on the pretreatment could reduce the impact of interfering factors in complex orchard environments and enhance the quality of road extraction. Using the optimized YOLOv7 model, the precision, recall, mAP, and F1-score for roadside fruit cluster detection were 88.9%, 89.7%, 93.4%, and 89.3%, respectively, all of which were higher than those of the YOLOv5 model and were more suitable for roadside grape recognition. Compared to the identification results obtained by the grape detection algorithm alone, the proposed synchronous algorithm increased the number of fruit identifications by 23.84% and the detection speed by 14.33%. This research enhanced the perception ability of robots and provided a solid support for behavioral decision systems.

Focus on the shortage of Leymus chinensis seeds’ discrete element parameters in the research of the seeds in production equipment, the study carried out parameter calibration and verification. The focus of this study is on the shortage of discrete element parameters for Leymus chinensis seeds in research related to seed production equipment. The study involved parameter calibration and verification. First, the physical property parameters of the seeds were measured through physical experiments to determine the range of the simulation parameters. Using Response Surface Methodology, a simulation accumulation test was conducted. Subsequently, the simulation parameters were calibrated and optimized. Specifically, significant parameters affecting the repose angle were identified using the Plackett–Burman Design test. The optimal value range of these parameters was then determined through a steepest slope climbing test. A second-order regression model between the significant parameters and the simulated stacking angle was established through the Box–Behnken Design experiment. The regression model was optimized by using the repose angle from the physical test as the target value. The obtained optimal parameter combination was then used to conduct a simulation test. The results showed that the relative error between the simulation and physical test was only 0.94%. This proves the accuracy, reliability, and authenticity of the simulated contact parameters. The study provides a theoretical basis for the optimization design and simulation research of Leymus chinensis seeds harvesting technology and equipment.

Temperature stability was taken as the evaluation index of processing performance, and the three factors that influence normal milk processing and mixing performance were optimized by response surface analysis and BP‐GA neural network algorithm. Analysis results showed the influence order of the factors on temperature stability was as follows: shape > height > rotating speed. In the optimization by response surface methodology (RSM), when rotating speed was 30 r/min, height was 31 mm, and blade shape was a full trapezoid, predicted value and actual value of variable coefficient were 0.0046 and 0.0044 respectively, with relative error of 4.5%. In the optimization by BP‐GA neural network algorithm, when rotating speed was 34 r/min, height was 25 mm, and blade shape was a full trapezoid, the predicted value and actual value of variable coefficient were 0.0036 and 0.0035 respectively, with relative error of 2.9%. The predicted root‐mean‐square error of the model by the BP‐GA neural network algorithm was 0.0013, determination coefficient was 0.9960, and relative percent deviation was 8.4961, which showed better performance than the RSM model. Thus, the BP‐GA neural network algorithm has better fitting performance, and then, the optimal working parameter combination was confirmed, which could provide reference to improving double‐blade normal milk processing and mixing device design and milk processing quality.

In this study, we propose a novel approach that integrates hyperspectral imaging with machine learning to address the challenge of rapid and non-destructive detection of artificial pigment adulteration in Wuyi Congou tea. Compared with traditional chemical methods, this method enables the identification of pigment types, quantification of adulteration rate, and visualization of pigment distribution. A hyperspectral dataset of black tea adulterated with three synthetic pigments (Tartrazine, Carmine, and Sunset Yellow) was constructed, supporting the establishment of qualitative identification and quantitative prediction models. The qualitative model achieved an accuracy greater than 98 %, while the quantitative model yielded a predictive correlation coefficient (R_p) greater than 0.95 and root mean square error of prediction (RMSEP) less than 0.025. These results validate the feasibility and effectiveness of this approach for detecting and quantifying pigment adulteration in black tea, providing new possibilities for tea quality and safety inspection.

Rapid and non-destructive detection methods for the withering degree of fresh tea leaves are crucial for ensuring high-quality tea production. Therefore, this study proposes a fresh tea withering degree detection model based on image classification confidence. The moisture percentage of fresh tea leaves is calculated by developing a weighted method that combines confidence levels and moisture labels, and the degree of withering is ultimately determined by incorporating the standard for wilted moisture content. To enhance the feature extraction ability and classification accuracy of the model, we introduce the Receptive-Field Attention Convolution (RFAConv) and Cross-Stage Feature Fusion Coordinate Attention (C2f_CA) modules. The experimental results demonstrate that the proposed model achieves a classification accuracy of 92.7%. Compared with the initial model, the detection accuracy was improved by 0.156. In evaluating the predictive performance of the model for moisture content, the correlation coefficients (Rp), root mean square error (RMSEP), and relative standard deviation (RPD) of category 1 in the test set were 0.9983, 0.006278, and 39.2513, respectively, and all performance were significantly better than PLS and CNN methods. This method enables accurate and rapid detection of tea leaf withering, providing crucial technical support for online determination during processing.