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Most of the current crop row detection algorithms focus on extracting crop canopy rows as location information. However, for some high-pole crops, due to the transverse deviation of the position of the canopy and roots, the agricultural machinery can easily cause the wheel to crush the crop when it is automatically driven. In fact, it is more accurate to use the crop root row as the feature for its location calibration, so a method of crop root row detection is proposed in this paper. Firstly, the ROI (region of interest) of the crop canopy is extracted by a semantic segmentation algorithm, then crop canopy row detection lines are extracted by the horizontal strip division and the midpoint clustering method within the ROI. Next, the Crop Root Representation Learning Model learns the Representation of the crop canopy row and crop root row to obtain the Alignment Equation. Finally, the crop canopy row detection lines are modified according to the Alignment Equation parameters to obtain crop root row detection lines. The average processing time of a single frame image (960 × 540 pix) is 30.49 ms, and the accuracy is 97.1%. The research has important guiding significance for the intelligent navigation, tilling, and fertilization operation of agricultural machinery. [ABSTRACT FROM AUTHOR]

The deployment of multi-agent systems (MASs) is widely used in the fields of unmanned agricultural machineries, unmanned aerial vehicles, intelligent transportation, etc. To make up for the defect that the existing PDE-based results are overly idealistic in terms of system models and control strategies, we study the PDE-based deployment of clustered nonlinear first-order and second- order MASs over a finite-time interval (FTI). By designing special communication protocols, the collective dynamics of numerous agents are modeled by simple fist-order and second-order PDEs. Two practical factors, external disturbance and Markov switching topology, are considered in this paper to better match actual situations. Besides, T-S fuzzy technology is used to approximate the unknown nonlinearity of MASs. Then, by using boundary control scheme with collocated measurements, two theorems are obtained to ensure the finite-time H∞ deployment of first-order and second-order agents, respectively. Finally, numerical examples are provided to illustrate the effectiveness of the proposed approaches. [ABSTRACT FROM AUTHOR]

Based on the monthly panel data of China's imports of agricultural machinery products from 2016–2022, this paper uses a multi-period double-difference model to assess the impact of China's imposition of counter-tariffs on China's imports of agricultural machinery in the context of U.S.–China trade friction. It is found that China's implementation of counter-tariffs significantly reduces China's imports of agricultural machinery products from the U.S. and significantly increases imports from 16 other countries, but the trade diversion effect is lower than the trade suppression effect. Mechanism analysis finds that China–U.S. trade friction affects the technological innovation capacity of agricultural machinery enterprises and the degree of uncertainty in the Chinese economy, which in turn affects China's agricultural machinery imports. Heterogeneity analysis finds that China–U.S. trade friction has a more significant inhibitory effect on seeding and planting and fertilizing machinery, drainage, irrigation, and water lifting machinery, and other machinery imported from the U.S., and a more significant diversionary effect on agricultural primary processing machinery and harvesting machinery imported from 16 other countries. The imposition of countervailing tariffs mainly affected imports of complete machinery products rather than machinery spare parts. [ABSTRACT FROM AUTHOR]

In the complex and harsh environment of agriculture, rolling bearings, as the key transmission components in agricultural machinery, are very prone to failure, so research on the intelligent fault diagnosis of agricultural machinery components is critical. Therefore, this paper proposes a new method based on SVD-EDS-GST and ResNet-Vision Transformer (ResViT) for the fault diagnosis of rolling bearings in agricultural machines. Firstly, an experimental platform for rolling bearing failure in agricultural machinery is built, and one-dimensional vibration signals are obtained using acceleration sensors. Next, the signal is preprocessed for noise reduction using singular value decomposition (SVD) combined with the energy difference spectrum (EDS) to solve for the interference of complex noise and redundant components in the vibration signal. Secondly, generalized S-transform (GST) is used to process vibration signals into images. Then, the ResViT model is proposed, where the ResNet34 network is used to replace the image chunking mechanism in the original Vision Transformer model for feature extraction. Finally, an improved Vision Transformer (ViT) is utilized to synthesize global and local information for fault classification. The experimental results show that the proposed method's average accuracy in rolling bearing fault classification for agricultural machinery reaches 99.08%. In addition, compared with SVD-EDS-GST-CNN, SVD-EDS-GST-LSTM, STFT-ViT, GST-ViT, and SVD-EDS-GST-ViT, the accuracy rate was improved by 3.5%, 3.84%, 4.8%, 8.02%, and 0.56%, and the standard deviation was also minimized. [ABSTRACT FROM AUTHOR]

This paper takes the frame as the research object and explores the vibration characteristics of the frame to address the vibration problem of a 1-MSD straw-crushing and residual film recycling machine in the field operation process, and an accurate identification of the modal parameters of the frame is carried out to solve the resonance problem of the machine, which can achieve cost reduction and increase income to a certain extent. The first six natural frequencies of the frame are extracted by finite element modal identification and modal tests, respectively. The rationality of the modal test results is verified using the comprehensive modal and frequency response confidences. The maximum frequency error of modal frequency results of the two methods is only 6.61%, which provides a theoretical basis for the optimal design of the frame. In order to further analyze the resonance problem of the machine, the external excitation frequency of the machine during normal operation in the field is solved and compared with the first six natural frequencies of the frame. The results show that the first natural frequency of the frame (18.89 Hz) is close to the external excitation generated by the stripping roller (16.67 Hz). The first natural frequency and the volume of the frame are set as the optimization objectives, and the optimal optimization scheme is obtained by using the Optistruct solver, sensitivity method, and grey correlation method. The results indicate the first-order natural frequency of the optimized frame is 21.89 Hz, an increase of 15.882%, which is much higher than the excitation frequency of 16.67 Hz, and resonance can be avoided. The corresponding frame volume is 9.975 × 107 mm3, and the volume reduction is 3.46%; the optimized frame has good dynamic performance, which avoids the resonance of the machine and conforms to the lightweight design criteria of agricultural machinery structures. The research results can provide some theoretical reference for this kind of machine in solving the resonance problem and carrying out related vibration characteristics research. [ABSTRACT FROM AUTHOR]

Reducing cumbersome mechanical control components is one of the trends of modern agricultural machinery towards a high degree of automation. Whether the control components of sugarcane harvesters can gradually be replaced by central control screens, similar to car cabins, is an unresolved question. At the level of human machine interaction, this involves comparing the efficiency between joystick and touch screen control. This paper conducts a simulated experiment to compare and study the efficiency and user experience of joystick and touch screen control in adjusting the topper and base cutter heights of sugarcane harvesters, aiming to provide a reasonable basis for the design of control interfaces in sugarcane harvester cabins. The electromyographic signals, experiment duration, and subjective cognitive evaluations of participants in both the topper and base cutter groups were analyzed. The results showed that the efficiency, learnability, and ease of use of different control methods varied under different operational tasks. For the topper that corresponds to the operating behavior and height transformation in real time, joystick control demonstrated superior ease of use and operational efficiency compared to touch screen control, with weaker learnability. There was no discernible difference in muscle activation levels between the two control methods. Consequently, joystick control is deemed more suitable for the height adjustment of the topper. Regarding the base cutter with non-real-time mapping of operating behavior and height changes, no significant disparity in ease of use and learnability was observed between the two control methods. Touch screen control yielded lower muscle activation levels and garners higher overall subjective cognitive scale ratings. Thus, touch screen control is considered more suitable for base cutter height adjustment. Lastly, the paper proposes the optimal combination of software and hardware for control components in sugarcane harvester cabins, and provides an objective and multidimensional experimental analysis method for future research on similar human machine interaction interfaces. [ABSTRACT FROM AUTHOR]

Effective succession planning is a critical aspect of leadership continuity and organizational success. This article delves into the succession planning strategies of two prominent agricultural machinery companies, John Deere and Titan Machinery, providing insights into their distinctive approaches during leadership transitions. John Deere, a renowned global leader in agriculture and related services, employed a strategy that centred around continuous improvement and the development of leaders capable of transmitting their knowledge to others. Their introduction of the “teachable point of view” (TPOV) and the organization of Leadership EDGE events exemplify their commitment to nurturing a culture of leadership development. In contrast, Titan Machinery adopted a strategy that emphasized experience and continuity. With Bryan Knutson, a seasoned executive within the organization, taking over as CEO, the company focuses on operational excellence, expansion, and an unwavering commitment to customer service. The outcomes of these strategies include seamless leadership transitions, a focus on leadership development, the preservation of organizational culture, a commitment to operational excellence, and the introduction of fresh strategic visions for future growth. The focus is on examining the strategies, approaches, and outcomes of their succession plans, with an emphasis on commonalities and differences between the two companies. The paper aims to provide insights into effective succession planning practices within the context of these organizations. [ABSTRACT FROM AUTHOR]

In this paper, we propose a statistical model to describe the behaviour of failure times associated with groups of repairable systems hierarchically represented, under a competing risks framework, considering the existence of unobserved heterogeneity that acts individually on systems of each group, as well as the possibility of imperfect repairs whose initial failure rate is in the form of the power law. In this context for the unobserved heterogeneity in the groups, we consider the multiplicative frailty. To illustrate the use of the proposed model, we consider a database with the failures of 38 agricultural machines categorized into five different groups. We understand that the tractor fleet corresponds to the farm's agricultural system, therefore the need for intervention in this system occurs with the failure of any unit, individually, in a serial structure, of competing risks.e On the other hand, the understanding of the time in which all the machinery that makes up the fleet will have required some intervention is obtained by analysing the results under a parallel structure. [ABSTRACT FROM AUTHOR]

The FCDET is considered as a promising and advanced agricultural machinery due to its convenience and controllability. To enhance the control performance of the FCDET unit dynamic system, understanding the accurate position of the center of gravity (CoG) of the tractor unit in longitudinal motion and the operating environment is essential. In this paper, we proposed a real-time estimation method of CoG position of longitudinal motion for FCDET plowing and transportation conditions. CoG height and longitudinal axis distance distribution coefficients are used as state estimation variables, and the data obtained from displacement and velocity sensors are used as state observation variables, establishing a discrete system state space model and proving the observability. The proposed Derived UKF algorithm introduces the Sage-Husa adaptive noise equation within the Square-Root UKF framework to address the non-positive definite matrix problem. The research shows that the precise CoG longitudinal position effectively reflects changes in operational pavement load. The Derived UKF algorithm exhibits high state estimation accuracy in plowing and transportation conditions, with mean absolute errors of 14.2% and 5.0% of the state amplitude, respectively. This study supports the practical application of real-time state parameter estimation algorithms in controlling advanced agricultural machinery distributed drive systems. [ABSTRACT FROM AUTHOR]

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]

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]

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]

The purpose of this paper is to investigate and examine the role of waqi'iyah networking power (WNP) in mediating the relationship between levels of cross functional involvement (LCFI), harmony of cross functional relations (HCFR), and quality of cross functional information (QCFI) with marketing performance (MP). This paper selects 267 manager of agricultural equipment and machinery service business (UPJA) and tested the regression relationship of the five constructs using SEM with AMOS 24.0. Findings show LCFI, HCFR and QCFI as an important antecedent for WNP and then have a positive effect on MP. It shows WNP has an important role as mediating in the relationship of LCFI, HCFR and QCFI with MP. By examining the relevant literature cross-functional integration, networking power, waqi'iyah, and marketing performance This paper offers a unique analysis of the roadmap of levels of cross functional involvement, harmony of cross functional relations, and quality of cross functional with marketing performance. [ABSTRACT FROM AUTHOR]

The importance of realistic financial reporting depends on the consideration of numerous factors by the decision maker. In this paper, the authors focused on a comprehensive analysis of agricultural machinery in use by individual farms and medium-sized agricultural enterprises. The main conclusion reached by the authors would be that there are significant differences regarding the use of agricultural machinery in relation to its age, value, fuel consumption and its maintenance (p<0.0005*) in relation to the two forms of organizing agricultural production. Medium-sized agricultural enterprises use younger, more expensive agricultural machinery, as well as the fact that the owner's satisfaction with it is somewhat lower with lower fuel consumption and lower maintenance costs of agricultural machinery. The value of agricultural mechanization can be predicted both for individual farms based on the mentioned factors (F=262.901, p<0.0005) and for medium-sized agricultural enterprises (F=161.229, p<0.0005). [ABSTRACT FROM AUTHOR]

In a globalizing world, the borders on the map have started to disappear economically and customers in the world economy expect high levels of quality from their suppliers. Only companies that can respond to customer expectations in the most accurate way will be able to survive in the future. One of the main goals of today's companies is to offer the best quality product at the most affordable price in line with the needs of customers. At this point, the Six Sigma approach is one of the most effective methods that companies can try. In this study, by applying the Six Sigma methodology, the root causes of PE (Polyethylene) film breaks in plastic bags were investigated by taking plastic bag production machines, processes and control steps as an example, necessary measurements were made by considering the production lines in a sample company and improvements were made in the production process by analyzing with Minitab program. At the end of the study, 24.012 TL/year savings were achieved by reducing downtime due to film breaks in the PE Film line, and 13.938 TL/year savings were achieved financially by reducing the amount of waste. In this way, the plastic bag industry benefited. In six sigma projects, it has been determined once again that the only element that is as important as making the change is that all the personnel of the company should keep up with and support this change. [ABSTRACT FROM AUTHOR]

Introduction: The impact of farm machinery outsourcing services (FMOS) on grain production has been extensively studied in the literature. However, whether FMOS would adjust cropping structures and ensure food security is little known. To address this research gap, we analyzed the impact of FMOS on the cropping structure adjustment and grain production of farmers. Methods: We estimate the impact of FMOS on grain production by using data collected from a survey of 3,863 wheat farmers in China. We employ the Extended Regression Model (ERM) to address selectivity bias. Results and discussion: The empirical results show that FMOS can adjust the cropping structure and increase the proportion of household food cultivation. Among all segments of FMOS, fertilizer application services drive the increase in grain acreage more significantly. We also find that large-scale households prefer to self-purchase farm machinery rather than FMOS, leading to an insignificant impact of FMOS on cropping structure. Therefore, in the process of promoting the development of FMOS, we should focus on the coordinated development of various types of FMOS, strengthen the popularization and application of fertilization services, and formulate a differentiated subsidy policy for heterogeneous farmers in terms of scale to better play the role of agricultural machinery in promoting grain production. [ABSTRACT FROM AUTHOR]

The purpose of the research is to determine the specific capital costs of carrying out agricultural work with the farmers'own agricultural machines compared to the costs of agricultural services. A stratified sample based on economic size was used, consisting of 60 farms specialised in large crops from the NE and SE development regions of Romania. The novelty elements of this research consist in the fact that the analysis of the efficiency of the use of agricultural machinery uses a shadow cost--the opportunity cost--to determine the specific income of the use of agricultural machinery. The results show that small farms spend more than twice as much to carry out agricultural work with their own agricultural machinery. The average expenses per ha specific to the agricultural works carried out by farmers with their own agricultural machines recorded an average of 376.3 euro/ha for farms with a size smaller than 100 thousand SO and 186.5 euro/ha for farms with a size greater than 700 thousand SO. For a representativeness level of 69.3%, farms with a size smaller than approx. 578 thousand SO register losses if they carry out agricultural work with their own agricultural machines. This threshold can be appreciated on an area of approx. 854 ha cultivated with grain corn, common wheat, and rapeseed in equal proportions. Using the rationales and cost analysis models presented can help farmers make rational investments and strengthen informed demand in the agricultural services market. [ABSTRACT FROM AUTHOR]

The paper is dedicated to studying the structural and mechanical properties of ceramic coatings obtained by micro-arc oxidation (MAO) using a cathodic-anodic mode in alkali-silicate, borate, and alkali-silicate-phosphate electrolytes from an experimental sample of compositions. Coatings formed using borate electrolyte have the best mechanical properties, resulting in the improvement of wear resistance of an unprotected aluminum surface by 2.1 to 3.1 times, depending on the content of borate ions in the utilized electrolyte. Ceramic MAO coatings offer a potential for use at the Russian repair and maintenance enterprises, which are engaged in the repair and restoration of worn aluminum and steel materials used in critical components of agricultural machinery. [ABSTRACT FROM AUTHOR]

Extreme environments, like the steep olive groves in Liguria, Italy, cannot be reached by tractors and large-sized devices. This paper describes a small, tracked elevation platform able to lift the farmers close to the branches for harvesting or pruning. The vehicle moves thanks to tracks. The elevation platform, having no motors and no sensors, is powered by hand. A prototype of the forest lift has been tested on the field. The forest lift has a maximum elevation of 2 m with a tilting (0° ＋ 30°) and rolling mechanism (－15° ＋ 15°) compensating for steep terrains. The iron prototype weighs 400 kg and is 3,160 mm tall, 2,000 mm long and 900 mm wide. [ABSTRACT FROM AUTHOR]

This correction notice addresses mistakes in a scientific publication on intelligent agricultural machinery. The corrections include errors in the description of the full-coverage path planning algorithm, parallel path sorting methods, curve generation methods, construction and testing of simulation and real vehicle test platforms, and establishment of multiple objective functions and coupling mechanisms. The authors state that these corrections do not impact the scientific conclusions of the publication. The references in the original publication have also been updated. [Extracted from the article]

In the current draft of the European Regulation on the sustainable use of plant protection products (COM 2022 305), it is established that all the member states have to set up an electronic register of application equipment in professional use. It will set out notification requirements for transfer of ownership and withdrawal of or return to use, and it will also record the inspection certificates. In this way, it will be possible to know the total number of application equipment units that have to be inspected. At the same time, the regulatory bodies will have information on the development of the inspection programme, because it will be possible to know how many units have already passed the inspection and when it has to be renewed. It is also important to know which are the units that have never been inspected and, therefore, they cannot be used for professional use. In Spain the registration of agricultural machines has been in place for more than 60 years. At the beginning, only the machines that have to drive on the roads, had to be in this official list, before they got the licence plate by the traffic authorities. That means all tractors, combines and other selfpropelled and towed agricultural machines (tillage equipment, seeders, mowers, balers, etc.). When the inspection of application equipment in use became mandatory, according to the Directive for the sustainable use of pesticides (2009/128/CE), the registration of all kind of application equipment, both new and in use was also compulsory. For the machines already in use, a special procedure was established to make their registration straightforward. At present, information on the current situation of the inspection programme is available on a digital database. Although there are some constraints, like some old, not used machines which have not been withdrawn, a great deal of useful, detailed information can be obtained from this database. Based on this, the inspection programme can be fine-tuned, in order to get all the sprayers inspected, which is the main objective of the current European Directive. In this paper, the set-up and present situation of the Spanish official registration of application equipment will be presented, because hopefully it can be useful for the establishment of similar tools in other member states. [ABSTRACT FROM AUTHOR]