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To address the lack of specialized machinery for the mechanical weeding of SCSCP in the Huang Huai Hai region, this study designs a mechanized inter-row weeding machine for SCSCP. The machine features a reciprocating weeding shovel and an adaptive contouring mechanism for cultivation and soil loosening. This paper details the machine's principles by analyzing the geometric relationship and mechanical model between the corresponding profiling quantities, which determine the relevant parameters for adaptive contouring to ensure stable operation on undulating ground. Furthermore, by optimizing the design of the weeding shovel's reciprocating motion mechanism, combining EDEM simulation with the weeding shovel–soil interaction, it has been determined that, at various PTO shaft speeds, the optimal weeding efficacy is achieved with a blade-type weeding shovel structure when operating at a forward speed of 3.5 km/h. Field experiments were conducted with different PTO shaft speeds and weeding depths, using weeding and seedling injury rates as performance indicators. The results showed that, based on the optimal speed, the PTO shaft speed is 760 r/min, the operating depth is 3–5 cm, and the average row weeding rate is 90.4%. The average soybean and corn seedling injury rate is 3.4% and 4.2%, meeting the technical requirements for mechanical weeding. [ABSTRACT FROM AUTHOR]

Agricultural tractors are an essential agricultural power source. Therefore, the scientific literature tests have described agricultural tractors' evolution over time and determined future trends. This paper uses bibliometric tools to assess the agricultural evolution of tractor testing from 1969 to 2022 to ascertain the publication's scientific perspective on operational, ergonomic, and energy performance. We searched for relevant research in the Scopus and Web of Science (WOS) databases. The data were processed in RStudio software version 4.4.1, and we used elaborated bibliometric maps to research evolution, major journals, studies, countries, and keywords. The first research mainly concerned the development of new wheelsets, more efficient engines, and fuel consumption prediction models. After the 2000s, environmental protocols contributed to increasing publications on biofuels and renewable energies. Recently, an intense process of robotization in autonomous vehicles has improved to allow the replacement of combustion engines. Ergonomics and safety have been less recurrent topics in recent years, indicating a stable level in the actual research. New machine control models involving artificial intelligence are currently applied to obtain test results without using the machine in the field. These virtual models reduce costs and optimize resources. The most common terms were "tractor" and "agricultural machinery". The terms "Electric tractor", "agricultural robots", and "Matlab" indicate solid trends for future research. [ABSTRACT FROM AUTHOR]

In response to the current low work efficiency of soil ridge-working machinery, as well as its poor stability, passability, and adaptability, this paper designs an independent leg-type working platform that can autonomously adjust its vehicle attitude through LiDAR scanning in a soil ridge-working environment. The platform, in terms of its mechanism and structural design, adopts dual parallelogram mechanisms, dual lead screw mechanisms, and independent column leg mechanisms, with a maximum adjustable ground clearance of 107 mm and a maximum wheelbase adjustment of 150 mm. A gyroscope is mounted at the center of the platform for attitude adjustment, ensuring the accurate data collection of the ultrasonic ranging module. Moreover, the platform adopts an adaptive adjustment method based on vehicle attitude and soil ridge shape parameters, obtaining soil ridge parameters through LiDAR and combining ultrasonic ranging module data with stepper motor pulse signals to obtain the absolute vehicle attitude parameters, using first and second linear regression methods to adjust the vehicle attitude and other working parameters. A prototype was also created, and the test data from the soil obtained through experiments show that, after leveling with the gyroscope leveling algorithm, the average value of the pitch angle is up to 0.6154°, and the average value of the roll angle is up to 0.9989°, with the maximum variance of the pitch angle being 0.0474° and the maximum variance of the tilt angle being 0.1320°. After the ultrasonic ranging module data are filtered by the Kalman filter, the maximum variance is 0.0304, and after applying the final fusion algorithm, the maximum variance is only 0.0085. The LiDAR measurement width value deviates from the actual width value by no more than 1.0 cm, and the LiDAR measurement height value deviates from the actual height value by no more than 1.0 cm. The platform's actual adjusted width deviates from the actual soil ridge width by no more than 2.0 cm, and the platform's actual adjusted height deviates from the actual soil ridge height by no more than 1.2 cm. This platform can improve the passability, adaptability, and stability of agricultural machinery in soil ridge work and provide technical references for subsequent related research. [ABSTRACT FROM AUTHOR]

In order to solve the problem that, when the vehicle speed of an agricultural distributed dual-wheel electric-drive tractor changes or the system is disturbed by off-load, the traditional PI control cannot be adjusted in time, resulting in the overshoot of steering control or control delay, meaning it then cannot travel along the target trajectory quickly and accurately, a parameter-adaptive dual-dimensional fuzzy PI speed and steering adjustment controller was proposed, which can adjust the PI parameters in real time based on the deviation between vehicle speed, steering, and reference value, as well as the rate of deviation change. Firstly, based on the operational characteristics of agricultural tractors, a dynamic model of a distributed dual-wheel tractor was established, and a hardware-in-the-loop (HIL) test bench was set up. Fuzzy PI controller algorithms for vehicle speed and steering were designed and developed. In addition, simulations and tests were carried out under no-load and off-load tractor operating conditions with MATLAB/Simulink, respectively. The results indicate that, compared with a traditional PI controller, the fuzzy PI controller exhibits a faster control response and better robustness, reducing overshoot by approximately 60% and the steady-state response time by approximately 25%. When subjected to off-load disturbances, the maximum trajectory offset is controlled within 0.08 m, and the maximum trajectory offset is reduced by 45% compared with a traditional PI controller; therefore, the fuzzy PI control algorithm proposed in this paper makes the tractor's running trajectory more stable and has stronger anti-interference ability towards off-load disturbances. [ABSTRACT FROM AUTHOR]

The extraction of navigation lines is a crucial aspect in the field autopilot system for intelligent agricultural equipment. Given that soybean seedlings are small, and straw can be found in certain Northeast China soybean fields, accurately obtaining feature points and extracting navigation lines during the soybean seedling stage poses numerous challenges. To solve the above problems, this paper proposes a method of extracting navigation lines based on the average coordinate feature points of pixel points in the bean seedling belt according to the calculation of the average coordinate. In this study, the soybean seedling was chosen as the research subject, and the Hue, Saturation, Value (HSV) colour model was employed in conjunction with the maximum interclass variance (OTSU) method for RGB image segmentation. To extract soybean seedling bands, a novel approach of framing binarised image contours by drawing external rectangles and calculating average coordinates of white pixel points as feature points was proposed. The feature points were normalised, and then the improved adaptive DBSCAN clustering method was used to cluster the feature points. The least squares method was used to fit the centre line of the crops and the navigation line, and the results showed that the average distance deviation and the average angle deviation of the proposed algorithm were 7.38 and 0.32. The fitted navigation line achieved an accuracy of 96.77%, meeting the requirements for extracting navigation lines in intelligent agricultural machinery equipment for soybean inter-row cultivation. This provides a theoretical foundation for realising automatic driving of intelligent agricultural machinery in the field. [ABSTRACT FROM AUTHOR]

To address the inefficient crushing of fertilizer during the mechanized spreading process caused by the caking of high-humidity organic fertilizer, a fertilizer spreader with a combined rotor and lining plate crushing mechanism was proposed in this paper. With the introduction of the basic structure and working principle of the spreader, a particle group model for an organic fertilizer consisting of both caked and bulk fertilizer was built, based on the Hertz–Mindlin model with bonding and the Hertz–Mindlin model with JKR contact, in EDEM to construct an organic fertilizer-crushing-and-spreading model. With the rotor speed, the axial distance of the hammer, and the number of circumferential hammer groups as the experimental factors and the maximum broken bond rate of the caked organic fertilizer and the minimum coefficient of variation of spreading uniformity as the experimental indices, the Box–Behnken test method was employed to establish regression equations for response surface analysis and multi-objective optimization of the test results. The results indicated that, when the rotor speed was 6.47 Hz, the axial distance of the hammer was 90.30 mm, the number of circumferential hammer groups was five, the broken bond rate reached 90.86%, and the coefficient of variation was 21.45%. Verification tests under these conditions showed a broken bond rate of 90.03% and a coefficient of variation of 22.12%, which were consistent with the optimization results. Therefore, our research provides a reference for the structural design of an organic fertilizer spreader and the optimization of its working parameters. [ABSTRACT FROM AUTHOR]

Agricultural machinery services are an important guaranteed way to promote Conservation Agriculture. It is of great significance to study how to encourage farmers to choose agricultural machinery services to promote the standard implementation of Conservation Agriculture technology. In order to promote the implementation of Conservation Agriculture and improve the supply of agricultural machinery services, this paper identifies the stakeholders of normative Conservation Agriculture technology adoption behavior and the relationship between agricultural machinery service organizations, farmers and agriculture-related governments. An evolutionary game model was established to evaluate the decision-making characteristics of tripartite behavior and simulate the evolution trend of stakeholder behavior. The results show that agriculture-related governments, agricultural machinery service organizations and farmers can achieve evolutionarily stable strategies. The punishments and subsidies of agriculture-related governments and the supervision cost of all links of agricultural machinery social service organizations can significantly affect the behavior strategies of the three parties. The government set up reasonable subsidy and punishment mechanisms, and the agricultural machinery service organization controls the supervision cost of all links to ensure the stability of the three-party behavior strategy. This study provides theoretical guidance for scientific decision making and active cooperative development of the government, farmers and agricultural machinery service organizations and lays a foundation for countermeasures and suggestions to further promote farmers' implementation of Conservation Agriculture technology. [ABSTRACT FROM AUTHOR]

Maintaining agricultural machinery is crucial for efficient mechanized farming. Specifically, diagnosing faults in rolling bearings, which are essential rotating components, is of significant importance. Domain-adaptive technology often addresses the challenge of limited labeled data from a single source domain. However, information transfer can sometimes fall short in providing adequate relevant details for supporting target diagnosis tasks, leading to poor recognition performance. This paper introduces a novel fault diagnosis model based on a multi-source locally adaptive graph convolution network to diagnose rolling bearing faults in agricultural machinery. The model initially employs an overlapping sampling method to enhance sample data. Recognizing that two-dimensional time–frequency signals possess richer spatial characteristics in neural networks, wavelet transform is used to convert time series samples into time–frequency graph samples before feeding them into the feature network. This approach constructs a sample data pair from both source and target domains. Furthermore, a feature extraction network is developed by integrating the strengths of deep residual networks and graph convolutional networks, enabling the model to better learn invariant features across domains. The locally adaptive method aids the model in more effectively aligning features from the source and target domains. The model incorporates a Softmax layer as the bearing state classifier, which is set up after the graph convolutional network layer, and outputs bearing state recognition results upon reaching a set number of iterations. The proposed method's effectiveness was validated using a bearing dataset from Jiangnan University. For three different groups of bearing fault diagnosis tasks under varying working conditions, the proposed method achieved recognition accuracies above 99%, with an improvement of 0.30%-4.33% compared to single-source domain diagnosis models. Comparative results indicate that the proposed method can effectively identify bearing states even without target domain labels, showcasing its practical engineering application value. [ABSTRACT FROM AUTHOR]

Detailed performance testing of agricultural machinery is very important in determining its value in use. These measurements cannot be limited to laboratory tests, such as braking the power take-off (PTO) shaft of a tractor or performing dynamic tests of implements in a soil bin. Field tests are the ultimate way to test the capabilities of agricultural machinery. However, during such field tests, there are many parameters that can interfere with and affect the test results, such as inhomogeneity of the soil and tyre characteristics. In order to minimise these effects as much as possible, Hungarian University of Agriculture and Life Sciences (MATE) Institute of Technology, Agriculture Engineering Labs. has developed a dynamometer vehicle with an electronic brake control system that is suitable for measuring the traction characteristics of agricultural tractors and other terrain vehicles. It is also capable of testing different track systems and tyre-soil interactions. This paper introduces this special test vehicle by presenting measurement results and also describes other interesting applications for the agricultural community. [ABSTRACT FROM AUTHOR]

Agricultural irrigation pumps are the main agricultural irrigation machinery, and their performance is closely related to the flow characteristics inside them. This paper combines experimental research and numerical simulation analysis. Based on the good agreement between the experimental results and numerical simulation data, this paper focuses on studying the characteristics of pressure fluctuations of agricultural irrigation pumps under cavitation conditions. The study found that under non-cavitation conditions, the pressure fluctuations at different monitoring points in pumps with different numbers of blades showed periodic variations, which are related to the number of blades. Under cavitation conditions, the pressure fluctuation coefficients of agricultural irrigation pumps with different impeller blade numbers increased, with a significant impact on the distribution of radial forces on the impeller. The research results supplement the relevant theoretical analysis and have certain practical significance for the application of agricultural irrigation pumps to practical production. [ABSTRACT FROM AUTHOR]

The paper evaluates the possibility of using dissimilar materials joined by welding technology in the construction of agricultural machinery. The desire to design larger and more efficient structures requires designers to combine materials with different mechanical and structural properties. In such a case, it is very important to properly select welding parameters so that, on the one hand, the quality of the joint meets the standard requirements, and on the other, the welding process is not too energy-intensive. In this paper, overlay joints connecting S355 steel with Strenx 700 steel were analyzed in terms of strength for three different values of welding parameters and different thicknesses. The starting point was the reference parameters recommended by the company's welding technologists, which were reduced by 10 and 20% according to the linear welding energy. The study compared the strength, ductility and macrostructure of the joints, as well as the energy intensity of the process. The proposed dissimilar joints achieved approximately a 10% increase in the strength limit of the components in comparison to the previously recommended welding parameters. Additionally, finite element analysis calculations of the improved designs showed significant weight reduction (up to 40%) for the relevant agricultural machinery components. [ABSTRACT FROM AUTHOR]

This paper presents a path tracking algorithm based on a fuzzy control pure tracking model for autonomous navigation of 4WS agricultural machines. The aim of this research is to implement path tracking for unmanned 4WS agricultural machinery and to solve the problem of difficult determination of forward-looking distances in pure tracking algorithms. By using the pure tracking algorithm model and a fuzzy controller, this paper converts the heading deviation and lateral deviation in one control cycle into the sum of lateral deviation as the first input to the fuzzy controller and the vehicle travel speed as the second input to the fuzzy controller, thus outputting the actual forward-looking distance. In order to verify the practicality, accuracy, and path tracking precision of the proposed path tracking algorithm, a straight-line path tracking test under variable speed conditions and a turning path tracking test under non-fixed forward-looking distance conditions were carried out using a test platform after simulation on MATLAB/Simulink in this paper. The test results show that: in the straight-line path tracking process, the maximum overshoot is 0.123 m, and after stable driving, the maximum lateral deviation of the straight-line tracking part is 0.058 m and the steady-state deviation is 0.039 m; in the bow-turn path tracking process, the absolute value of the maximum lateral deviation of the actual driving trajectory of the farm machine from the desired path is 0.139 m, and the average tracking deviation is 0.041 m. It can be seen that the path tracking control algorithm proposed in this paper has good tracking accuracy as well as convergence, and can meet the demand for the autonomous navigation function of 4WS agricultural machinery, which has a certain application value. [ABSTRACT FROM AUTHOR]

In this paper, we investigated the idea of including mobile robots as complementary machinery to tractors in an agricultural context. The main idea is not to replace the human farmer, but to augment his/her capabilities by deploying mobile robots as assistants in field operations. The scheme is based on a leader–follower approach. The manned tractor is used as a leader, which will be taken as a reference point for a follower. The follower then takes the position of the leader as a target, and follows it in an autonomous manner. This will allow the farmer to multiply the working width by the number of mobile robots deployed during field operations. In this paper, we present a detailed description of the system, the theoretical aspect that allows the robot to autonomously follow the tractor, in addition to the different experimental steps that allowed us to test the system in the field to assess the robustness of the proposed scheme. [ABSTRACT FROM AUTHOR]

In order to improve the effect of agricultural machinery task scheduling, this paper starts from the perspective of multiobjective optimization to achieve task scheduling based on multiobjective particle swarm optimization algorithm. The position of each particle is a combination of resource options for each construction activity, and the displacement range and speed range of the particles are determined. Moreover, this paper uses the method of introducing an external storage library to store the current noninferior solutions and uses the adaptive grid method and the roulette selection method to select the global optimal solution of the particles. In addition, this paper proposes a task scheduling algorithm suitable for modern agricultural machinery based on the actual needs of current agricultural machinery task scheduling. The experimental results show that the agricultural machinery task scheduling algorithm based on multiobjective optimization proposed in this paper has a good agricultural machinery task scheduling effect and meets the basic purpose of optimizing the algorithm in this paper. [ABSTRACT FROM AUTHOR]

The aim of this study was to overcome the impact of vibration generated by agricultural machinery on the monitoring accuracy and performance of vehicle-mounted crop growth monitoring systems during field operation. This paper developed a vehicle-mounted crop growth monitoring system with vibration damping capability to achieve this goal. The system consists of a multispectral crop growth sensor, signal conditioning module, and truss-type sensor bracket with self-vibration damping capability. The commercial finite element analysis software ABAQUS 6.10 was used to conduct modal and dynamic simulation analyses of the sensor bracket, which indicate that the truss-type sensor bracket can damp vibrations effectively. The p-values (least significant differences) of crop canopy DNRE (red edge normalized difference vegetation index) under different operating speeds (1.5, 3, and 4.5 km/h) are 0.454, 0.703, 0.81, and 0.838, respectively, for four different crop growth stages. In a comparative experiment between the proposed monitoring system and two similar vehicle-mounted sensors (CropSpec and GreenSeeker RT 200) for measuring agronomic parameters at different stages of crop growth, the proposed monitoring system yielded R2 values of 0.8757, 0.7194, and 0.795, respectively, and RMSE values of 0.7157, 2.2341, and 2.0952, respectively, in the tillering stage, jointing stage, and tillering and jointing stage, outperforming the other two sensors. [ABSTRACT FROM AUTHOR]

In the cultivation of the oil-bearing rose, only the harvesting of the flowers in the rose plantations is completely manual. Research on the mechanization of the harvesting operation has been going on for the past few years. Partially improving the working conditions and speeding up the harvesting operation are provided by the developed technical means for transporting the hand-picking flowers pickers and their collected produce in the rows of the plantations with the oil-bearing culture. The attempt to fully mechanize the harvesting operation through the creation of a rose harvester that only picks off the petals was received skeptically by the specialists in the rose-producing branch both from the point of view of functional suitability and due to doubts about the economic inexpediency of the development. Preservation of the traditional hand-picking method of picking off the rose flower under the ovary, picking off the flowers to be harvested without injuring the adjacent undissolved buds and foliage, limiting to a minimum the contact with the thorns on the rose stems and stinging insects, etc., advantages provides a newly developed agricultural machine for pneumatic harvesting in oil-bearing rose plantations, field test results of which are presented in the paper. Experimentally established during the testing of two of its variants (one-row and two-row) are the advantages of machine picking over traditional manual picking -- better working conditions for the pickers, complete harvesting of the rose flowers to be picked for the day, higher productivity. The main factors (the condition of the rose plantation, the amount of blooming rose flowers, the qualification of the rose pickers) on which the efficiency of the pneumatic rose harvesting machine depends are determined. The ability to ensure the productivity of the tractor/implement combination formed on the basis of the two-row variant of the machine, with which, within the speci- fied time range to harvest the production from plantations with an area of 1.2 ha and more, has been proven. [ABSTRACT FROM AUTHOR]

This paper primarily presents statistics on the variation of physical characteristics (dimensions and mass) for the coulter discs of double-disc seeders. This statistic was calculated based on actual measurements of the probability density and cumulative probability for the mass of the discs, their average thickness, and outer diameter. These parameters (m = 4000 g, g = 4 mm, D = 380 mm) are tracked from the design phase to the actual realization phase, being the parameters specified in the purchase phase by users from agricultural machinery distributors. The standard deviation and deviation from the mean for a cumulative normal (Gaussian) distribution were calculated, and an analysis was made for the decrease of the mentioned parameters in discs already used for sowing cereals on an area of 80 hectares, due to the wear that appeared as a trace of the contact with the soil and the abrasive wear caused by it. Testing of disk parameters using both logarithmic and Gaussian distributions was performed, and test results are presented through appropriate graphs. With small deviations (which appeared for known or unknown reasons), it can be stated that the distribution of the mentioned parameters is generally a normal-type distribution. If the new discs are 15″ in diameter, experts recommend that they be replaced when they reach a diameter of less than 14.5 inches. This study can provide a reference for improving the physical characteristics of opener discs of seed drills in no-till conditions and beyond. [ABSTRACT FROM AUTHOR]

To address the problems of low bright rates and high rates of potato injuries, a left and right-hand rotation combination of potato–soil separation devices was developed. Its overall structure and working principle were introduced. A Texture Analyzer and pressure sensor were used to measure the force threshold of different varieties of potatoes. A discrete element model of separation rollers and potatoes was established. The collision characteristics of potatoes were analyzed using the device inclination angle, rotational speed, and the center distance of the separation rollers as test factors. A field trial was carried out to optimize the best combination of factors by taking the rate of injured potatoes, bright potatoes, and skin-breaking rate as the test indexes. The force threshold for skin-breaking injury in potatoes was found to be 190–195 N. When the inclination angle of the device was 6°, the rotation speed of the separation roller was 100 r/min, and the distance between the centers of the separation rollers was 79 mm. The rate of injury was 1.25%, the rate of bright potatoes was 99.01%, and the rate of skin-breaking was 1.58%. When the inclination angle of the device was 8°, the rotational speed of the separating roller was 80 r/min, and the center distance of the separating roller was 79 mm, the rate of injured potato was 1.43%, the rate of bright potato was 98.64%, and the rate of broken skin was 1.77%. This paper offers an optimized reference for the effectual removal of sticky soil. [ABSTRACT FROM AUTHOR]

This research identifies the critical challenges surrounding agricultural value chains and their role in fostering sustainable industrial development in Sub-Saharan Africa (SSA) between 1990 and 2022, utilizing the Panel Least Squares (PLS) technique. The paper reports that agricultural value added (p < 0.01), farm machinery (p < 0.05), human capital (p < 0.01), and access to electricity (p < 0.01) exhibited a positive and significant influence on industrial sector output. Conversely, agricultural raw materials exports (p < 0.01) had a negative and significant impact on industrial sector output (p < 0.01). In conclusion, the study underscores the critical role of agricultural value chains in fueling sustainable industrial development in SSA and recommends that policymakers formulate plans to leverage agricultural value chains as catalysts for enduring economic growth and development. [ABSTRACT FROM AUTHOR]

The paper proposes a process to translate systemic grand challenges of agricultural supply chain into company specific operationalization actions. The objectives of this study are to elucidate the requisite stages of the process, delineate the specific aspects addressed by each step, and present potential methodologies employed throughout the process. The approach adopted in this paper is inherently constructive, employing a mixed methodology that integrates various methods and tools. Furthermore, 90 challenges of agricultural supply chain are delineated and structured around 6 themes. The strength of evidence of the challenges is then analyzed with a model that takes into account the number and types of references in which the challenges are mentioned and found, respectively. The study identified 21 challenges as "evident" and proceeded to the next step for quality function deployment. Finally, operationalization actions, including cooperating with other stakeholders of the supply chain, regulators, and farmers were determined to tackle critical first mile challenges for the case company. The paper makes a distinctive contribution by presenting a comprehensive framework for translating systemic agricultural supply chain challenges into actionable steps, uniquely addressing the intersection of intricate challenges and sustainability imperatives. This novel approach does not only advance the understanding of operationalization but also underscores the vital role of sustainability in navigating the complexities of modern agricultural supply chains. [ABSTRACT FROM AUTHOR]

It is crucial to increase the resilience of the global food production and distribution systems against the growing concerns relating to factors that could cause global catastrophic infrastructure losses, such as nuclear war or a worldwide pandemic. Currently, such an event would result in the global loss of industry, including the ability to drill and refine crude oil. In such an event, the existing above-ground reserves of diesel and gasoline are likely to still be intact but would only be able to power the production and transportation of food between 158 days and 481 days with 80% confidence, where the mean is 195 days at current rates. This paper investigates a novel group of interventions in relation to the scenario of providing food under these conditions. It was found that by using a plausible combination of wood gasification, increasing vehicle utilisation rate, and reducing food consumption, the stockpile duration could increase to between 382 days and 1501 days with 80% confidence, where the mean is 757 days. This is an improvement in mean duration by a factor of 3.9. It was discovered that diesel is the limiting fuel in all scenarios due to wood gas only being a partial replacement for diesel fuel and also because of the prevalence of diesel engines in both the agricultural and trucking industries. A sensitivity analysis was completed identifying that reducing food consumption to minimum levels was the most effective method to prolong diesel reserves. The other factors that benefited from extending fuel reserves in terms of their effectiveness are reducing the lag time before gasification devices are installed, increasing the rate at which gasification devices are installed, and increasing the agricultural equipment utilisation rate. [ABSTRACT FROM AUTHOR]

The paper presents research on measuring and the possibility of prediction of noise levels on the left and right sides of the operator within the cabin of an agricultural tractor when moving across various agrotechnical surfaces, considering movement velocity and tire pressures while employing machine learning techniques. Noise level measurements were conducted on a LANDINI POWERFARM 100 type tractor, and aligned with standards (HRN ISO 5008, HRN ISO 6396 and HRN ISO 5131). The obtained noise values were divided into two data sets (left and right set) and processed using multiple linear regression (mlr) and three machine learning methods (gradient boosting machine (gbm); support vector machine using radial basis function kernel (svmRadial); monotone multi-layer perceptron neural network (monmlp)). The most accurate method, considering surfaces, from the left side data set—(R2 0.515–0.955); (RMSE 0.302–0.704); (MAE 0.225–0.488)—and the right side—(R2 0.555–0.955); (RMSE 0.180–0.969); (MAE 0.139–0.644)—was monmlp predominantly, and to a lesser extent svmRadial. On analyzing the total data sets from the left and right sides regarding surfaces, gbm emerged as the most accurate method. The application of machine learning methods demonstrated data accuracy, yet in future research, measurements on certain surfaces may need to be repeated multiple times potentially to improve accuracy further. [ABSTRACT FROM AUTHOR]

Loop closure detection plays an important role in the construction of reliable maps for intelligent agricultural machinery equipment. With the combination of convolutional neural networks (CNN), its accuracy and real-time performance are better than those based on traditional manual features. However, due to the use of small embedded devices in agricultural machinery and the need to handle multiple tasks simultaneously, achieving optimal response speeds becomes challenging, especially when operating on large networks. This emphasizes the need to study in depth the kind of lightweight CNN loop closure detection algorithm more suitable for intelligent agricultural machinery. This paper compares a variety of loop closure detection based on lightweight CNN features. Specifically, we prove that GhostNet with feature reuse can extract image features with both high-dimensional semantic information and low-dimensional geometric information, which can significantly improve the loop closure detection accuracy and real-time performance. To further enhance the speed of detection, we implement Multi-Probe Random Hyperplane Local Sensitive Hashing (LSH) algorithms. We evaluate our approach using both a public dataset and a proprietary greenhouse dataset, employing an incremental data processing method. The results demonstrate that GhostNet and the Linear Scanning Multi-Probe LSH algorithm synergize to meet the precision and real-time requirements of agricultural closed-loop detection. [ABSTRACT FROM AUTHOR]

Soil compaction is generally viewed as one of the most serious soil degradation problems and a determining factor in crop productivity worldwide. It is imperative to understand the processes involved in soil compaction to meet the future global challenges of food security. In this work, we used co‐occurring keyword analysis to summarize 3491 papers on soil compaction over the past 40 years, elaborating on the main research focuses such as the causes, influencing factors, and effects of soil compaction on crops, and the mitigation and prevention of soil compaction. This review provides a comprehensive discussion of the effects of soil compaction, including altering soil structure, increasing bulk density (BD) and penetration resistance (PR), and reducing porosity and soil hydraulic properties. Notably, based on the 387 data points of 11 papers about BD, our results demonstrated soil compaction on average, increased BD by 7.6%, 6.9%, and 3.2% in the medium‐, coarse‐, and fine‐textured soils, respectively. Based on the 264 data points of 18 papers, in the 0–30 cm soil layer, compaction increased penetration resistance (by 91% in the coarse‐textured, 84.2% in the medium‐textured, and 8.8% in the fine‐textured soils). Compacted soil limits the access of crop roots to water and nutrients, leading to poor root development and reduced crop productivity. There was a difference in soil compaction sensitivity between the different crops, but crop growth and yield showed an overall worsening trend with increasing degrees of compaction. This review collected data points on 142 crop yields and found that wheat, barley, corn, and soybean yields decreased by an average of 4.1%, 15.1%, 37.7%, and 22.7%, respectively, in the BD range of 1.1–1.8 Mg/cm3 after compaction. Additionally, the effectiveness of different compaction mitigation measures, including natural, tillage, and biological, is systematically discussed. Compared with soil compaction mitigation measures, prevention should be the top priority although there is still a lack of practical prevention methods. Soil conditions and agricultural machinery type are the main factors affecting the risk of soil compaction in the process of soil compaction. Therefore, it is particularly important to optimize the soil working conditions in the field and the type of farm machinery used to reduce the risk of soil compaction. This initiative is pivotal for ensuring sustainable systems for food production and recovering crop productivity from compacted soil. [ABSTRACT FROM AUTHOR]

A major occupational health risk in the agricultural industry is Hand Arm Vibration Syndrome (HAVS), which is brought on by exposure to vibration from handled machinery. This review thoroughly covers the historical background and prevalence of HAVS in agriculture, highlighting the harm it does to the health and productivity of workers. The goal is to solve the research deficit in this area by concentrating on vibration isolation and dampening approaches. The paper comprehensively examines measurement methods, such as accelerometer-based approaches, strain gauge and force plate methods, wireless sensor networks, for evaluating vibrations related to HAVS. These methods are compared in order to clarify their advantages and disadvantages. Furthermore, engineering controls, isolation mounts, dampers, and smart technologies are included in the categories of shock and vibration reduction solutions based on their efficiency and application. Also covered are human factors that affect vibration reduction. The difficulties in putting reduction strategies into practice are underlined. These difficulties include economic considerations, technological limitations, and user approval. In order to draw useful conclusions, case studies demonstrating effective vibration reduction methods in agricultural contexts are provided. Future directions for reducing HAVS in agriculture include interdisciplinary research opportunities, developing technologies for vibration reduction, and a global approach. This review concludes by highlighting the importance of tackling HAVS, providing insights into real-world applications, and suggesting future research initiatives. [ABSTRACT FROM AUTHOR]

Effective scheduling of multiple agricultural machines in emergencies can reduce crop losses to a great extent. In this paper, cooperative scheduling based on deep reinforcement learning for multi-agricultural machines with deadlines is designed to minimize makespan. With the asymmetric transfer paths among farmlands, the problem of agricultural machinery scheduling under emergencies is modeled as an asymmetric multiple traveling salesman problem with time windows (AMTSPTW). With the popular encoder-decoder structure, heterogeneous feature fusion attention is designed in the encoder to integrate time windows and asymmetric transfer paths for more comprehensive and better feature extraction. Meanwhile, a path segmentation mask mechanism in the decoder is proposed to divide solutions efficiently by adding virtual depots to assign work to each agricultural machinery. Experimental results show that our proposal outperforms existing modified baselines for the studied problem. Especially, the measurements of computation ratio and makespan are improved by 26.7% and 21.9% on average, respectively. The computation time of our proposed strategy has a significant improvement over these comparisons. Meanwhile, our strategy has a better generalization for larger problems. [ABSTRACT FROM AUTHOR]

In order to solve the problem of poor mixing effect of inclined spiral conveyor, this paper designed an inclined spiral and ribbon blade combination conveying device. The mechanics and kinematics analysis of the movement process of the material unit in each mechanism was carried out, on which key devices and components were designed based. Effects of the speed of main shaft (X1), the full coefficient (X2) and the angle of inclination (X3) on the mixing uniformity (Y1) and the residual rate of material (Y2) were explored. Three-dimensional quadratic regression orthogonal rotation central combination experiment method combined with response surface method was used to conduct experiments and explore the interaction effects of influence factors on indicators. A regression model of influence factors and evaluation indicators was established through the analysis of variance. The significant factors affecting Y1 were ordered of X3, X2, X1, and the significant factors affecting Y2 were ordered of X2, X1, and X3. In the interaction of factors, X1X2 had a significant impact on Y1 and Y2; X1X3 had an extremely significant impact on Y1; X2X3 had an extremely significant effect on Y1 and a significant impact on Y2. The optimal structure and working parameters combination were determined to be 50 rpm for the speed of main shaft, 60% for the full coefficient, and 19° for the angle of inclination, while the verification experiments demonstrated that the mixing uniformity and residual rate of material corresponding to the verification test were 90.37% and 5.31%, respectively. The inclined device with combined spiral and ribbon blade developed in this study meets the design requirements for the misprocess of the fertilizer utilization of agricultural organic wastes. [ABSTRACT FROM AUTHOR]

The Complete Coverage Path Planning (CCPP) problem is a subfield of industrial motion planning that has applications in various domains, ranging from mobile robotics to treatment applications. Especially in precision agriculture with a high level of automation, the use of CCPP techniques is essential for efficient resource utilization, reduced soil compaction, and increased yields. This paper reviews the CCPP problem in the context of machines operating in agricultural fields and proposes a methodological approach consisting of three steps: Generating the Guidance Tracks (i.e., the track system along which the path should be oriented), determining the traversing sequence through these tracks, and planning a smooth and drivable path. This paper provides an in‐depth review of optimization‐based approaches that deal with the first step, the generation of the guidance track system. Thereby, a comprehensive and pedagogical approach for the generation of guidance tracks for arbitrarily shaped two‐dimensional regions of interest is provided, along with an overview and detailed elaboration on different exact cellular decomposition techniques found in literature. Furthermore, cost functions are outlined for the different approaches presented in this work, which are utilized to generate optimal guidance tracks. Finally, this survey serves as an introductory guide for research and practitioners to solve the CCPP problem effectively and efficiently. [ABSTRACT FROM AUTHOR]

Because of the continuous improvement of technology, mechanization has emerged in various fields. Due to the different suitable seasons for the growth of agricultural plants, agricultural mechanization faces problems different from other industries. That is, agricultural machinery and equipment may be used frequently for a period of time, or may be idle for a long time. This leads to the aging of equipment no longer becoming regular, the maintenance time of spare parts is not fixed, the number of spare parts stored in the spare parts warehouse cannot be too large to occupy funds, and the number cannot be too small to meet the maintenance needs, so the prediction of agricultural machinery spare parts has become particularly important. Due to the lack of information, the difficulty of labeling, and the imbalance of positive and negative sample classification, this paper used a semi-supervised learning algorithm to solve the problem of agricultural machinery spare parts data classification. In order to forecast the demand for spare parts of agricultural machinery, this paper compared the IPSO-BP neural network algorithm and BP neural network algorithm. It was found that the IPSO-BP neural network was used to forecast the demand for spare parts of agricultural machinery, and the error between the predicted value and the actual value was small and met the accuracy requirements. [ABSTRACT FROM AUTHOR]

Identifying the in-field trajectories of harvests is important for the activity analysis of agricultural machinery. This paper presents a K-means-based trajectory identification method that can automatically detect the "turning", "working", and "abnormal working" trajectories for wheat harvester in-field operation scenarios. This method contains two stages: clustering and correction. The clustering stage performs by the two-step K-means iterative clustering method (D-K-means). In the correction stage, the first step (M1) is performed based on the three distance features between the trajectory segments and the cluster center of the trajectory segments. The second step (M2) is based on the direction change of the "turning" and "abnormal working" trajectories. The third correction step (M3) is based on the operating characteristics to specify the start and stop positions of the turning. The developed method was validated by 50 trajectories. The results for the three trajectories and the five time intervals from 1 s to 5 s both have f1-scores above 0.90, and the f1-score using only the clustering method and the method of this paper increased from 0.55 to 0.95. After removing the turning and abnormal operation trajectories, the error of calculating farmland area with distance algorithm is reduced by 17.04% compared with that before processing. [ABSTRACT FROM AUTHOR]

Intelligent agricultural machinery refers to machinery that can independently complete tasks in the field, which has great significance for the transformation of agricultural modernization. However, most of the existing research on intelligent agricultural machinery is limited to unilateral research on positioning, planning, and control, and has not organically combined the three to form a fully functional intelligent agricultural machinery system. Based on this, this article has developed an intelligent agricultural machinery system that integrates positioning, planning, and control. In response to the problem of large positioning errors in the large range of plane anchoring longitude and latitude, this article integrates geographic factors such as ellipsoid ratio, long and short axis radius, and altitude into coordinate transformation, and combines RTK/INS integrated inertial navigation to achieve precise positioning of the entire vehicle over a large range. In response to the problem that existing full-coverage path planning algorithms only focus on job coverage as the optimization objective and cannot achieve path optimization, this paper proposes a multi-objective function-coupled full-coverage path planning algorithm that integrates three optimization objectives: job coverage, job path length, and job path quantity. This algorithm achieves optimal path planning while ensuring job coverage. As the existing pure pursuit algorithm is not suitable for the motion control of tracked mobile machinery, this paper reconstructs the existing pure pursuit algorithm based on the Kinematics characteristics of tracked mobile machinery, and adds a linear interpolation module, so that the actual tracking path points of motion control are always ideal tracking path points, effectively improving the motion control accuracy and control stability. Finally, the feasibility of the intelligent agricultural machinery system was demonstrated through corresponding simulation and actual vehicle experiments. This intelligent agricultural machinery system can cooperate with various operating tools and independently complete the vast majority of agricultural production activities. [ABSTRACT FROM AUTHOR]

In the field of agricultural machinery, various empirical field tests are performed to measure the tillage force for precision tillage. However, the field test performance is costly and time-consuming, and there are many constraints on weather and field soil conditions; the utilization of simulation studies is required to overcome these shortcomings. As a result, the SPH method and the coupled FEM-SPH method are used in this paper to investigate the mouldboard plough–soil interaction. In this paper, the finite element software LS-DYNA was used to build the SPH model and the FEM-SPH coupling model of soil cutting, as well as to investigate the change in cutting resistance during the soil cutting process. The simulation results are compared with those of the experiments, and the curves of the simulation and experiment are in good agreement, which verifies the reliability of the model. The validated simulation model was used to investigate the effects of the cutting speed, depth of cut, inclination angle, and lifting angle of the mouldboard plough on cutting resistance. The simulation studies show that the SPH model takes 5 h and 2 min to compute, while the FEM-SPH coupled model takes 38 min; obviously, the computational efficiency of the FEM-SPH coupled model is higher. The relative errors between the SPH model and the experiment are 2.17% and 3.65%, respectively. The relative errors between the FEM-SPH coupled model and the experiment are 5.96% and 10.67%, respectively. Obviously, the SPH model has a higher computational accuracy. The average cutting resistances predicted by the SPH model and the FEM-SPH coupled model, respectively, were 349.48 N and 306.25 N; these resistances are useful for precision tillage. The cutting resistance increases with the increase in cutting speed and is quadratic; the cutting resistance increases with the increase in cutting depth and is quadratic; the horizontal cutting resistance and the combined cutting resistance increase with the increase in inclination angle, while the vertical cutting resistance remains essentially constant with the increase in inclination angle; the horizontal cutting resistance and combined cutting resistance increase as the lifting angle increases, while the vertical cutting resistance decreases as the lifting angle increases. [ABSTRACT FROM AUTHOR]

The development of agricultural farming has evolved from traditional agricultural machinery due to its efficiency and autonomy. Intelligent agricultural machinery is capable of autonomous driving and remote control, but due to its limited perception of farmland and field obstacles, the assistance of unmanned aerial vehicles (UAVs) is required. Although existing intelligent systems have greater advantages than traditional agricultural machinery in improving the quality of operations and reducing labor costs, they also produce complex operational planning problems. Especially as agricultural products and fields become more diversified, it is necessary to develop an adaptive operation planning method that takes into account the efficiency and quality of work. However, the existing operation planning methods lack practicality and do not guarantee global optimization because traditional planners only consider the path commands and generate the path in the rectangular field without considering other factors. To overcome these drawbacks, this paper proposes a novel and practical collaborative path planning method for intelligent agricultural machinery based on unmanned aerial vehicles. First, we utilize UAVs for obstacle detection. With the field information and operation data preprocessed, automatic agricultural machinery could be assisted in avoiding obstacles in the field. Second, by considering both the historical state of the current operation and the statistics from previous operations, the real-time control of agricultural machinery is determined. Therefore, the K-means algorithm is used to extract key control parameters and discretize the state space of agricultural machinery. Finally, the dynamic operation plan is established based on the Markov chain. This plan can estimate the probability of agricultural machinery transitioning from one state to another by analyzing data, thereby dynamically determining real-time control strategies. The field test with an automatic tractor shows that the operation planner can achieve higher performance than the other two popular methods. [ABSTRACT FROM AUTHOR]

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The article offers information on the 10 most cited papers in International Journal of Agricultural and Biological Engineering (IJABE) including "Overview of biomass pretreatment for cellulosic ethanol production," by Yi Zheng and colleagues, "Effect of nitrogen and extraction method on algae lipid yield," by Enrong Mao and colleagues, and "Review of research on agricultural vehicle autonomous guidance," by Ming Li and colleagues.

The unmanned farm control platform is of great significance in promoting the supervision of farm production with less manpower or autonomous operation of farm machinery and the construction of farm informatization. Addressing the existing control platform for farm location information acquisition is time-consuming, labor-intensive, and lacks the whole process control of multiple types of farm machinery. In this paper, we propose an Internet of Things (IoT) control scheme for intelligent farm machinery operation of unmanned farms and design the access standards for multiple types of farm machinery, as well as realize the remote control of intelligent farm machinery operation by constructing a remote control model. A high-precision map construction method is designed to improve the DeepLabV3+ algorithm to identify fields and roads. The control models of path planning, remote task, remote control, and safety system are built to achieve the remote control of intelligent agricultural machinery operation. The proposed technology is implemented in the platform integration and application tests are carried out. The error of the constructed high-precision map is less than 3 cm, the completeness rate of the automatic boundary extraction rate is 96.71%, and the correctness rate is 95.63%, which can be used to obtain the boundary instead of manual labeling or on-site point picking. The use of the platform for the simultaneous control of three farm machinery operations reduces the number of people in operation and production and reduces the professional requirements of the personnel, which will promote the management of the entire farm by one person or even by no one in the future. [ABSTRACT FROM AUTHOR]

In mechanized agricultural activities, fuel is particularly important for tillage operations. In this study, the impact of seven distinct parameters on fuel usage per unit of draft power was examined. The parameters are tractor power, soil texture index, plowing speed, plowing depth, width of implement, and both initial soil moisture content and soil bulk density. This study investigated the construction of an artificial neural network (ANN) model for tractor-specific fuel consumption predictions for two tillage implements: chisel and moldboard plows. The ANN model was created based on the collection of related data from previous research studies, and the validation was performed using actual field experiments in clay soil using a chisel plow. The developed ANN model (9-22-1) was confirmed by graphical assessment; additionally, the root-mean-square error (RMSE) was computed. Based on the RMSE, the results demonstrated a good agreement for specific fuel consumption per draft power between the observed and predicted values, with corresponding RMSE values of 0.08 L/kWh and 0.075 L/kWh for the training and testing datasets, respectively. The novelty of the work presented in this paper is that, for the first time, a farm machinery manager can optimize tractor fuel consumption per draft power by carefully controlling certain parameters, such as initial soil moisture content, tractor power, plowing speed, implement width, and depth of plowing. The results show that the input parameters make a significant contribution to the output over the used data with different percentages. Accordingly, the contribution analysis showed that the implement width had a high impact on tractor-specific fuel consumption for both plows at 30.13%; additionally, the chisel and moldboard plows contributed 4.19% and 4.25% in predicting tractor fuel consumption per draft power. This study concluded that practical useful advice for agricultural production can be achieved through optimizing fuel consumption rate by selecting the proper levels of affecting parameters to reduce fuel costs. Moreover, an ANN model could be used to develop future tractor fuel-planning schemes for tillage operations. [ABSTRACT FROM AUTHOR]

Manufacturing has the potential to drive economic growth, job creation and poverty reduction, but African manufacturers face challenges in competing with global markets. Agricultural machinery manufacturing may be one niche where African manufacturers can succeed. This paper examines the challenges and opportunities facing local agricultural manufacturers in Africa based on a survey of 386 manufacturers in four countries and qualitative methods. Results show that small, dedicated entrepreneurs have created vibrant local machinery markets. These manufacturers have several comparative advantages, in particular, the ability to develop machinery adapted to local agroecological conditions but face challenges related to financing, human resources, utilities, raw materials, production equipment and the regulatory environment. The paper offers recommendations on how to support local manufacturers to make "Made in Africa" the first choice for African farmers and agro‐food processors. [ABSTRACT FROM AUTHOR]

The number of highly automated machines in the agricultural sector has increased rapidly in recent years. To reduce their fuel consumption, and thus their emission and operational cost, the performance of such machines must be optimized. The running gear–terrain interaction heavily affects the behavior of the vehicle; therefore, off-road traction control algorithms must effectively handle this nonlinear phenomenon. This paper proposes a linear parameter-varying model that retains the generality of semiempirical models while supporting the development of real-time state observers and control algorithms. First, the model is derived from the Bekker–Wong model for the theoretical case of a single wheel; then, it is generalized to describe the behavior of vehicles with an arbitrary number of wheels. The proposed model is validated using an open-source multiphysics simulation engine and experimental measurements. According to the validated results, it performs satisfactorily overall in terms of model complexity, calculation cost, and accuracy, confirming its applicability. [ABSTRACT FROM AUTHOR]

Weeds and crops engage in a relentless battle for the same resources, leading to potential reductions in crop yields and increased agricultural costs. Traditional methods of weed control, such as heavy herbicide use, come with the drawback of promoting weed resistance and environmental pollution. As the demand for pollution-free and organic agricultural products rises, there is a pressing need for innovative solutions. The emergence of smart agricultural equipment, including intelligent robots, unmanned aerial vehicles and satellite technology, proves to be pivotal in addressing weed-related challenges. The effectiveness of smart agricultural equipment, however, hinges on accurate detection, a task influenced by various factors, like growth stages, environmental conditions and shading. To achieve precise crop identification, it is essential to employ suitable sensors and optimized algorithms. Deep learning plays a crucial role in enhancing weed recognition accuracy. This advancement enables targeted actions such as minimal pesticide spraying or precise laser excision of weeds, effectively reducing the overall cost of agricultural production. This paper provides a thorough overview of the application of deep learning for crop and weed recognition in smart agricultural equipment. Starting with an overview of intelligent agricultural tools, sensors and identification algorithms, the discussion delves into instructive examples, showcasing the technology's prowess in distinguishing between weeds and crops. The narrative highlights recent breakthroughs in automated technologies for precision plant identification while acknowledging existing challenges and proposing prospects. By marrying cutting-edge technology with sustainable agricultural practices, the adoption of intelligent equipment presents a promising path toward efficient and eco-friendly weed management in modern agriculture. [ABSTRACT FROM AUTHOR]

Path-tracking control algorithms in agriculture typically focus on how to improve the trajectory-tracking performance of autonomous agricultural machinery, and the agricultural productivity is optimized in a two-layer way. The upper operational layer optimizes an optimal tracking trajectory with the best agricultural productivity, and the lower control layer—such as Nonlinear Model Predictive Control (NMPC)—receives this optimized tracking trajectory first, and then steers the vehicle to track this trajectory with high accuracy. However, this two-layer structure cannot improve the agricultural productivity at the control layer online, which makes the agricultural operation sub-optimal. In this paper, we focus on agricultural machinery operational efficiency, to represent agricultural productivity; in order to realize optimizing control to further improve agricultural machinery operational efficiency, a new path-tracking control algorithm, named Efficiency-oriented Model Predictive Control (EfiMPC), is proposed. EfiMPC is intrinsically a nested structure, which can consider the global performance of the whole system defined in the operational layer—like the agricultural machinery operational efficiency considered in this paper—in the control layer online; thus, the agricultural machinery operational efficiency can be improved during the farming operation. An unreachable tracking point, denoted as the pseudo-point, has been proposed, to indicate the agricultural machinery operational efficiency objective in a receding horizon fashion; EfiMPC can utilize this pseudo-point to realize the optimizing control online. A simulation case study was used to test the superiority of the proposed EfiMPC algorithm, and the results show that, compared with the traditional NMPC algorithm, the agricultural machinery operational efficiency realized by EfiMPC was improved by 8.56%; thus, the effectiveness of the EfiMPC has been demonstrated. [ABSTRACT FROM AUTHOR]

To improve the speed regulating characteristics of continuously variable transmission for agricultural machinery, in order to meet the engineering and technical requirements of precision agriculture and intelligent agriculture, the paper researches and proposes a method combining the analysis of speed regulating characteristics, regression-based correction, and the improved particle swarm optimization (I-PSO) algorithm. First, the paper analyzes the degree of deviation between the linearization degree and the theoretical value of the speed regulating characteristics of the variable-pump constant-motor system of agricultural machinery according to the measurement results of the bench test. Next, the paper corrects the speed regulating characteristics and compares the regression results based on four models. Finally, the paper proposes a design method for the expected speed regulating characteristics of agricultural machinery and it completes the optimization of speed regulating characteristics and the matching of transmission parameters with the I-PSO algorithm. Results indicate that the speed regulating characteristics of the variable-pump constant-motor system show high linearization (with a coefficient of determination of 0.9775). The theoretical and measured values of the speed regulating characteristics have a certain deviation (with a coefficient of determination of 0.8934). Therefore, correcting the speed regulating characteristics of the variable-pimp constant-motor system is highly necessary. In addition, the second reciprocal function model proposed has the highest correction precision (with a coefficient of determination of 0.9978). The I-PSO algorithm is applicable to the design and application of hydro-mechanical continuously variable transmission (HMCVT) for agricultural machinery. The new method proposed can improve the HMCVT's speed regulating characteristics efficiently and quickly. It also ensures that the speed regulating characteristics are highly consistent with the expected design characteristics (with a mean error of 1.73%). Thus, the research offers a theoretical direction and design basis for the research and development of continuously variable transmission units in agricultural machinery. [ABSTRACT FROM AUTHOR]

The maize field environment is complex. Weeds and maize have similar colors and may overlap, and lighting and weather conditions vary. Thus, many methods for the automated differentiation of maize and weeds achieve poor segmentation or cannot be used in real time. In this paper, a weed recognition model based on improved Swin-Unet is proposed. The model first performs semantic segmentation of maize seedlings and uses the resulting mask to identify weeds. U-Net acts as the semantic segmentation framework, and a Swin transformer module is introduced to improve performance. DropBlock regularization, which randomly hides some blocks in crop feature maps, is applied to enhance the generalization ability of the model. Finally, weed areas are identified and segmented with the aid of an improved morphological processing algorithm. The DeepLabv3+, PSANet, Mask R-CNN, original Swin-Unet, and proposed models are trained on a dataset of maize seedling images. The proposed Swin-Unet model outperforms the others, achieving a mean intersection over union of 92.75%, mean pixel accuracy of 95.57%, and inference speed of 15.1 FPS. Our model could be used for accurate, real-time segmentation of crops and weeds and as a reference for the development of intelligent agricultural equipment. [ABSTRACT FROM AUTHOR]

The development of modern technology has brought progress to the agricultural sector. Previously, farming was carried out using traditional methods, resulting in lower crop production. Now the world is faced with various problems, there are challenges such as climate fluctuations and increasing human population. This problem causes food needs to increase drastically, so adopting Industry 4.0 technology in the agricultural sector is necessary. Artificial Intelligence (AI) and Internet of Things (IoT) are part of industrial technology advances 4.0 that can be applied to modern agriculture. This paper reviews several AI technologies used in the agricultural sector, such as Fuzzy Logic (FL), Artificial Neural Network (ANN), Machine Learning (ML), Deep Learning (DL), Genetic Algorithm (GA), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Decision Support System (DSS). The application form of integration between AI and IoT is divided into several categories: soil monitoring, agricultural irrigation, fertilizer spraying, pest and plant disease control, harvesting, forecasting, and yield monitoring. This review paper was created to provide a comprehensive overview of modern agriculture integrating AI and IoT. This form of application makes it possible to predict the future of agriculture so that it can manage resources more efficiently and run autonomously. This review aims to analyze and explore the latest developments in integrating AI and IoT in agricultural equipment in the period 2019 to 2023. Thus, it is hoped that this article can provide in-depth insight into future agricultural technology advances. [ABSTRACT FROM AUTHOR]