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With China’s urbanization, land circulation has emerged as an avenue for efficient and intensive land use. Embracing technology as a primary driver, the unmanned farm technology model represents a bold attempt at sustainable agriculture development. This review focuses on the frontier and development trajectory of key core technologies, and addresses the significant industrial challenges of insufficient accumulation of data on unmanned farms, unknown interaction mechanisms among the environment, plants, and equipment, and lack of multi-parameter integration and regulation strategies for intelligent equipment. Technologies such as autonomous positioning and navigation, online professional sensor, work obstacle information perception, path planning, decision-making, multi-machine collaboration, autonomous operation, and variable operation technology have been implemented in this study. The research shows that, in future, realizing unmanned farms requires special sensors for agricultural intelligent equipment, precise operational decision control systems, and practically intelligent equipment. At meanwhile, addressing such challenges necessitates a focus on core technologies such as insufficient basic big data accumulation of unmanned farms, modeling of environment-plant-equipment interaction mechanism, and intelligent decision control algorithm. This comprehensive improvement in automation, intelligence, and environmental sustainability of agricultural production marks the future development trajectory of unmanned farms.
In order to solve the problems of high difficulty and low efficiency of manual counting in scientific research of cotton aphid population prediction and control, a cotton aphid image recognition algorithm based on YOLO neural network was proposed and developed into software. First, the images of artificially inoculated cotton aphids were taken by mobile phone for 15 consecutive days, 50 clear images were selected and cut into 6 sub-images, and then the training set and test set were obtained by using LabelImg software for manual labeling. Then, 10 models from the series of YOLOv5 and YOLOv8, whose training parameters were set as the same(batch size was 32, iteration was 100 rounds, initial learning rate was 0.01, and periodic learning rate was 0.01), were selected and trained by using the server of the AutoDL platform. Finally, the trained models were tested, and the YOLOv8l model showed the best overall performance, with mAP50 reaching 0.926. In order to provide users with convenient and easy-to-use man-machine software, the front end of the software was developed by using PYQT5, to realize the functions of reading and counting of cotton aphid pictures, visualizing results and exporting results to Excel. The back end of the software adopted an image processing method of "splitting-detection-merging", which ensuring the efficient detection of YOLO model on small targets. After testing, the software had an average precision of 0.945 for counting dead cotton aphids and live cotton aphids, which was comparable to manual counting and had good practical value. This research may provide an intelligent detection tool for researchers related to cotton aphid control and also provided key operation information for precise operation in scenes such as unmanned farms.
With the increase in labor costs, using intelligent modern equipment to replace humans to complete a series of repetitive, boring, and even high-intensity labor is an important direction in current robot research. Sorting robots, as an important branch of agricultural robots, have good development potential. In order to replace manual, this article used the STM32 microcontroller, OpenMV machine vision module, and ESP32 microcontroller to design an intelligent fruit sorting robot so as to identify and sort fruits with different maturities and sizes. The OpenMV machine vision module collected the color and size information of the fruit, and judged whether the fruit was ripe. The STM32 microcontroller controlled the robot's movement and the robotic arm executed the corresponding sorting program to sort the fruit into the designated container. The ESP32 microcontroller uploaded the sorting data to the cloud. The robot used 3D software for structural design, established 3D models, and conducted simulation analysis during the design process. The simulation results indicated that sorting automation, informatization, and efficiency can be achieved to a certain extent. In the research, multiple and two sorting points were debugged on the physical object, and a total of 24 fruits were sorted. The success rate of fruit sorting reached 87.5%. The research results showed that the sorting robot can achieve precise sorting and achieve a certain degree of reliability and practicality, which can replace human beings to complete the work of precise fruit sorting.
Agricultural machinery autonomous navigation is an effective approach to achieve precision agriculture, and high-precision path tracking control is a critical assurance for the reliability of intelligent agricultural machinery autonomous operations. To improve the adaptability of path tracking algorithms under different path curvatures and initial error conditions, a geometric path tracking combined algorithm based on adaptive switching of two geometric path tracking methods was proposed. The steady-state errors and convergence characteristics of the pure pursuit (PP) and Stanley model in U-shaped path tracking were analyzed, and their optimal geometric path tracking parameters were respectively set at a constant velocity. A path tracking method switching logic was designed based on tracking errors and path curvature as input, and a boustrophedon path was used to test the geometric path tracking combined algorithm. A mobile cart platform was built to verify the effectiveness of the proposed algorithm. The tracking results showed that when the initial error is 2.5 meters, the Stanley model had a faster convergence rate than the PP algorithm. When operating at speeds of 1.0 m/s for straight paths and 0.7 m/s for curved paths, the PP and Stanley model exhibited similar tracking errors for straight segments, but when the curvature changed abruptly, the maximum tracking errors for the PP and Stanley model were 12.0 cm and 11.0 cm respectively. The path tracking combined algorithm effectively reduced the tracking error during curvature changes, with a maximum tracking error of 9.0 cm under the same speed configuration for the reciprocating shuttle path, representing a reduction of 25.0% and 18.2% compared to the PP and Stanley model respectively. The integration of geometric-based path tracking algorithm reduced the computational load and complexity of online optimization for forward distance and gain coefficient, and thus effectively improved the adaptability of path tracking algorithms for agricultural machinery under complex soil condition.
With the continuous development of information and intelligent manufacturing technology, agriculture has entered the era of intelligent and automated production. Unmanned smart farms, as an important direction of modern agricultural development, are facing opportunities and challenges. As one of the key supporting technologies for unmanned farms, unmanned aerial vehicle (UAV) swarm flight control technology provides crucial technical support for agricultural production in fields such as field inspection, livestock management, and irrigation control. This study aimed to analyze the data interaction methods of various components in UAV swarm simulation flight control, establish and optimize a multi-rotor UAV swarm flight simulation environment to meet the application requirements of UAV swarm flight control in unmanned smart farms. In this paper, by analyzing the interaction logic among the ROS system, PX4 flight controller, MAVROS communication module, and Gazebo simulation environment in UAV swarm simulation flight control, we built a multi-rotor UAV swarm flight simulation environment based on the open-source XTDone simulation platform. We also realized model construction and flight control of UAV swarm based on the ROS system, PX4 flight controller, and Gazebo. Furthermore, using a laser radar to collect environmental information, we optimized software algorithms in the ROS distributed framework, achieving simultaneous localization and mapping (SLAM) based on scan matching algorithm and navigation based on optimal path planning algorithm. Theoretical simulation and experimental results demonstrated that the platform had advantages such as open-source, low cost, scalability, and modularity. The constructed simulation environment can achieve UAV swarm flight control, construction of 2D maps in enclosed environments, and autonomous navigation flight. Analysis revealed that the unit flight accuracy of the UAV swarm under this simulation platform was approximately 76%, and the regression model constructed from cumulative flight distance and flight error had an R2 of 0.830 9. The research results demonstrated the feasibility of using UAV formation collaborative flight operations to meet the common agricultural operation needs in unmanned smart farms, demonstrated the application effect and advantages of UAV formation in unmanned smart farms, and provided technical ideas for deep optimization of simulation flight environments and expansion of modern agricultural production application scenarios. It also showed certain reference value and reference value in research and practice in related fields.
China is a major producer and consumer of fruits, with output and area consistently ranking first in the world. However, China's orchards have long faced complex environments, with traditional manual operations facing high costs, labor shortages, and low efficiency. With the establishment of standardized and large-scale orchards, traditional models are no longer suitable for the development of modern orchards. The multi-functional operation technology platform integrates the functions of picking, ditching, fertilization, spray, transportation, etc., effectively improves the efficiency of orchard operations, saves costs, and becomes the key tool for the efficient development of modern forestry and fruit industry. This article introduces the structural composition and working principle of a multifunctional homework platform, mainly introducing the two key parts of the lifting structure and chassis system, and provide an overview of the development trends of standardized orchard operation platforms at home and abroad, from simple mobile platforms to scalable platforms, followed by a series of improvements such as navigation and automatic leveling systems. By comparing the characteristics and advantages of different types of orchard operation platforms, clarify the shortcomings of domestic multi-functional operation platforms, and summarize and analyze the main research focus of multifunctional operation platforms at home and abroad, focusing on automatic leveling technology In terms of general chassis technology, navigation obstacle avoidance technology, lifting technology, chassis passability technology, power system coordination and management technology, and system quality improvement technology, this paper explores the bottlenecks faced by the development of multifunctional work platforms in China, such as insufficient intelligence and informatization, and relatively lagging policy support. Finally, suggestions are proposed to address the development gap between China's homework platforms and foreign homework platforms, such as promoting standardization and standardization of orchard planting, and developing towards intelligence and informatization, in order to provide development ideas for high-quality mechanized operations in China's orchards.
Seed distribution information detection is an essential prerequisite for ensuring the performance of seeders, providing primary data for precise and efficient management of crop growth processes such as seed quality detection, depth determination and fertilization, field weeding, and harvesting. Computer vision technology has been widely applied in multiple fields by extracting meaningful information from image and video data and analyzing and explaining physical phenomena. Based on a systematic analysis of existing literature, this article explored the research progress of computer vision technology in seed distribution information detection from the perspectives of technology application scenarios, hardware devices, software processing algorithms, etc. The study found that computer vision technology still faces challenges in accuracy and stability in seed distribution information detection, and seed distribution information detection requires real-time and large-scale data processing capabilities. Traditional algorithms and devices are difficult to meet these needs, and data fusion and analysis capabilities still need to be improved. Accurate interpretation of seed distribution information requires comprehensive utilization of multiple sensor data and effective data processing and analysis to obtain more comprehensive and accurate results. In response to the above issues, we proposed the following suggestions: Firstly, the widespread application of deep learning and machine learning algorithms is crucial. By utilizing large-scale annotated data for model training, the accuracy and robustness of the algorithm can be improved; Secondly, it is necessary to improve the adaptability of the algorithm and optimize it for different soil conditions, seed types, and light changes to ensure that the algorithm can work effectively in various environments; At the same time, developing multi-sensor data fusion technology can provide more comprehensive and accurate seed distribution information by fusing image data with other sensor data, such as laser scanning and thermal infrared images. This study aimed to provide a reference for the future development of computer vision in seed distribution information detection.
Intelligent plant protection machinery in large fields is an important means to improve pesticide utilization, enhance the quality of agricultural products, and ensure the sustainable development of agriculture. In order to understand the research status of intelligent plant protection technology and clarify the future development direction, this article focused on three main directions of intelligent operation of high-clearance plant protection machinery: prescription application in large-scale farmland, target spraying on a small scale in land parcels, and variable-speed spraying. From the aspects of perception, analysis, decision-making, and control, the article elaborates on the technical principles of prescription map construction, spatial coordinate transformation methods, and prescription recognition that integrate high-precision satellite positioning, the current level of key technologies for target spraying technology routes and weed recognition, and believed that high-precision spraying based on online prescriptions must be the focus of future research. The article carried out a comparative analysis of the advantages and disadvantages of four speed measurement modes for variable-speed spraying and believed that with the widespread application of satellite positioning in agricultural machinery, satellite-based speed measurement will become the main method of high-precision speed measurement due to its greater versatility, convenience, and accuracy. To explore the current development status of variable spraying control, the article summarized two control methods and implementation methods for pressure-controlled variable spraying. From a comprehensive perspective, pipeline cut-off flow control is the main way to achieve variable spraying, and three control algorithms of segmented control, pulse width modulation, and PID control of flow control were analyzed from the perspective of technical principles, implementation processes, and optimized applications. After the discussion, it is believed that PID control based on machine learning will be an important direction to improve traffic control performance. With the continuous development of artificial intelligence technology, plant protection robots that integrate intelligent perception, analysis and decision-making and autonomous operation capabilities will become the mainstream development direction of plant protection machinery in the future.
Aiming to solve the problems of insufficient capacity and high consumption of electric tractors, an optimal control strategy of composite power supply system based on battery plus super capacitor was proposed. By analyzing the topological structure of the electric tractor complex power system and the resistance characteristics of the electric tractor under various operating conditions, a complex power system structure scheme suitable for electric tractor was selected and a test condition suitable for electric tractor was designed. At the same time, the power demand of the electric tractor drive motor under the test condition was determined. According to the charging and discharging characteristics of the main power components of the electric tractor composite power system, the relevant mathematical model was established, and the simulation model of the electric tractor complex power system was built in Simulink. In addition, according to the load demand of the electric tractor under the test condition and the discharge characteristics of the complex power supply system, a fuzzy control strategy of the complex power supply system was formulated. Taking the minimum current at the maximum power discharge of the battery as the optimization goal, the improved genetic algorithm was used to optimize the fuzzy control parameters. The models of the composite power supply system under different conditions such as single power supply and logic threshold control strategy was built for comparative simulation. The results showed that: compared with the single power supply, logic threshold control strategy and fuzzy control strategy, the peak current battery with the improved genetic algorithm can be reduced by 63%, 62.9% and 43.9% respectively, the average current was respectively reduced 24.8%, 3.2% and 8.5%. It is also shown that this strategy is beneficial to reduce the peak current battery and prolong the service life of the battery.
Agricultural robot is one of the hottest topics in the field of agricultural machineries. Domestic and foreign research on robot mobile platforms for greenhouse/farmland/orchard operations (weeding, fertilization, spraying, picking, etc.) has achieved preliminary results, but mobile robotic system for soil sampling is still seldom found in literatures. Undisturbed soil sampling is an important basis for analyzing soil mechanical properties. If the original state of the soil sample cannot be guaranteed, it will be difficult to obtain accurate research results through subsequent laboratory physical and mechanical testing and analysis. To address this issue, we developed a farmland soil collection robot mobile platform with compact structure, strong pass ability, good soil extraction quality, and high soil extraction efficiency, put forward the design scheme of the mechanical system and control system and carried out preliminary field trial research. The main contents of this research are as follows: First, the design of robotic mobile platform was conducted, by determining differential steering mode, fulfilling mechanical design, selecting hardware components, and building control software framework. The wheelbase and track width of the platform were 960 mm and 600 mm, respectively. The power of the in-wheel motor was 1 000 W. The movement of the platform could be controlled through both speed control knob and remote-control handle. Second, an on-board layer soil sampling equipment was developed, which worked in a hydraulic screw-in mode. The main structure parameters of the sampler were determined based on theoretical analysis, which were validated with a finite element analysis software-ANSYS. Third, field tests were also conducted to test the mobility and soil sampling performances of the robot system. The maximum obstacle crossing height and climbing slope of the robot were 80 mm and 35°, respectively. Based on the shear strength testing results of soil samples in depth 0 to 200 mm, we knew that the internal friction angle of the soil samples, which came from the proposed new system, had no significant differences compared to those coming from cutting ring sampling, with a P-value of 0.866 at the confidence level of 0.05. Similarly, for the soil samples in depth 0 to 100 mm and 100 to 200 mm, the variances of soil cohesion from our new system also had no significant differences compared to those from cutting ring sampling, with P-values of 0.145 and 0.717 at the confidence level of 0.05, respectively. The soil extraction efficiency comparison test results showed that the soil extraction device only took 3 to 5 minutes to complete one soil extraction.
Pneumatic centralized seeding technology has the advantages of high seeding efficiency, wide adaptability and low seed damage. In the study, it was found that the type and structural parameters of the seed distributor had a great influence on the seeding uniformity of the pneumatic centralized seeding system. In this study, M-type, T-type and Y-type seed distributors were proposed respectively, and the overall structure and working principle of the pneumatic collection system were expounded. Taking wheat as the research object, the force and motion analysis of the seed particle group in the distributor under the action of airflow was carried out. Based on the CFD-DEM gas-solid coupling method, the simulation models of wheat seeds and distributors were established respectively. Taking the coefficient of variation of the uniformity of each row displacement as the index, the simulation test of the influence of different outlet types on the performance of the distributor was carried out, and the M-type seed distributor was determined as the optimal structure. On this basis, the influence of different outlet pipe inclination angles, top cover cone angles, inlet diameters, fillet radius and airflow velocity on the uniformity of the internal flow field of an M-type seed distributor was studied by single factor experiment, and the significant factors and their horizontal range were determined. The orthogonal test of three factors and three levels was carried out with the factors of outlet pipe inclination angle, top cover cone angle and fillet radius. The optimal structural parameters of the M-type seed distributor were determined by response surface analysis with the variation coefficient of uniformity of each row displacement as the index. The bench and field verification tests were carried out on the experimental farm of Northwest A & F University. The simulation and experimental results show that compared with the T-type and Y-type seed distributors, the M-type seed distributor significantly improves the seeding quality of wheat seeds, and the coefficient of variation of seeding uniformity is reduced by 7.41% and 3.72%, respectively. When the cone angle of the top cover is 117.03° and the angle of the outlet pipe is 59.50°, the corner radius is 69.46 mm, the uniformity of the seed distributor is the best, and the coefficient of variation of the uniformity of each row is 6.05%. The absolute errors between the simulation results of the M-type seed distributor and the bench and field test results were 1.00% and 1.55%, respectively. The research provides reference and technical support for the design of a pneumatic collecting and discharging system.
Research on crop diseases has become a hot topic of the application of artificial intelligence technology in smart agriculture. Existing studies are restricted by the factors such as difficulties in collecting data, high cost of technology implementation, and complex crop disease trends. Plant electronic medical records (PEMRs) formed by Beijing Plant Clinic provides a new idea for the diagnosis and prevention of crop diseases and pests. PEMRs are stored in the form of multi-modal data, containing a wealth of plant information, disease information, and environmental information. Therefore, how to mine PEMRs information and utilize it to assist follow-up research is an urgent problem to be solved. In view of the information representation ability of knowledge map, the mining ability of machine learning and the feature extraction ability of deep learning, structured data is used to construct the knowledge graph of crop diseases and pests, unstructured data and domain knowledge are used for knowledge enhancement according to the characteristics of PEMRs. Further, Neo4j graph database and graph data science (GDS) combined with machine learning algorithm was employed to conduct association mining from three dimensions of popular point discovery, link discovery and similar disease discovery. At the same time, text feature extraction and disease diagnosis were realized by using unstructured text data based on the bidirectional encoder representation from transformers (BERT) with convolutional neural network (CNN) model, and intelligent service was realized by simulating plant doctor. The comprehensive accuracy of 20 common diseases was up to 93.13%. This study can provide theoretical support for timely diagnosis, symptomatic control, scientific medication guide and assistant decision-making of crop diseases and pests, and innovate a new model and new business of social service of agricultural science and technology.
A large part of the world’s greenhouse gases comes from livestock and poultry breeding and manure. A survey report by the Food and Agriculture Organization of the United Nations (FAO) pointed out that animal husbandry is the industry that contributes the most to the greenhouse effect among all industries. Against the backdrop of the current goal of achieving “carbon peak and carbon neutrality”, in order to address the energy waste and environmental pollution caused by large-scale pig farms, building a multi energy complementary system for pig farms with clean energy as the main focus is of great significance in energy conservation, emission reduction, and improving energy efficiency. Inthelight of multi-energy complementation and energy cascade utilization, a multi-energy complementary system in pig farm has been constructed based on the pig farm’s own conditions that the pig farm has abundant biomass energy and solar energy, and it also has large daily electricity consumption and high heat load in winter. Based on the scale overview and load demand of the pig farm, appropriate supporting equipment was selected, the simulation calculation of photovoltaic power generation was conducted using PVsyst software. Taking into account the investment cost, equipment operation and maintenance cost, electric heating load demand, and seasonal demand of the system. Taking into account the system investment cost, equipment operation and maintenance cost, electric heating load demand, and seasonal demand, the final calculation showed that the pig farm can still earn 1.585 7 million yuan based on the industry benchmark return rate of 8%, and the dynamic investment recovery period is approximately 7.3 years. Compared with traditional pig farms purchasing electricity from the grid, it can reduce carbon dioxide emissions by 204.29 tons per year, indicating that the system can bring good economic benefits to pig farms and has significant energy-saving, emission reduction, and green environmental protection advantages. It can provide new business ideas for other farms such as chicken farms and cattle farms.
The upward seeding of garlic cloves has the characteristics of emergence early and strong, which is the common planting agronomy of garlic in China.Based on the parallel robot, a control system of garlic clove’s orientation was designed to improve the upward seeding rate, and to promote the garlic planting mechanization in China. This system mainly included four modules: image acquisition, image preprocessing, garlic clove feature extraction and instruction drive. First, when the garlic cloves were transported to the established position, the visual system received the photo trigger signal emitted by the parallel robot and collected the image immediately. Second, the ideal binary image was obtained by median filtering, binarization, and morphological processing of the image to fill holes and filter out background particles, and the area of the connected domain of the binary image was screened according to the size range of garlic species to obtain the garlic binary image of the right size. Third, the center contour of the image and the curvature of the contour were extracted and calculated, and the part with the maximum curvature was regarded as the bud of the garlic clove and this part was used as the clove orientation, while this orientation would be quantified as the angle value and sent to the robot. Finally, according to the position information, velocity vector information and orientation information of the garlic cloves, the parallel robot controlled the grasper to grasp the garlic clove, and the garlic clove was adjusted to a unified direction and put into the established position, so as to adjust the orientation of the garlic clove. The key parameters of the adjusting system were tested and the multiple regression model of the adjusted success rate was established. The results indicated that: (1)the indicators of adjusted success rate were impacted by weight of garlic clove, conveying speed and grasping period significantly. (2)the order of priority of influencing factors is grasping time, weight of garlic cloves, and conveying speed. (3) the combination of the weight of garlic seed (4.54 g), the conveying speed (0.1 m/s), and the grasping time(2.445 s) led to a relatively high adjusted success rate (97.41%). This study provided a technological guidance for the application of the intelligent garlic clove’s orientation control system, which will be helpful for solving the problem of using an embedded industrial computer to adjust garlic clove’s orientation, and promoting the garlic planting mechanization in China.
The opening of the Metaverse era has gradually changed the ways of production and distribution of human society. The highly virtualized immersive Internet world has become a new stage of the development of the next generation of the Internet. The “food, clothing, housing and transportation” in the Metaverse are also an essential elements of life and important fields for virtual citizens. However, as a new form of integration of a variety of cutting-edge information technologies, the Metaverse is currently less studied and applied in agricultural related fields. This paper analyzes the value of the social attribute and digital asset attribute of virtual food in the Metaverse, discusses the significance and application scenarios of food traceability in the Metaverse, and builds a Metaverse traceability system based on the perception layer, data layer, basic layer, tool layer, and application layer. This system targets operation and maintenance management client, merchant enterprise client and consumer user client, and designs the main functions such as authority management, blockchain management, smart contract management, platform operation and maintenance, food processing management, data collection management, product traceability, claim and planting, as well as the supporting front-end and back-end development technology routes. It describes the working principle of smart contract as the core technology in the process of Metaverse food traceability. A set of design ideas and deployment process of smart contract are proposed. This system is an innovative exploration of cross-border integration between agricultural industry and other new technology scenarios such as blockchain and Metaverse. It is forward-looking and applicable, and provides guidance and reference for future research on the application of Metaverse in agriculture.
Deep learning models are widely used in the field of crop detection and recognition. Their advantage lies in optimizing the model by constructing different functional perception layers, which can automatically extract features from input data and achieve end-to-end learning. However, the unknown data processing process in this model leads to a lack of interpretability, which becomes the main obstacle to the application of deep learning. To overcome the shortcomings of insufficient interpretability in deep learning models, researchers have proposed an interpretability method based on class activation mapping. This article summarizes the research progress of the class activation mapping algorithm Grad-CAM in crop disease classification and detection, crop pest detection and recognition, crop variety classification, target crop detection, and other applications. It explains the advantages of the class activation mapping algorithm in visualizing convolutional neural networks with arbitrary structures, and analyzes the shortcomings of class activation mapping algorithms such as low interpretation precision, unstable gradients, lack of evaluation standards, and single application background. It proposes the development trend of building models with high accuracy and interpretability, construction of new interpretive algorithms, establishing unified evaluation standards for interpretable algorithms, and ensuring the correctness of interpretable algorithms.
In order to solve the problems of grain loss, breakage and impurity in sorghum harvest, the influence of maturity and mechanical harvest rate on the effect of combined harvest was studied. Taking Jinza No.22 sorghum as the research object, the plot experiment scheme was adopted, and three mechanical harvesting rates were set at the milk ripening stage, wax ripening stage and complete ripening stage, respectively, and the mechanical harvesting rates were set at 0.5 m/s, 1.0 m/s and 1.5 m/s, and the indexes such as crushing rate, impurity content, total loss rate and moisture content were measured, so as to obtain the influence rules of mature stage and mechanical harvesting rate on the harvesting effect. The results showed that the maturity stage and mechanical harvest rate had significant effects on the harvest indexes of sorghum, such as impurity content, crushing rate and total loss rate, and the most influential factors on the three harvest indexes were maturity stage. The best results can be achieved when the wax is ripe and the mechanical harvest rate is 1.0 m/s, when the grain moisture content is from 14.756% to 15.746%, the impurity content is 0.14%, the crushing rate is 0.29% and the total loss rate is 5.48%. This study obtained the influence law of mature period and mechanical harvest rate on sorghum harvest effect, which can provide theoretical basis for reasonable selection of mature period and popularization of harvesting technology of sorghum mechanization.
In recent years, the improvement of living standards in rural areas of developing countries and the continuous upgrading of rural industrial economy have led to increasingly serious rural sewage discharge. The rural sewage from production and domestic wastewater, agricultural tailwater, and aquaculture wastewater seriously damages the ecological environment, affecting residents' well-being and economic development. This paper points out the limitations of traditional ecological methods and biological treatment methods, such as long treatment cycles, significant environmental impact, and secondary pollution. Furthermore, it further discusses the advantages of plasma technology in treating rural sewage, including its wide application range, high treatment efficiency, and absence of secondary pollution, filling the gap in the application of plasma technology in agricultural wastewater treatment. Although some reaction mechanisms of plasma are still unclear, and the requirements for equipment are relatively high, which still need to be continuously improved. However, in the future, by combining with other technologies, the efficiency and application range of plasma technology can be further improved, enabling it to play a greater role in optimizing and improving the comprehensive treatment process of rural sewage in developing countries.
To strengthen the country, agriculture must be first strengthened, and only by strengthening agriculture can the country be strong. The report of the 20th National Congress of the Communist Party of China takes “basically realizing agricultural modernization” as one of the overall development goals of China in 2035. Comprehensively promoting rural revitalization and accelerating the construction of agricultural power, are the strategic deployment of the Party Central Committee in order to comprehensively build a modern socialist power. Since the reform and opening up, Nanjing’s agriculture has developed rapidly and its agricultural competitiveness has improved rapidly. However, compared with the developed agricultural areas in China, the agricultural competitiveness is not strong. The number of agricultural employees in Nanjing is only 210 000, with an average annual decrease of 20 000. The per capita cultivated land is 0.025 hectares, which is only 25% of the national average. Brand awareness is not enough, and it suffers from fierce competition in the industry. Based on the diamond model analysis framework, this paper analyzes Nanjing’s agricultural competitiveness from five aspects: production factors, demand conditions, related supporting industries, enterprise strategic structure and industry competition, government, and opportunities. This paper puts forward five strategies to improve the competitiveness of modern agriculture, including talents, science and technology, capital, land, and enterprise competitiveness, to give full play to the advantages of human resources in Nanjing, continuously optimize the environment of human resources, and do a good job in the internal allocation and external introduction of agricultural talents. Relying on innovation carriers such as agricultural high zone and agricultural innovation Park, accelerate the integrated innovation of key technologies in leading agricultural industries such as efficient facility vegetables, economic forest fruits, and agricultural product processing, and promote the “going out” and “bringing in” of Nanjing agriculture; establish a diversified agricultural investment system guided by financial support for agriculture to provide financial support for the development of modern agriculture and comprehensively enhance the competitiveness of agricultural enterprises. Through the promotion of agricultural competitiveness, the agricultural modernization of Nanjing will be accelerated, so as to provide strength to help China build an agricultural power.
Seedling pick-up mechanism play an important role in the transplanter of dry land, which determines the quality, reliability and efficiency of transplanting. It is the key to realize transplanting automation. Based on the study of the current situation of seedling collecting mechanism at home and abroad, this paper introduces several typical seedling collecting mechanisms of transplanting machinery in dry land. They are connecting rod seedling picking mechanism, planetary gear system seedling picking mechanism, pneumatic seedling picking mechanism, ejector rod seedling picking mechanism, clamping seedling picking mechanism and transplanting seedling picking robot. Their advantages and disadvantages are compared from the aspects of seedling picking speed, complicated structure and cost.Automatic seedling collecting mechanism is the most suitable seedling collecting mechanism, while robot seedling collecting mechanism will be the final choice. At present, the seedling picking mechanism is still developing, and it is developing in the direction of high speed, high efficiency and intelligence.