As a critical component of field management, cultivation and weeding significantly influence the yield and quality of crops. Currently the domestic cultivation and weeding operation mainly relies on mechanical working parts, while chemical and biological technologies are usually applied to weeding operation alone. In the context of realizing modern and sustainable agriculture, efficient mechanical weeding components and their supporting intelligent control technology have gradually become the core direction of the industry's development. Relying on the advantages of flat and continuous farmland, European and American modular farms focus on the development of chemical weed control system with precision variable spraying as the core; while Japan, characterized by intensive cultivation, has developed a variety of advanced weeding-cultivating machine under the conditions of small fields, which occupies the technological commanding heights of the international agricultural equipment market. In this paper, the research progress of cultivation and weeding techniques is systematically described from two dimensions of equipment classification and application, and intelligent control technology. The technical characteristics of four types of inter-row and inter-plant operation equipment, namely, pivoting type, oscillating type, fixed rotary type and shovel type, are summarized. At the same time, the application of mainstream technologies such as image processing, deep learning, sensor fusion and satellite navigation in the field of intelligent control is introduced in detail focusing on the two major aspects of plant sensing and identification, path control and decision-making. Based on the results of domestic and international experiments, this paper further discusses the shortcomings of the existing equipment and control technology in terms of weed removal rate, seedling injury rate, durability and adaptability, and thus looks forward to the future of cultivation and weeding technology, which will continue to take high quality and high efficiency as the core objective, with a particular emphasis on the development and fusion of intelligent precision, greening, integration, and bionic material technology.

In response to the practical issues faced in hilly areas, such as limited per capita arable land, scattered plots, rugged terrain, low efficiency of large agricultural machinery, and the occasional inconvenience of using small machinery, this paper designs a sowing device that combines a groove wheel and a sowing disk based on drone technology. The paper presents the design concept and optimization simulation plan for the combined sowing device, using rice seeds as the research subject. A discrete element method (DEM) is employed to model the rice seeds using basic unit particles. The sowing process of the device is simulated using DEM software EDEM, primarily analyzing the sowing disk structure. It is found that the angle of the sowing disk and its rotational speed significantly affect the distribution of rice seeds on the disk, while the number of blades significantly impacts the distribution of rice seeds in the air. To achieve uniform sowing, the study uses DEM to design an orthogonal experiment with three factors at three levels, focusing on the rotational speed of the sowing disk, the angle of the blades, and the number of blades. The simulation results indicate that the optimal seeding disc rotation speed of 850 r/min, deflection angle of 50° , and number of blades of 5 resulted in a uniformity coefficient of variation of 12.99%. Verification tests were conducted in three sets focusing on flight altitude, sowing amount, and flight speed. The results show that the maximum coefficient of variation for actual distribution is 24.25%, while the minimum is 15.42%. The average coefficients of variation for rice distribution corresponding to flight altitude, sowing amount, and flight speed are 17.83%, 20.90%, and 18.55%, respectively, which generally meet the agronomic requirements for rice aerial seeding. Through the research and analysis of the structural parameters, discrete element simulation, and field tests of the groove wheel and sowing disk combination, the paper identifies the optimal parameters for the operational performance of the sowing device. These parameters lay the groundwork for research on aerial seeding and its practical application in hilly areas.

A large amount of research has been conducted on the data base, quantitative models, intelligent decision-making, and digital scenarios of high water efficiency agriculture both domestically and internationally. Multiple crop high water efficiency universal models and platforms have been developed internationally, but this aspect is still blank in China. This article summarizes and analyzes the research status of management decision-making platforms related to agricultural water use in China. It points out the limitations of the current construction of agricultural water management platforms from the aspects of platform functions and data models, and proposes a preliminary plan and overall architecture for building a “Sky-Air-Ground” integrated high water efficiency intelligent control platform. Addressing the issues of weak digital scenarios and low automation levels in irrigation district information management platforms, this study develops modular integration technology for data, models, algorithms, and simulation engines. It also develops a standardized and regulated intelligent decision-making knowledge base for high-efficiency agriculture, aiming to solve key technologies for the digital mapping, intelligent simulation, and construction of specialized digital scenarios for key production factors in high-efficiency agriculture. The goal is to achieve synchronous simulation operation, virtual-real interaction, and iterative optimization with physical irrigation districts, enabling managers to intelligently simulate, refine, and scientifically manage crop irrigation water needs and optimize regional water resource allocation, thereby promoting intelligent management and sustainable development of agriculture.

To address the challenges of inconsistent salt content and low production efficiency in traditional Chinese ham curing processes—issues arising from the reliance on manual expertise and the lack of automated salting equipment—this study developed an automated three-dimensional profiling and variable-rate salting machine. The system integrates machine vision, dynamic variable control, and 3D contouring technology while adhering to traditional craftsmanship. The overall structure and operational principles of the salting machine are elaborated with detailed analysis and design of key components, including the ham transfer mechanism, vision detection system, and automated salting mechanism. The machine achieves full-process automation in salt quantity calculation, non-uniform path planning, and adaptive salt spraying, effectively resolving the instability caused by irregular ham morphology and significant weight variations. A machine vision-based 3D feature recognition method is proposed, utilizing high-precision cameras to capture morphological characteristics and construct a “three-pick positions” database for key salting regions. Combined with deep learning algorithms, this method generates differentiated salting paths that align with the traditional requirement of “more salt at three-pick positions, less salt in intermuscular areas”. A dynamic weighing-precision salting collaborative system is designed, incorporating gravity sensing and a graded vibratory feeder controlled via PID algorithms to dynamically adjust vibration amplitude, ensuring salt dispensing accuracy within ±5 g. A three-axis linear-module-driven flexible robotic arm integrates real-time trajectory correction, achieving 1 mm positioning accuracy for the salting nozzle. Coupled with a variable-aperture nozzle that dynamically adjusts salting intensity and travel speed (0-0.3 m/s), the system significantly enhances salt layer uniformity on complex curved ham surfaces. Experimental results demonstrate that the machine achieves a production rate of 123 hams/hour within a ham carcass weight range of 6-15 kg, with an average salt application error rate of 7.42%, meeting design requirements in adaptability and precision. This research realizes full automation of the ham salting process and provides critical technical support for the standardization and intelligent transformation of traditional meat processing industries.

This study selected 60 accessions of Camellia sinensis var. assamica from 7 regions in Hainan Province as research materials. It systematically examined 28 phenotypic traits (comprising 19 qualitative traits and 9 quantitative traits) and comprehensively assessed their morphological variation and genetic diversity through diversity analysis, correlation analysis, principal component analysis, and K-means clustering. The findings indicated that the accessions of Camellia sinensis var. assamica in Hainan exhibited rich phenotypic variation with an average diversity index of 4.13 for the 9 quantitative traits and 4.29 for the 19 qualitative traits. Among these traits, the variations in bud length, the length of one bud with two leaves, and leaf base were especially notable. There were intricate correlations among the traits. Leaf length was significantly and positively correlated with the number of leaf veins and bud-leaf trichomes, whereas leaf shape was negatively correlated with leaf tip, leaf tooth density, and sharpness. The cumulative variance contribution rate of the first 9 principal components extracted by principal component analysis was 76.77%, which respectively represented key trait combinations such as leaf morphology, tree structure, and fruit shape. The clustering analysis classified the 60 accessions into 5 phenotypic groups, and the characteristics of each group differed significantly, which were associated with ecological adaptability. The phenotypic database established in this study provided an important morphological foundation for the conservation of Camellia sinensis var. assamica germplasm resources in Hainan, variety breeding, and mechanized production in tea gardens, such as the optimization of pruning equipment and the design of conveyor belts to prevent damage.

With the continuous advancement of facility agriculture, the application of solar greenhouses in northern China is becoming increasingly popular, and double-film solar greenhouses, as an effective agricultural facility, have significantly improved the crop growth environment. This study focuses on double-layered film solar greenhouses in cold and arid northern regions under frigid weather conditions. January 21st and 22nd, the two days with the lowest average temperatures in January 2024, were taken as the research object, and the thermal insulation performance of the greenhouse and the temporal and spatial variation of the internal thermal and humid environment under severe cold weather conditions were discussed through the analysis of its internal thermal and humid environment. The study shows that the double-film solar greenhouse can maintain a relatively stable indoor temperature and humidity in the severe cold weather after heavy snowfall, which greatly improves the growth environment of winter crops. The internal temperature of the double-film solar greenhouse showed three stages, including the heating stage accompanied by the rising sun, the rapid cooling stage after noon, and the slow cooling stage at night. The warming phase accompanied by the rise of the sun gradually decreased from east to west along the east-west length direction, from south to north along the north-south span direction, and gradually decreased from high to low along the vertical height direction. In the slow cooling stage after noon, the temperature gradually decreased from west to east along the east-west length direction, from north to south along the north-south span direction, and gradually decreased from low to high along the vertical height direction. Relative humidity also showed a three-stage periodic variation law, and the specific change was negatively correlated with the temperature variation law. The spatial distribution of soil temperature decreasing from the middle to the east and west along the length direction also showed a decreasing spatial distribution. The span direction decreased from the middle to the north and south, and the temperature of the north wall area was higher than that of the southern front foot. In the vertical direction, the temperature fluctuation decreased with the increase of depth，indicating that the underlying soil had stronger thermal stability. The results show that the double-film solar greenhouse can also keep the planting area not lower than 3.61 ℃ in extreme weather, and the stable cultivation conditions of the ambient temperature of the planting area higher than 5 ℃ in winter. The results of this study further reveal the characteristics of the hot and humid environment of the double-film solar greenhouse under severe cold weather conditions, and provide a scientific basis and data support for the precise regulation of environmental factors in the greenhouse.

China's lotus root industry holds a dominant position in the international market. Given that the rigid chassis of traditional agricultural machinery fails to meet the requirements for stability, trafficability, and adaptability, this study designed a variable triangular track chassis to improve the passability and stability of the harvester in soft and high water content lotus root fields. By analyzing the working conditions of the lotus root field and the force characteristics of the chassis, key parameters such as ground contact pressure of the track, traction force, and power were determined. The overall structure was optimized by using modular design and lightweight aluminum alloy material (ZL303). A three-dimensional model was established using SolidWorks, and finite element analysis was conducted on the drive wheel, variable device, and load-bearing components based on ANSYS Workbench, including stress distribution, fatigue safety margin, and modal characteristics. The simulation results show that when the total mass is 1.1 t and the maximum speed is 15 km/h, the maximum stress of the drive wheel is 44.05 MPa, the fatigue safety factor of key components is all higher than 2.4, and the lowest natural frequency is 323.3 Hz, effectively avoiding resonance with the vibration frequency (50-300 Hz) of auxiliary equipment. In addition, by hydraulically adjusting the track tension (400-2 000 N) and the variable track gauge mechanism, the chassis has a vertical obstacle crossing ability of 300 mm and water depth adaptability of 0.4 m. The whole machine adopts redundant design and modular maintenance system, effectively solving problems such as insufficient system integration, low energy efficiency, and high manufacturing costs in existing technologies. The chassis achieves a load-to-weight ratio of 54.5% when fully loaded, with a center of gravity height controlled below 420 mm. The overturning moment safety factor meets the requirements of GB/T19930-2005 standard, and it reserves 3 sets of power output interfaces and 4 standardized mounting bases, supporting rapid replacement of multi-functional agricultural operation devices, providing a reliable platform for the development of intelligent agricultural machinery equipment.

The development of navigation technologies for autonomous mobile equipment is pivotal to the intelligent upgrading of facility agriculture, with its core challenge being how to achieve safe, efficient, and precise movement within greenhouse environments. Through an in-depth analysis of the latest research trends, this study aims to comprehensively elucidate the evolution of these navigation technologies and discuss their future directions. We begin by reviewing the state of the art, with a focus on comparing the advantages and disadvantages of different navigation methods, including guide-rail, path-tracking, and multi-source data fusion navigation based on simultaneous localization and mapping (SLAM) technology. Furthermore, we explore recent advances in SLAM-based multi-source data fusion navigation for real-time localization, mapping, and path planning. Relevant research indicates that while traditional guide-rail and path-tracking methods are simple to implement, they suffer from fixed routes, high installation costs, and limited operational range. In contrast, SLAM-based multi-source data fusion navigation offers greater flexibility and autonomy, enabling precise localization, intelligent obstacle avoidance, and adaptive path planning, thus holding broad application prospects in facility agriculture. As SLAM technology continues to mature, equipment employing this navigation approach will see even wider adoption. This study also addresses practical challenges and limitations, such as computational demands and algorithm optimization. Finally, we propose several development prospects for China's facility agriculture: adopting multi-source data fusion navigation to achieve high-precision localization and mapping; integrating artificial intelligence, cloud computing, and edge computing to enhance the equipment's perception and understanding of complex environments; combining global and local path planning to enable autonomous decision-making; implementing intelligent obstacle avoidance to ensure operational safety and reliability; and investigating multi-vehicle and human-vehicle collaboration for efficient multi-task coordination. This research aims to provide valuable insights and references for the intelligent development of autonomous mobile equipment in Chinese facility agriculture.

In China, walnut kernel grading predominantly relies on mechanical and manual methods, which suffer from high economic costs, low efficiency, and poor accuracy, severely limiting the standardization and economic value of walnut kernel products. To achieve automated and intelligent walnut kernel grading, this study proposes a deep learning-based intelligent grading method. An image acquisition device was utilized to capture walnut kernel images across six grades under three distinct background colors: pure white, light green, and conveyor belt. The Python OpenCV library was employed for data augmentation to expand the dataset, resulting in a VOC format dataset of 12 246 images, with training, testing, and validation sets allocated in an 8：1：1 ratio. To select the most suitable model for walnut kernel grading, two models were constructed based on the YOLOv5 and YOLOv7 object detection networks, respectively. Both models were trained using pre-trained weights, achieving average grading accuracies of 87.83% and 91.16% for YOLOv5 and YOLOv7, respectively. During validation, YOLOv7 exhibited mispredictions, prompting the integration of two attention mechanisms, ECANet and CBAM, to refine the model. The improved models demonstrated enhanced performance, with the YOLOv7+CBAM model achieving the highest average accuracy (94.5%) and F1-score (90.2%). Compared to the original YOLOv7, the upgraded model increased average accuracy and F1-score by 3.34% and 5.9%, respectively, while adding only 2ms to inference time. To validate practical feasibility, a recognition system platform was developed for walnut kernel classification testing. Four experimental groups were designed, each containing 120 walnut kernels (20 per grade). The YOLOv7+CBAM model achieved an average recognition accuracy of 91.63%, demonstrating robust grading capabilities for walnut kernel appearance quality. This study provides a reference for intelligent walnut kernel grading systems.

Cyperus esculentus is an emerging green economic crop that integrates grain, oil, forage, and feed production. It is drought-resistant, water-saving, high-yielding, and stable in production, making it highly valuable for ecological protection, increasing farmers' income, and poverty alleviation efforts. Although Cyperus esculentus has many advantages, its industrial development is facing a key bottleneck-the problem of harvesting. Due to the lack of advanced and applicable harvesting equipment, the planting scale of Cyperus esculentus grows slowly, and the level of mechanized harvesting is relatively low. This has become a major obstacle restricting the high-quality development of the entire industrial chain. This problem not only affects the economic benefits of Cyperus esculentus, but also limits its application in the process of agricultural modernization. This paper starts from the research published by domestic and international scholars in recent years on tiger nut harvesting technology. Focusing on the cultivation characteristics of Cyperus esculentus and the requirements for combined harvesting operations, it analyzes the research progress and existing issues in aspects such as digging technologies, conveying technologies, and screening technologies of harvesting equipment. For instance, there are issues like low operation efficiency, high loss rate, and poor adaptability, which seriously affect the mechanized harvesting effect of Cyperus esculentus. Furthermore, from the angles of structural design, optimization and improvement, and applicability, this paper extracts the research trends and key issues in the critical technological aspects of tiger nut harvesting, including the high efficiency and low-loss of the digging device, the stability and reliability of the transporting system, and the cleanliness and efficiency of the screening technology, etc. The development direction of these technologies can provide reference for further optimizing and improving the existing harvesting technology and creating new types of harvesting equipment, thereby promoting the wide application of mechanized harvesting in the production of Cyperus esculentus. This not only helps to expand the planting scale of Cyperus esculentus, but also improves the production efficiency and income level of farmers, providing a new economic growth point for industrial poverty alleviation.