Xiaohui Lei

Accurately extracting the canopies of fruit trees is crucial to improve the estimation accuracy of CNC inversion as well as determine a reasonable application of nitrogen fertilizer. To date, existing studies have mainly focused on canopy extraction in scenarios with no grass or sparse grass cover, paying less attention to scenarios with a full grass cover. Thus, in this paper, a two-stage canopy extraction (TCE) method was proposed to address the issue of canopy extraction in scenarios with full grass cover. Firstly, the height difference between the canopies of pear trees and the ground grass was used to eliminate the interference of the ground grass and achieve a coarse-grained canopy extraction. Then, based on the extracted coarse-grained canopies and CIELAB color space, the color thresholds of the L*, a*, and b* channels were determined to remove the interference factors, e.g., branches, shadows, and trellises, for fine-grained canopy extraction by using data distribution from the three channels based on a histogram and the threshold of confidence interval. In canopy extraction experiments, the accuracy, recall, precision, and F1-score of TCE in scenarios with full grass cover can reach 91.725%, 95.789%, 91.284%, and 93.482%, respectively, demonstrating the effectiveness of TCE in addressing canopy extraction issues in this scenario. Thirdly, the RF algorithm was utilized to select suitable VIs based on R² and RMSE values, and CNC inversion models were constructed. In estimation experiments on CNC inversion, the R², RMSE, and nRMSE of the constructed CNC inversion based on TCE in a scenario with full grass cover were 0.724, 0.243, and 19.120%, respectively. A comparative analysis with the baseline method revealed that accurate canopy extraction contributed to a high estimation accuracy of CNC inversion. Therefore, our proposed method can provide technical support for the efficient and non-destructive monitoring of the canopy nutrient status in pear orchards.

Performance evaluation of mechanical blossom thinning in Y-trellis pear orchard

Design and test of one-rotor orchard horizontal rotary rake based on tine movement trajectory analysis

With the rapid pace of urbanization, a significant number of rural laborers are migrating to cities, leading to a severe shortage of agricultural labor. Consequently, the modernization of agriculture has become a priority. Autonomous picking robots represent a crucial component of agricultural technological innovation, and their development drives progress across the entire agricultural sector. This paper reviews the current state of research on fruit- and vegetable-picking robots, focusing on key aspects such as the vision system sensors, target detection, localization, and the design of end-effectors. Commonly used target recognition algorithms, including image segmentation and deep learning-based neural networks, are introduced. The challenges of target recognition and localization in complex environments, such as those caused by branch and leaf obstruction, fruit overlap, and oscillation in natural settings, are analyzed. Additionally, the characteristics of the three main types of end-effectors—clamping, suction, and cutting—are discussed, along with an analysis of the advantages and disadvantages of each design. The limitations of current agricultural picking robots are summarized, taking into account the complexity of operation, research and development costs, as well as the efficiency and speed of picking. Finally, the paper offers a perspective on the future of picking robots, addressing aspects such as environmental adaptability, functional diversity, innovation and technological convergence, as well as policy and farm management.

To reduce pesticide pollution and promote sustainable agricultural development in China, we designed a pilot-scale biofilter system to treat residual imidacloprid wastewater in an orchard. The biofilter system demonstrated a high rate of removal of imidacloprid from the biodegradation wastewater, with removal rates from the outlet exceeding 99% at different concentrations of pesticides. Among environmental factors, imidacloprid concentration at the inlet and biomixture significantly affected the activity of imidacloprid-degrading bacteria. The dominant microbial communities during the stable operation of the biofilter system included Firmicutes, Actinobacteria, Proteobacteria, and Bacteroidetes at the phylum level and Bacillus, Methylobacter, and unclassified_f__Microbacteriaceae at the genus level. In future initiatives to improve biofilter performance and applicability, increasing attention should be paid to the dominant microbial communities, the number of biofilter units, and important environmental factors. Orchard workers in China should improve the existing treatment of residual pesticide wastewater to mitigate agricultural non-point source pollution.

In addressing the challenges of high energy consumption and low efficiency in fertilization borehole drilling for clayey soils in southern orchards, this study utilizes the Discrete Element Method to establish a simulation model for clayey soils. Through this approach, we identify an optimal set of operational parameters that significantly reduces energy consumption. By utilizing simulation technology to model the drilling process, we analyzed the impact of rotation speed and feed rate on the torque and resistance of the drilling apparatus. Initially, this paper describes field measurements of particle parameters in soils from southern orchards. Subsequently, utilizing the Discrete Element Method and particle contact theory, we established a simulation model to represent the interactions between soil and soil, as well as soil and auger in the soil environment of the southern region. For the Southern orchard clay with a moisture content of 16.8% and a measured angle of repose of 35.55°, parameter calibration was performed. The contact model “Hertz-Mindlin with Johnson-Kendall-Roberts” was selected in EDEM. Using Design Expert, a regression model variance analysis was applied to the discrete element model parameters, leading to the determination of the optimal values for significant soil model parameters. The soil JKR surface energy is 5.85 J·m⁻², with a soil–soil restitution coefficient of 0.65 and a soil–steel static friction coefficient of 0.5. Subsequently, discrete element simulation experiments on the drilling apparatus were conducted in EDEM, considering various rotation speeds and feed rates. The simulation analysis indicates that the torque consistently increases with higher rotation speeds, with a maximum relative error of 7%. The torque initially rises from zero to a maximum value, then gradually decreases to a low value, followed by a rapid increase to a higher value, and finally drops back down. This cycle repeats in the observed pattern. The total force experienced reaches its minimum average value of 200 N at a feed rate of 0.05 m/s. Simulation test results indicate that, among the three forces acting on the auger (vertical resistance, horizontal resistance, and lateral resistance), vertical resistance is the primary factor contributing to power consumption. As the rotation speed increases, the maximum value of vertical resistance continues to rise, while horizontal resistance and lateral resistance exhibit a declining trend. As the feed rate increases, the maximum values of resistance in all three directions also increase. When the feed rate exceeds 0.05 m/s, the maximum lateral resistance experiences a sharp increase. Through comprehensive analysis, the optimal operational parameters for borehole fertilization are determined to be a rotation speed of 100 r·min⁻¹ and a feed rate of 0.05 m/s. The aim of this study is to reduce the energy consumption of borehole fertilization operations, minimize carbon emissions, and promote the sustainable development of orchard production.

Efficient and precise thinning during the orchard blossom period is a crucial factor in enhancing both fruit yield and quality. The accurate recognition of inflorescence is the cornerstone of intelligent blossom equipment. To advance the process of intelligent blossom thinning, this paper addresses the issue of suboptimal performance of current inflorescence recognition algorithms in detecting dense inflorescence at a long distance. It introduces an inflorescence recognition algorithm, YOLOv7-E, based on the YOLOv7 neural network model. YOLOv7 incorporates an efficient multi-scale attention mechanism (EMA) to enable cross-channel feature interaction through parallel processing strategies, thereby maximizing the retention of pixel-level features and positional information on the feature maps. Additionally, the SPPCSPC module is optimized to preserve target area features as much as possible under different receptive fields, and the Soft-NMS algorithm is employed to reduce the likelihood of missing detections in overlapping regions. The model is trained on a diverse dataset collected from real-world field settings. Upon validation, the improved YOLOv7-E object detection algorithm achieves an average precision and recall of 91.4% and 89.8%, respectively, in inflorescence detection under various time periods, distances, and weather conditions. The detection time for a single image is 80.9 ms, and the model size is 37.6 Mb. In comparison to the original YOLOv7 algorithm, it boasts a 4.9% increase in detection accuracy and a 5.3% improvement in recall rate, with a mere 1.8% increase in model parameters. The YOLOv7-E object detection algorithm presented in this study enables precise inflorescence detection and localization across an entire tree at varying distances, offering robust technical support for differentiated and precise blossom thinning operations by thinning machinery in the future.

Orchard thinning can avoid biennial bearing and improve fruit quality, which is a necessary agronomic section in orchard management. The existing methods of artificial fruit thinning and chemical spraying are no longer suitable for the development of modern agriculture. With the continuous acceleration of the construction process of modern orchards, blossom thinning mechanization has become an inevitable trend in the development of the orchard flower and fruit management. Based on relevant reports in the past 20 years, the paper discusses the current level of development of mechanized blossom thinning technologies and equipment in orchards from three aspects: mechanism research, machine development, and intelligent upgrading. Firstly, for thinning mechanism research, three directions were investigated: the rope flexible hitting force, thinning agronomic requirements, and the fruit tree growth model between thinning and fruit yields. Secondly, for marketable machine developments, two types of machines were investigated: the hand-held thinner and tractor-mounted thinner. The hand-held thinner is mainly suitable for traditional old orchards with a messy canopy structure, especially in the interior and top of the canopy. The tractor-mounted thinner is mainly suitable for orchards with the same crown structure, such as the hedge type, trunk type, and V-type. Thirdly, for equipment intelligent upgrading, the research of the intelligent detection algorithm for inflorescence on the fruit tree was investigated, for species including the apple, pear, citrus, grape, litchi, mango, and apricot. Finally, combining the advantages and disadvantages of the research, the authors propose thoughts and prospects, which can provide a reference for the design and applications of orchard mechanized blossom thinning.

Accurate navigation is crucial in the construction of intelligent orchards, and the need for vehicle navigation accuracy becomes even more important as production is refined. However, traditional navigation methods based on global navigation satellite system (GNSS) and 2D light detection and ranging (LiDAR) can be unreliable in complex scenarios with little sensory information due to tree canopy occlusion. To solve these issues, this paper proposes a 3D LiDAR-based navigation method for trellis orchards. With the use of 3D LiDAR with a 3D simultaneous localization and mapping (SLAM) algorithm, orchard point cloud information is collected and filtered using the Point Cloud Library (PCL) to extract trellis point clouds as matching targets. In terms of positioning, the real-time position is determined through a reliable method of fusing multiple sensors for positioning, which involves transforming the real-time kinematics (RTK) information into the initial position and doing a normal distribution transformation between the current frame point cloud and the scaffold reference point cloud to match the point cloud position. For path planning, the required vector map is manually planned in the orchard point cloud to specify the path of the roadway, and finally, navigation is achieved through pure path tracking. Field tests have shown that the accuracy of the normal distributions transform (NDT) SLAM method can reach 5 cm in each rank with a coefficient of variation that is less than 2%. Additionally, the navigation system has a high positioning heading accuracy with a deviation within 1° and a standard deviation of less than 0.6° when moving along the path point cloud at a speed of 1.0 m/s in a Y-trellis pear orchard. The lateral positioning deviation was also controlled within 5 cm with a standard deviation of less than 2 cm. This navigation system has a high level of accuracy and can be customized to specific tasks, making it widely applicable in trellis orchards with autonomous navigation pesticide sprayers.