The accurate measurement of orchard canopy volume serves as a crucial foundation for wind regulation and dosage adjustments in precision orchard management. However, existing methods for measuring canopy volume fail to satisfy the high precision and real-time requirements necessary for accurate variable-rate applications in fruit orchards. To address these challenges, this study develops a canopy volume measurement model for orchard spraying using LiDAR point cloud data. In the domain of point cloud feature extraction, an improved Alpha Shape algorithm is proposed for extracting point cloud contours. This method improves the validity judgment for contour line segments, effectively reducing contour length errors on each 3D point cloud projection plane. Additionally, improvements to the mesh integral volume method incorporate the effects of canopy gaps in height difference calculations, significantly enhancing the accuracy of canopy volume estimation. For feature selection, a random forest-based recursive feature elimination method with cross-validation was employed to filter 10 features. Ultimately, five key features were retained for model training: the number of point clouds, the 2D point cloud contour along the X- and Z-projection directions, the 2D width in the Y-projection direction, and the 2D length in the Z-projection direction. During model construction, the study optimized the hyperparameters of partial least squares regression (PLSR), backpropagation (BP) neural networks, and gradient boosting decision trees (GBDT) to build canopy volume measurement models tailored to the dataset. Experimental results indicate that the PLSR model outperformed other approaches, achieving optimal performance with three principal components. The resulting canopy volume measurement model achieved an R² of 0.9742, an RMSE of 0.1879, and an MAE of 0.1161. These results demonstrate that the PLSR model exhibits strong generalization ability, minimal prediction bias, and low average prediction error, offering a valuable reference for precision control of canopy spraying in orchards.

The residual agricultural plastic film in China is not easily recovered due to the thinness and poor mechanical properties of domestic films, and a large amount of plastic film remaining in farmland soil poses a great threat to soil quality and crop production. A spring-tooth residual plastic film collector (SRPFC) is widely used in domestic residual plastic film (RPF) recycling operations. However, there are two major problems in the current SRPFC: the low recovery rate of the residual film (RRRF) caused by the difficulty of film-stripping and the high impurity rate in the film (IRF). In this paper, a stripping and impurity removal device (SIRD) is designed to address the existing problems of SRPFC, which is mainly composed of film-stripping tooth plates (FTP), two wind-collecting hoods, and two centrifugal fans. The motion and force analysis of the RPF in the film-stripping process was carried out, and the arc FTP was determined to be used for film-stripping. The size parameters of the FTP were obtained by establishing the coordinate system to solve the differential equation. By comparing and analyzing the force of RPF in the airflow field of the test bench for suspension speed and the airflow field of the wind-collecting hood, the RPF equivalent particle was established. The discrete phase model (DPM) in Fluent software was used to simulate the movement of the RPF equivalent particle, and the calculated air volume range of the centrifugal fan was 5501.88~6829.92 m³/h. The effects of forward speed, rotating speed of film conveying chain harrow (FCCH), and rotating speed of the centrifugal fan on RRRF and IRF were studied by orthogonal rotary combination experiment. The test results showed that the best combination of machine operation parameters was when the forward speed was 5 km/h, the rotating speed of the FCCH was 235 r/min, and the rotating speed of the centrifugal fan was 1978 r/min. Under these conditions, the RRRF was 92.53%, and the IRF was 9.31%. Field experiments were carried out with the rounded parameters, and the average RRRF was 92.07%, and the average IRF was 9.56% under the parameter combination, indicating that the optimization scheme of the device was feasible.

Bearings, as commonly used elements in mechanical apparatus, are essential in transmission systems. Fault diagnosis is of significant importance for the normal and safe functioning of mechanical systems. Conventional fault diagnosis methods depend on one or more vibration sensors, and their diagnostic results are often unsatisfactory under strong noise interference. To tackle this problem, this research develops a bearing fault diagnosis technique utilizing a multi-channel, multi-scale spatiotemporal convolutional cross-attention fusion network. At first, continuous wavelet transform (CWT) is applied to convert the raw 1D acoustic and vibration signals of the dataset into 2D time–frequency images. These acoustic and vibration time–frequency images are then simultaneously fed into two parallel structures. After rough feature extraction using ResNet, deep feature extraction is performed using the Multi-Scale Temporal Convolutional Module (MTCM) and the Multi-Feature Extraction Block (MFE). Next, these traits are input into a dual cross-attention mechanism module (DCA), where fusion is achieved using attention interaction. The experimental findings validate the efficacy of the proposed method using tests and comparisons on two bearing datasets. The testing findings validate that the suggested method outperforms the existing advanced multi-sensor fusion diagnostic methods. Compared with other existing multi-sensor fusion diagnostic methods, the proposed method was proven to outperform the five existing methods (1DCNN-VAF, MFAN-VAF, 2MNET, MRSDF, and FAC-CNN).

At present, the picking of nectarines mainly relies on manual completion in China, and the process involves high labor intensity during picking and low picking efficiency. Therefore, it is necessary to introduce automated picking. To improve the accuracy of nectarine fruit recognition in complex environments and to increase the efficiency of automatic orchard-picking robots, a lightweight nectarine detection method, YOLOv8n-CSD, is proposed in this study. This model improves on YOLOv8n by first proposing a new structure, C2f-PC, to replace the C2f structure used in the original network, thus reducing the number of model parameters. Second, the SEAM is introduced to improve the model’s recognition of the occluded part. Finally, to realize real-time detection of nectarine fruits, the DySample Lightweight Dynamic Upsampling Module is introduced to save computational resources while effectively enhancing the model’s anti-interference ability. With a compact size of 4.7 MB, this model achieves 95.1% precision, 84.9% recall, and a mAP@0.5 of 93.2%—the model’s volume has been reduced while the evaluation metrics have all been improved over the baseline model. The study shows that the YOLOv8n-CSD model outperforms the current mainstream target detection models, and can recognize nectarines in different environments faster and more accurately, which lays the foundation for the field application of automatic picking technology.

In the harvesting operation, the stubble height of the grain is a vital parameter index in the combined harvesting operation; if the stubble is too high or too low, it will directly affect the harvesting quality and the service life of the header. At present, the profiling control system can only control the lift of the header in the vertical direction but not the horizontal direction and the angle of the cutter profiling. This study proposes a contouring control strategy and system for grain harvesting by analyzing the designed contouring adjustment mechanism and simulating the control method and hydraulic system through Amesim2404 software to simulate and analyze the control method and hydraulic system. Finally, different forward speeds of the harvester (5, 7, 9, and 11 km/h) and other cutting heights of the harvester were analyzed based on static and field tests and different stubble heights (100, 150, 200, and 250 mm) on the test indexes. The results of the field test showed that for different operating speeds, the error between the mean value of stubble height and the target value was small, the absolute error was less than 2 mm, the mean value of the coefficient of variation of stubble height was 4.53%, and the mean value of control accuracy is 94%. The developed adaptive control system of the profiled grain header has high precision and stability, which can provide a reference for the all-terrain profiling control technology of the combined harvester header deck.

In order to improve the working performance of an Alfalfa air conveyor, a type of horizontal screw conveyor with a seed stirring function was designed. The working process of the horizontal screw conveyor was co-simulated by EDEM software and ADAMS software. The effects of a seed stirring mechanism on population movement characteristics, population stress, the seed mass flow rate and the variation coefficient of each index were obtained. The results showed that the stirring mechanism can effectively improve the mobility of the population, reduce the local dead zone of the population, and increase the material filling coefficient between the spiral blades. Under the same working conditions, the horizontal screw conveyor with a stirring mechanism has a higher conveying efficiency and better conveying uniformity. In order to optimize the structure parameters of the Venturi injector diffuser and improve seeding efficiency and uniformity, EDEM software and Fluent software were used to co-simulate the seeding process. The influence of diffuser structure parameters on the working performance of the Venturi injector was analyzed by taking the changes in pressure and velocity in the inner flow field of the pipeline and the velocity and force of seed particles as indices. The results showed that when the diffusion angle is 5°, the length of the diffusion section is 200 mm and the length of the mixing section is 50 mm, the pressure loss of the Venturi ejector is the smallest, the outlet air velocity is the largest, the uniformity of seed feeding is the best and the seed feeding efficiency is the highest. Taking the inlet air pressure and particle feeding efficiency as test factors and the variation number of ejector discharge as test index, a two-factor and five-level full factor test was carried out, and the range and variance analyses were carried out. The results showed that the seed feeding rate and the inlet wind pressure had significant effects on the coefficient of variation. The optimal combination of working parameters was 1.6 kpa inlet wind pressure and 1.8 g/s particle feeding efficiency.