Tea bud detection is the first step in the precise picking of famous teas. Accurate and fast tea bud detection is crucial for achieving intelligent tea bud picking. However, existing detection methods still exhibit limitations in both detection accuracy and speed due to the intricate background of tea buds and their small size. This study uses YOLOv5 as the initial network and utilizes attention mechanism to obtain more detailed information about tea buds, reducing false detections and missed detections caused by different sizes of tea buds; The addition of Spatial Pyramid Pooling Fast (SPPF) in front of the head to better utilize the attention module’s ability to fuse information; Introducing the lightweight convolutional method Group Shuffle Convolution (GSConv) to ensure model efficiency without compromising accuracy; The Mean-Positional-Distance Intersection over Union (MPDIoU) can effectively accelerate model convergence and reduce the training time of the model. The experimental results demonstrate that our proposed method achieves precision (P), recall rate (R) and mean average precision (mAP) of 93.38%, 89.68%, and 95.73%, respectively. Compared with the baseline network, our proposed model’s P, R, and mAP have been improved by 3.26%, 11.43%, and 7.68%, respectively. Meanwhile, comparative analyses with other deep learning methods using the same dataset underscore the efficacy of our approach in terms of P, R, mAP, and model size. This method can accurately detect the tea bud area and provide theoretical research and technical support for subsequent tea picking.

Green, healthy and sustainable development is the development direction of China’s agriculture, and stable fertilizer has become the first choice for orchard fertilizer. In this paper, in order to improve the working performance of the combined fertilizer application device of 2KF-30 type orchard stable fertilizer deep applicator, the matching degree of the combined operation of scraper and auger was taken as the research objective, and theoretical analysis, numerical simulation, and experimental verification were combined to carry out the research. Applying fuzzy theory, the comprehensive qualified rate weighted by the uniformity of fertilization and the qualified rate of fertilization was proposed as the evaluation index. The scraper shaft speed, scraper space, spiral conveyor speed, and spiral conveyor pitch were selected as the test factors, and the comprehensive qualified rate of fertilization for the combined fertilization device was taken as the evaluation index. The test design was carried out by single-factor and response surface method tests, and numerical simulations were conducted by discrete element software to analyze the optimal combination of fertilizer application parameters. The test results were analyzed based on the numerical simulation of discrete element software. The testbed was set up to verify the above optimal results. The results showed that the maximum error between the physical test and the numerical simulation was 8.67%, the minimum error was 1.75% and the overall mean error between the physical test value of the combined fertilizer application pass rate and the predicted value of the quadratic regression equation was 5.99%, which can meet the operational performance requirements of the fertilization device. The research results can provide a theoretical basis for the design and improvement of the deep fertilizing machine for orchard manure.

As the existing residual film crushing device in Xinjiang cannot directly crush membrane-impurity mixed material, by analyzing the compressive and cutting force characteristics of residual film material layer and cotton stalk, the cutting conditions of mixed materials were obtained, and the method of cutting was determined. A multi-edge toothed cutters was created, and a cutting device was built. It was preliminarily determined that the number of teeth in the cutters was 8, the clearance between the teeth and between the tooth and fixed blade was 3 mm, the speed of the high-speed cutter was 800 r/min, and the speed difference between the high- and low-speed cutters was –300 r/min. Test results show that the ratio of residual film to total residual film sampling mass was (2.22±0.3)%, (19.19±2.02)%, (58.94±3.19)% and (20.65±2.05)%, respectively, when the maximum outer profile size in the range of [0,20) mm, [20,100) mm, [100,500) mm and [500,~) mm. The mass of cotton stalks with lengths of [0,50) mm, [50,100) mm and [100,~) mm accounted for (32.57±1.5)%, (27.77±1.3)% and (39.66±1.75)%, respectively, and the cutting power consumption was (85.41±15.63) kJ. The test results can provide a basis for the subsequent membrane-impurity mixed material cutting technology, as well as some guidance for the separation of it. Keywords: multi-edge toothed cutter; membrane-impurity mixed material; cutting device; cutting conditions DOI: 10.25165/j.ijabe.20231602.7240 Citation: Liang R Q, Zhang B C, Chen X G, Meng H W, Wang X Z, Shen C J, Li Y P, Kan Z. Design and test of a multi-edge cutting device for membrane-impurity mixed material. Int J Agric & Biol Eng, 2023; 16(2): 73-84.

Efficient furrow fertilization is extremely critical for fertilizer utilization, fruit yield, and fruit quality. The precise determination of trench quality necessitates the accurate measurement of its characteristic parameters, including its shape and three-dimensional structure. Some existing algorithms are limited to detecting only the furrow depth while precluding the tridimensional reconstruction of the trench shape. In this study, a novel method was proposed for three-dimensional trench shape reconstruction and its parameter detection. Initially, a low-cost multi-source data acquisition system with the 3D data construction method of the trench was developed to address the shortcomings of single-sensor and manual measurement methods in trench reconstruction. Subsequently, the analysis of the original point cloud clarified the “coarse-fine” two-stage point cloud filtering process, and then a point cloud preprocessing method was proposed based on ROI region extraction and discrete point filtering. Furthermore, by analyzing the characteristics of the point cloud, a random point preselection condition based on the variance threshold was designed to optimize the extraction method of furrow side ground based on RANSAC. Finally, a method was established for extracting key characteristic parameters of the trench and trench reconstruction based on the fitted ground model of the trench side. Experimental results demonstrated that the point cloud pretreatment method could eliminate 83.8% of invalid point clouds and reduce the influence of noise points on the reconstruction accuracy. Compared with the adverse phenomena of fitting ground incline and height deviation of the original algorithm results, the ground height fitted by the improved ditch surface extraction algorithm was closer to the real ground, and the identification accuracy of inner points of the ground point cloud was higher than that of the former. The error range, mean value error, standard deviation error, and stability coefficient error of the calculated ditch width were 0 ~ 5.965%, 0.002 m, 0.011 m, and 0.37%, respectively. The above parameters of the calculated depth were 0 ~ 4.54%, 0.003 m, 0.017 m, and 0.47%, respectively. The results of this research can provide support for the comprehensive evaluation of the quality of the ditching operation, the optimization of the structure of the soil touching part, and the real-time control of operation parameters.

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.