Aiming at the situation of mountain planting of blueberry and small operation area of some farmers in China, a portable blueberry picking machine was designed with electric drill as the driving part. By analyzing the mechanical structure and working principle of the machine, the kinematic equation and dynamic equation of the picker are obtained, and the mechanical characteristics of the picker are simulated in Adams. The simulation results show that the slide displacement of the picking device, the drive torque of the drill and the power curve of the system change in the shape of “W” when the machine travel speed ratio coefficient increases. When the travel speed ratio coefficient is 1.3, the driving torque and power of the system are maximum, which are 2.7 N ⋅ m and 34 W respectively, which are less than the rating value of the electric drill and meet the design requirements. The orthogonal experiment of blueberry picking was carried out with a self‑made portable picker. The orthogonal test results showed that the speed of the hand drill had the greatest influence on the picking efficiency, the picking rate of ripe fruit and the shedding rate of immature fruit, and the stroke speed ratio coefficient had the greatest influence on the fruit damage rate. When the stroke speed ratio coefficient of the picking device was 1.3, the rotating speed was 150 r/min, and the torque was 35 N ⋅ m, the picking efficiency and picking quality of the machine were the best. The ratio of mechanical and human mining efficiency is 5.9. This provides theoretical reference and technical support for the design and development of domestic small blueberry picker.

This article summarizes the basic connotation of high‑quality development of agricultural mechanization in China and the characteristics of “four highs and one good” (i.e. the level of agricultural machinery equipment, the level of agricultural machinery operation, the level of agricultural machinery service, and the level of agricultural machinery management have reached a high standard, and agricultural mechanization is more environmentally friendly). It summarizes and analyzes the four internal factors that affect the high‑quality development of agricultural mechanization (including three market entities: agricultural machinery manufacturing and circulation enterprises, agricultural machinery households and service organizations, farmers and family farms, and one development entity: people's governments and public institutions at all levels) and five external factors that constrain it (i.e. machinery and equipment, farmland conditions, agricultural requirements, economic level, and social environment)， and comprehensively proposes the realization of promoting high‑quality development of agricultural mechanization in China. Path and policy recommendations for the 15th Five Year Plan Provide theoretical support and decision‑making reference for the management of agricultural mechanization industry during the period.

To address the issues of low efficiency, easy false detection, missed detection and limited accuracy in pest detection based on machine vision in agricultural fields, a deep learning algorithm for pest recognition named YOLOv8—ECSI, based on an improved YOLOv8, was proposed to enhance detection performance for small, dense and similar‑featured common pests. Firstly, EfficientNetwas used to replace the original backbone network, achieving model lightweighting without significantly affecting detection accuracy. Secondly, the original upsampling module was replaced with a CARAFE upsampling module to reduce the loss of detail information. Additionally, the small object detection layer incorporated spatial‑channel reorganization convolution (SCConv) to enhance feature representation capabilities. The original loss function was replaced with the Inner—CIoU loss function to improve small object detection capabilities. A dataset was established by using the large public dataset IP102 and web queries. Experiments demonstrated that the improved model achieved an average precision of 94.6%, with a reduction of 33.6% in the number of parameters and a reduction of 23.5% in computational load. Compared with models such as YOLOv5, YOLOv7, and SSD, the improved model exhibited superior detection performance and faster speed.

In order to optimize the parameter combination of UAV spraying operation on peach trees, a three-factor and three-level orthogonal experiment was set up with a double-rotor agricultural UAV as the operating platform. The influence rules and degree of operation height, operation speed and spraying flow rate on the deposition density, uniformity and penetration of droplets were studied through the test results and range analysis. The results showed that the optimal deposition density of droplets was on average 34.03 particles/cm2 with operation height 3.5 m, operation speed 2.5 m/s and spraying flow rate 72 L/hm2, and it also had good deposition uniformity and penetration. The optimal factor levels of deposition density for the upper layer of peach trees were H=3.5 m, V=2.5 m/s, T=108 L/hm2, and H=4.0 m, V=2.0 m/s, T=72 L/hm2 for uniformity of deposition distribution. The optimal factor levels of deposition density for the middle and lower layers were H=4.0 m, V=2.5 m/s, and T=72 L/hm2, while the optimal factor for uniformity of deposition distribution in the middle layer were H=4.0 m, V=1.5 m/s, T=72 L/hm2, and in the lower layer were H=3.5 m, V=2.5 m/s, T=90 L/hm2. The optimal factor levels for deposition penetration were H=3.5 m, V=2.0 m/s, T=108 L/hm2. The primary and secondary order that affected the deposition effect of droplets shows different results in the upper, middle and lower height layers of the tree crown. Through comprehensive comparison, it was found that the operation height has the greatest impact on droplets deposition density and uniformity, followed by operation speed, and the impact of the spraying flow rate is the smallest. The main and secondary factors affecting the penetration of droplets deposition density are spray flow rate, operation height, and operation speed. The experimental results can provide a theoretical basis for the green flight prevention application and development of agricultural UAV in the fruit tree industry.

As an important support for promoting rural revitalization strategy in China, smart agriculture organically links digital agriculture and precision agriculture into a whole, and deeply integrates modern communication technology and intelligent equipment. It is the development trend of modern agriculture. Intelligent equipment represented by unmanned aerial vehicles (UAVs) is an important carrier and content of smart agricultural production applications. This paper systematically elucidates the basic overview and platform composition of UAV technology, summarizes the integration status and application advantages of UAV technology in smart agriculture scenarios such as crop irrigation spraying, crop phenotype monitoring, drone pollination aerial mapping and three-dimensional modeling, and it also presents an outlook into the further development and applications of drone technology in smart agriculture, including enhancing endurance, autonomous obstacle avoidance and system intelligence, building a remote sensing multi-source database based on provincial-municipal-county administrative divisions, optimizing path planning algorithms for multi-UAV collaborative operations, and establishing a sound industry regulatory framework and operating standards. In view of the current problems of insufficient UAV performance, unstable processing of large amounts of operation data, and imperfect technical standards, this paper proposes suggestions such as accelerating the enhancement of UAV comprehensive performance, promoting multi-source information fusion and system standardization construction, etc., so as to provide a theoretical basis for the precise application of UAVs in smart agriculture and promote the further development of drone technology in smart agriculture.

Agricultural low‑altitude economy, as an emerging economic form, is driving the transformation of traditional agricultural industrial systems. Agricultural drones have significantly contributed to enhancing agricultural productivity, promoting green development, and ensuring food security serving as the core carriers of this economy. This paper systematically reviews current applications of agricultural drones in scenarios including ‌remote sensing and mapping, plant protection spraying, and seeding‌, while extending the discussion to their expanded applications in ‌inspection and monitoring, disaster response, ecological restoration, bait dispensing, pollination and seed production, harvesting and pruning, as well as cargo transportation‌. Building on this analysis, the paper examines key common technologies within the agricultural low‑altitude technology system‑‌covering energy supply, communication networks, environmental perception, and decision‑‌making control‌‑‌summarizes limitations in current technological applications, and outlines future development trends.

The spatial‑temporal evolution of the county's grain production pattern is the factual basis for formulating grain industry policies. In addition, exploring the actual role of total agricultural machinery power is also the meaning of promoting an efficient grain production system. The present study utilizes the geographical analysis tool ArcGIS to visually depict the changing trends of China's county‑level grain production patterns, focusing on the panel data of grain production in 2 396 counties in China from 2000 to 2022. An economic‑geographical weight matrix is constructed, and spatial econometric methods are used to systematically assess the driving effect of total agricultural machinery power on changes in grain production patterns. The research indicates that China's grain production pattern has undergone significant expansion towards the east, accompanied by northward movement and extension to the northeast and northwest, signifying a notable shift in the focus of grain production. High‑value grain production areas are identified within the primary producing regions, where output value stabilizes after doubling and serves as a catalyst for the growth of surrounding grain production. The initial disparity in grain production value between the primary production areas and the balance area is substantial, but this gap gradually narrows over time, eventually converging. At the county level, an initial negative correlation between grain production and output value is observed, which subsequently transitions into a significant positive correlation. The total power of agricultural machinery has been shown to have a positive contribution to grain production, with differentiated direct and indirect effects in different regions. For every 1% increase in the total power of agricultural machinery, the average direct and indirect effects across the country are approximately 0.085 7% and 0.242 3% respectively. In major production areas, the direct and indirect effects are approximately 0.104 5% and 1.899 2% respectively. In balanced areas, the direct and indirect effects are approximately 0.14% and 0.399 9% respectively. Considering these findings, it is recommended that long‑term agricultural development planning be tailored to local conditions. The layout of investment in agricultural machinery should be optimized, with the development and promotion of agricultural machinery suitable for local conditions being a priority. Furthermore, the strength of agricultural machinery subsidies should be increased. The establishment of a platform for leasing and sharing agricultural machinery would improve the convenience and cost‑effectiveness of agricultural machinery use.

Chinese forest fruit and vegetable output ranks among the top in the world, in order to achieve cost reduction and efficiency increase in fruit and vegetable harvesting, it is an inevitable trend to promote mechanized and automated harvesting operations. As a direct actuator of the picking function, the performance level of the end effector has a direct impact on the efficiency and quality of fruit and vegetable harvesting. By introducing the structure of the end-effector of agricultural and forestry picking robots at home and abroad and the application of intelligent control methods in the end-effector, the current research status of the end-effector is analyzed. The existing problems is summarized as follows: there is a lack of design and test for unstructured environments, the picking process is easy to damage the surface of fruits and vegetables, the picking cost is high. In response to the current existing challenges, recommendations are put forward from aspects such as the integration of forest machine and forest art, the application of advanced intelligent control technology, multi-body collaborative picking operations, and enhanced versatility and practicality of the equipment, with the aim of providing references for the development of end-effector for agricultural and forestry picking robots.

In order to study the effects of different straw returning methods on soil environmental factors and bacterial community structure under rice‑wheat rotation system, three treatments were set up as follows: straw removal + traditional rotary tillage (RTNSR), straw returning + traditional rotary tillage (RTSR), straw returning + vertical deep rotary tillage (VTSR). The changes of soil organic carbon components and microbial community structure under different tillage modes were studied, and the molecular ecological network and ecological module of bacterial community were constructed to explore the correlation between soil organic carbon components and bacterial community and network characteristics. The results showed that compared with treatment RTNSR, soil alkali‑hydrolyzable nitrogen content was significantly increased in both treatment RTSR and VTSR, which were 93.71 mg/kg and 96.47 mg/kg, respectively, and soil available phosphorus content was increased by about 3.42% and 21.20%, respectively, the soil microbial biomass carbon and nitrogen contents in treatment VTSR were 211.76 mg/kg and 29.37 mg/kg, respectively, which were significantly increased by about 17.95%, 55.72%, the soil microbial biomass carbon was significantly increased by about 27.51% and microbial biomass nitrogen content was 27.69 mg/kg in treatment RTSR, which was significantly increased by 46.81%, soil total organic carbon, organic carbon mineralization, and heavy organic carbon contents in treatment VTSR increased by about 1.32%, 17.45% and 10.18% as well. Proteobacteria, Acidobacteriota and Gemmatimonadota were the main dominant phyla of soil bacterial community. Acidobacteriota, Chloroflexi, Proteobacteria and Gemmatimonadota as the module key species phylum were found in the soil bacterial community co‑occurrence network, and environmental factors such as pH, total phosphorus, total potassium, and particulate organic carbon significantly drove the changes of these keystone species in the community. In summary, straw returning+vertically rotary sub‑soiling tillage could regulate the soil fertility level, change the contents of soil organic carbon components, and influence the interactions between soil bacterial communities.

The southwestern region of China is characterized by complex hilly and mountainous terrain, fragmented arable land, steep slopes, and small plots, all of which, combined with variable climatic conditions, severely hinder the development of mechanized corn harvesting. Currently, the region's mechanized harvesting rate for corn remains significantly below the national average, relying primarily on small‑scale harvesting machinery that suffers from poor adaptability, low operational efficiency, and high crop loss rates. Although various adaptive harvesting machines have been developed both domestically and internationally, most have faced persistent challenges such as low levels of intelligence, unstable performance, and difficulties in large‑scale adoption. To address these limitations, future development was recommended to focus on the creation of small‑scale, intelligent mechanical equipment, supported by favorable policies and active market promotion to collectively enhance the overall level of mechanized corn harvesting in the region. Moreover, the integration of intelligent and unmanned technologies presents promising opportunities to boost harvesting efficiency while minimizing environmental impact. These insights provide both a theoretical foundation and practical technical recommendations for guiding the future direction of mechanized corn harvesting in China's southwestern hilly regions.

In order to enable a mechanized cassava planting model with large ridges and double rows, a ridge‑type wide‑narrow row cassava planter was developed. This planter integrated multiple operations including ditching, fertilizing, cassava stalk cutting, seed stem discharging, and soil covering. The key components of the prototype were designed, and the working parameters for the fertilizing system, roller‑type stalk cutting mechanism, and chute‑type seed stem discharging system were established. In addition, the operation process of the cassava stalk cutting and seed stem arranging device was analyzed to determine the conditions necessary to achieve the required soil covering thickness for the cassava seed stems. Field test results demonstrated that, at an operational speed of 0.5 m/s to 0.9 m/s, the qualified rate of seed sections reached ≥ 98.3%, the missed seeding rate was ≤ 0.4%, and the exposed seed rate was 0%. The fertilizer coverage rate was 100%, with a fertilization amount deviation of only 3.2%. The qualified rate of seed stem depth reached 97.6%, and the productivity ranged between 0.4 hm2/h and 0.56 hm2/h. All test indicators met industry standards, and the test results confirmed that the planter fulfilled both the design expectations and practical operational requirements.

Aiming at the problem that the rice unmanned aerial vehicle (UAV) seeding operation seeds are not in a row, which is not conducive to the later field management, the pelleted seeds have poor cavitation ability and high cost of seed coating treatment, the research on high efficient strip direct seeding technology of rice UAV with single seed box and multiple rows of seed tubes was carried out. A kind of multi‑row furrow direct seeding method with lateral seed filling, soft brushing and seed cleaning without seed conservation was proposed. The correlation between the structural parameters of the seeding wheel and the amount of seeds was analyzed, and the structural parameters of the seeding wheel were determined. Under the premise of determining the length of the seeding pipe through the rotor airflow simulation, the kinetic model of the rice seed in the seeding pipe was established, the structural parameters of the seeding pipe were optimized, the influence of the rotating speed of the seeding row wheel, the height of the seeding row and the airflow of the rotor on the diffusion diameter was studied to verify the correctness of the theoretical calculation. Through the bench test, it was verified that the seeding quantity of the seed discharge device met the seeding quantity demand, the correlation between the diameter of the seeding discharge wheel, the width of the slot, the depth of the slot and the consistent variation coefficient of the displacement of each row, and the stable variation coefficient of the total displacement was analyzed, and the structural parameters of the seeding discharge wheel were determined. The field test results showed that when the flying height was 1 m, the flying speed was 3 m/s, the striping index was 22.7%, which met the requirements of the unmanned aerial vehicle in strip broadcasting operation and achieved uniform striping. The study provides theoretical support and experimental basis for the design of rice strip direct seeding machinery and the determination of reasonable working parameters and has reference significance for the mechanization of rice planting.

To investigate the differentiated impacts of high‑standard farmland construction on different sources of machinery and different levels of agricultural mechanization for grain production, this paper constructs a theoretical framework for analyzing the impact of well‑facilitated farmland construction on agricultural mechanization, and then uses data collected in 2020 and 2022 from the China rural revitalization comprehensive survey, to quantitatively analyze the impact mechanism and policy heterogeneity of the impact of well‑facilitated farmland construction on agricultural mechanization. It shows that: well‑facilitated farmland construction has increased the comprehensive mechanization rate of the three main grain crops by 5%. From the perspective of different machinery sources, well‑facilitated farmland construction has increased the proportion of agricultural machinery service operation for the three major grain crops (especially wheat and corn), but reduced the proportion of self‑operated machinery for wheat. Mechanism analysis results show that the farmland consolidation and field road projects under well‑facilitated farmland construction directly promote agricultural mechanization and enhance mechanization levels by increasing the scale of farmland leases, but the policy effect is not achieved through the transmission path of promoting off‑farm employment. Policy heterogeneity analysis indicates that well‑facilitated farmland construction in areas with steeper topographic slopes can more effectively increases the proportion of self‑operated machinery for grain production. In villages located further from township governments and those with stable land tenure, the construction of well‑facilitated farmland can increase both the proportion of mechanized work and the proportion of mechanized services in grain production.

To address the high risk and labor intensity associated with coconut harvesting, a wheel-based tree-climbing harvesting robot was developed. The rationality and functionality of the mechanical design were verified through Adams software simulations, which modeled both vertical climbing and circular motion. Additionally, the workspace of the robotic arm was visualized to ensure it met the operational requirements for coconut harvesting. The results of the picking test showed that within a voltage range of 12 V and below, the robot's climbing speed exhibited a linear relationship with voltage, and the climbing process remained stable and met performance standards. During picking tests conducted within the voltage range of 15-24 V, the average shear time per coconut was 2.8 s, with an average picking success rate of 81.5%. Moreover, the picking time decreased progressively as voltage increased. This research provides both a theoretical and experimental validation for the design of coconut harvesting machinery. It offers practical guidance for the future development and application of coconut harvesting robots.

In order to investigate whether the physical and mechanical properties of potato tubers varied with different compression directions and compression parts, an experimental study was conducted, providing a theoretical basis for the design of key mechanical components used in potato harvesting, sorting and transportation. This research also supported the development of simulation models for analyzing tuber behavior under mechanical stress. Qingshu No.9 potatoes at harvest maturity were selected as the research object. Using the drainage method, their average density was determined to be 1.04×103 kg/m3, while the drying method indicated a moisture content of 79%. An electronic universal testing machine was employed to assess the compressive mechanical properties of the potato tubers in both axial and radial directions, as well as across different structural regions, namely the surface, central area, and the entire tuber body. Tests were performed at loading speeds of 10 mm/min, 20 mm/min and 30 mm/min. The elastic modulus of the tubers was measured as 3.57 MPa (axial), 3.79 MPa (radial), 3.90 MPa (surface), 3.79 MPa (central), and 3.91 MPa (whole tuber). Corresponding shear modulus values were 1.17 MPa, 1.22 MPa, 1.25 MPa, 1.18 MPa and 1.21 MPa, while Poisson's ratios were calculated as 0.53, 0.56, 0.56, 0.60 and 0.62, respectively. Statistical analysis results showed that the p‑values for samples tested in the axial and radial directions were 0.279 2，0.203 5 and 0.538 4, respectively, while those for the surface, central, and whole body were 0.830 3, 0.068 9 and 0.684 6, respectively. As all values exceeded the significance threshold (p > 0.05), the differences were not statistically significant. Therefore, the results indicated that the mechanical properties of potato tubers did not significantly differ by direction or position. Hence, for engineering and simulation purposes, potato tubers could be reasonably modeled as isotropic and homogeneous materials.

By exploring the impact of digital rural on agricultural economic resilience, it points out the direction for promoting the modernization of agriculture and rural areas. Based on the panel data of 30 provincial regions in China from 2011 to 2021, an indicator system for digital villages, agricultural economic resilience and urban‑rural integration was constructed, models such as mediating effect, threshold effect and spatial Durbin were used to analyze the path and spatial spillover effect of digital village on the resilience of agricultural economy. The research results show that digital rural have significantly improved the resilience of the agricultural economy, digital rural can further enhance the resilience of the agricultural economy by promoting urban‑rural integration, and this facilitation has a single threshold effect. Due to the differences in regions and agricultural production functions, the impact of digital villages on agricultural economic resilience is different. Further analysis reveals that the impact of digital rural on the resilience of agricultural economy has a positive spatial spillover effect. Based on this, China should strengthen the construction of digital villages, establish an urban‑rural integration development mechanism, enhance the resilience of the agricultural economy, and accelerate the realization of agricultural modernization.

Targeting the large‑scale production and breeding of fattening pigs, traditional pig weighing methods have problems such as low automation level, low efficiency, time‑consuming and labor‑intensive, and easy stress on pigs. A non‑contact fattening pig population weight estimation method based on instance segmentation and machine learning is proposed. Two different instance segmentation algorithms, Mask R—CNN and Mask2former, were used to obtain the mask contours of pigs, and the results were compared. The segmentation accuracy of Mask R—CNN and Mask2former were 93.86% and 98.98%, respectively. Finally, the Mask2former instance segmentation model was selected. Combining the image information of the pig segmentation mask, the relevant feature parameters of the mask image were extracted as the data input for the model. Different algorithms were used to construct multiple weight estimation models for comparison. The random forest estimation model is found to have the best performance, with a coefficient of determination R2 of 0.94, an average absolute error of 7.92 kg, and an average relative error of 2.58%. The experiment demonstrates that the non‑contact weight estimation method for fattening pig populations based on instance segmentation and machine learning can effectively predict body weight, providing technical and theoretical support for achieving automatic weighing of pig populations.