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Adverse weather events can significantly compromise the availability and economics of a wind farm. This paper focuses on rotor icing detection, which constitutes a major challenge in wind farm operation. When ice accumulates on wind turbine blades, it causes substantial generation losses, operational disruptions, and safety hazards to the personnel, assets, and equipment in a wind farm. Alerts about early signs of rotor icing can assist operators in proactively initiating icing mitigation measures. To this end we propose a machine-learning-based framework that effectively learns the unique signatures of icing events. The framework effectively extracts salient features by condensing the multivariate turbine sensor data into a small-sized subset of information-rich descriptors. Those, along with power-curve-derived features, are used to train a deep-learning-based model that flags icing events and estimates icing probabilities. We also propose a new loss measure, called the icing power loss error (IPLE), that realistically quantifies the expected icing-related power losses. Our experiments show that the proposed framework achieves up to 96.4% accuracy in flagging icing events, while keeping the number of false alarms at minimum. When compared to prevalent data-driven benchmarks, up to 18.7% reduction in power loss estimation error is realized. [Display omitted] • A machine-learning-based approach for rotor icing detection is proposed. • Operational data is condensed into a tensor-based set of predictive features. • Combined with power-curve-derived inputs, an icing classification model is trained. • A loss measure is proposed to rigorously quantify icing-related power losses. • Two icing monitoring modes are presented for diagnostic and prognostic settings. [ABSTRACT FROM AUTHOR]

• A multi-layer perceptron model for straight-turn binary classification training using K-Fold cross validation is proposed. • A convolutional neural network-based model is proposed to proform turning classification between Omega turn and T turn. • The proposal of line and turn segmentation algorithms and turn type recognition algorithms provides new methods for estimating the efficiency of agricultural machinery operations. GPS technology is an indispensable technique in "precision agriculture". In the field of path planning, the evaluation of planned trajectories is based on GPS data. However, due to the unstructured operating environment, the existing methods for evaluating the efficiency of actual operating trajectories are not yet fully developed. In the context of agricultural machinery GPS trajectories, headland turning of the field is considered a non-working segment, which related to evaluation standard for trajectory planning efficiency in many studies. This paper proposes a processing method that evaluates GPS trajectories efficiency. A turning extraction algorithm based on changes in the angle of the agricultural machine's driving direction is proposed. The turning parts of the raw GPS data are preliminarily extracted, and a method for constructing a turning extraction dataset is presented. Then a multi-layer perceptron model for straight-turn binary classification training using K-Fold cross validation is designed and applied, which can implement robust segmentation of agricultural machinery trajectory points in actual environment. Two methods for building different turning classification datasets are proposed, and turning classification is performed using two deep learning models. A convolutional neural network-based model classifies headland turnings by their normalized sequences' visualization images, while a long short-term memory network-based model utilizes the trajectory point sequences. The CNN-based model performs relatively better than the LSTM-based model. Finally, some commonly used GPS trajectory efficiency evaluation standards in the field of agricultural machinery path planning are combined to evaluate the efficiency of actual GPS trajectories of agricultural machinery. [ABSTRACT FROM AUTHOR]

• An improved model was used to identify high working intensity load segments. • The LSTM-based model is composed of a multi-head attention module and ConvLSTM. • The accuracy and F1-score of the model are 97.69% and 97.83%, respectively. • The model can greatly reduce calculation time by up to 15.82%. • The failure rate of identifying high intensity load segments is less than 3%. In order to solve the problems of serious redundancy of existing load spectrum data, unclear identification of load segments corresponding to high working intensity, and failure of load spectrum interception to meet the requirements of heavy load conditions, this paper proposes an MHA-ConvLSTM (Multi-head attention Convolutional LSTM) network model for identifying high working intensity load segments of the tractor load spectrum. The deep learning model integrates a multi-head attention mechanism and ConvLSTM network as the core, keeping the temporal order of continuously changing load sequences as the basic principle, deeply mining the local features contained within the small range load of dynamically changing load, and strengthening the matching relationship of the intrinsic features in long distance and large data volume load. This research selects rotary tillage as the verification condition, builds the multi-sensor test system to carry out the working load test, and takes the tractor rotary tillage load spectrum data as the validation object. The analysis shows that the accuracy and F1-score of the MHA-ConvLSTM model reach 97.69% and 97.83%, respectively, and the operation time is only 0.5289 s, which is 15.82% faster than LSTM. In addition, the model in this paper was used to identify 399 load segments with high workload and high work intensity, and 388 load segments were successfully verified with an error rate of less than 3%. This paper provides a new technical solution for applying the agricultural equipment load spectrum. [ABSTRACT FROM AUTHOR]

• All-Terrain Vehicles (ATVs) are linked to farming fatalities and injuries every year. However, despite evidence that wearing a helmet reduces the risk of serious injury, few farmers wear them. • This study assessed farmer attitudes to ATV helmets using the Michie COM-B model. • Participants reported a range of barriers to helmet wearing including the perception that helmets could interfere with work activities, social stigma and perceived exemption from harm. • Key interventions are suggested using the Michie behaviour change wheel, including modelling, raising awareness and training. All-Terrain Vehicles (ATV) are a popular piece of farming machinery but are linked to many fatalities and injuries every year. Despite evidence that ATV helmets reduce the risk of serious or fatal injury, research suggests that few farmers wear them. The aim of this study was to explore farmer attitudes toward ATV helmets, using the COM-B model as a framework to identify key barriers and enablers of helmet use and suggest potential interventions to increase helmet use in agriculture. Methods : A mixed-methods online survey featuring quantitative and qualitative questions was used to explore key attitudinal factors relevant to farmer helmet wearing. A total of 211 UK and Irish farmers were recruited, including farm owners, managers, workers, and contractors. Results : Personal exemption from risk, emotional benefits, cognitive barriers, and guideline prompts were all found to be significant predictors of farmer helmet wearing. Key categories within the qualitative data indicated that helmet properties, risk perception, farming culture, and the farming environment could all function as barriers to helmet wearing. Suggested enablers of helmet use included increasing helmet availability and enhancing awareness of the consequences of ATV accidents. Conclusions : The key barriers and enablers presented within this paper highlight the relevance of capability (helmet design, time pressure), opportunity (social norms), and motivation (perceived control of risk) when considering farmer helmet wearing behaviors. In order to encourage farmers to wear helmets it will be necessary to design specific interventions using the behavior change wheel. Practical applications : Key interventions to prompt helmet use include the inclusion of more cues to prompt helmet wearing (e.g., stickers on the ATV), enhanced training and education, and modelling (e.g., key farming figures shown to wear helmets in the media). [ABSTRACT FROM AUTHOR]

[Display omitted] • Tillage involves soil mechanical disturbance, in order to improve its structure. • Agricultural production directly depends on the tillage quality. • Soil quality critically influence environment and the efficiency in crop production. • Present paper is focused to modelling the soil particle size distributions. • Important task of soil tillage is to reduce the soil compaction to minimum. Tillage involves various complex mechanisms of mechanical disturbance of the soil, aimed to advance its physical structure by creating relevant conditions for growing crops Quality regulation of every specific tillage conception is critical for the soil physical parameters. Thus, the main goal of this work was to review approaches to the analysis of the quality of soil cultivation techniques. Relationships have been established between the basic operating parameters of the mechanization applied and the consequent particle size spreading of the soil. Supplementary, statistical indices of the distribution pattern of soil particles, based on higher-order dimensionless statistical parameters were estimated. The observed mass distributions of soil aggregates was the result of extremely complex and superimposed influences of soil type, field conditions, crop structure, equipment used, etc. At this stage, an accurate forecast of the impact of soil tillage techniques on the changes in composition and size distribution of soil aggregates based on mathematical modelling is very difficult. Further research will be continued to include wider range of soil types, agricultural mechanization systems and their operational regimes. [ABSTRACT FROM AUTHOR]

The increased focus on reducing greenhouse gas emissions and decreasing dependence on fossil fuels has led to heightened interest in alternative fuels. Given the critical role of agriculture in both the consumption and production of energy, understanding the factors that influence farmers' decisions to purchase an alternative fuel tractor is paramount. This study aims to identify constructs that influence German farmers' intentions to purchase alternative fuel tractors, applying the Unified Theory of Acceptance and Use of Technology (UTAUT) framework. An online survey conducted in 2022 yielded 141 usable responses from German full-time farmers, and the UTAUT model was estimated using Partial Least Squares Structural Equation Modelling (PLS-SEM). Farmers perceive alternative fuel tractors as particularly suitable for low-energy farm and field work, but lower driving ranges relative to conventional diesel tractors are the main barrier to adoption. General performance expectancy and social influence emerged as the most potent drivers of farmers' intentions to purchase alternative fuel tractors. The paper contributes original insights into the factors influencing technology adoption in agriculture and provides practical implications for policymakers and agricultural machinery manufacturers. The results guide strategies to encourage alternative fuel tractor adoption and suggest directions for future sustainable agriculture research. • First research focussing on the intention to buy an alternative fuel tractor • Identification of barriers to the adoption of alternative fuel tractors • UTAUT model for farmers' intention to buy an alternative fuel tractor • Performance expectancy predicts the intention to buy an alternative fuel tractor. • Results are of interest for policy-makers and agricultural machinery manufacturers [ABSTRACT FROM AUTHOR]

• The first model based on GRU to reconstruct missing points in agricultural machinery trajectories is proposed. • A bidirectional feature extraction module is proposed to capture bidirectional information of the missing trajectory points. • A lightweight model is customized for massive agricultural machinery trajectory data. • The reconstruction deviation exceeds the Beidou GNSS positioning accuracy. Agricultural machinery trajectory data often encounters the phenomenon of missing trajectory points, and reconstructing these missing trajectory points is crucial for subsequent researches that require complete and high-quality agricultural machinery trajectory data. In this paper, we propose a new method called Fast-TRGRU to accurately and quickly reconstruct missing points in the trajectory of agricultural machinery. First, we design a novel data preprocessing process to improve the quality of raw data. Next, we introduce a bidirectional feature extraction module (BFE) to simultaneously utilize the bidirectional adjacent subtrajectory information of missing trajectory points. Then, we use gated recurrent unit (GRU) to extract spatiotemporal correlation features from time series data representing agricultural machinery trajectories to reconstruct trajectories. Finally, we reduce the training time of the model by adjusting the structure of the model in the GRU model. To verify the effectiveness of the model, we conducted experiments using 125 real agricultural machinery trajectory samples including 1,111,813 points provided by the Key Laboratory of Agricultural Machinery Monitoring and Big Data Applications of the Ministry of Agriculture and Rural Affairs. The experimental results show that when the trajectory missing rate is 10 %, the average reconstruction deviations of the model in longitude and latitude are only 1.80 m and 1.95 m, respectively. When the trajectory missing rate is 60 %, the average reconstruction deviation of the model in both longitude and latitude is still less than 4 m, which is higher than the positioning accuracy of the Beidou Navigation Satellite System. The Fast-TRGRU can provide complete and high-quality data for the analysis of agricultural machinery trajectories, effectively assisting in the research of optimizing agricultural machinery operation paths and evaluating agricultural machinery operation efficiency. [ABSTRACT FROM AUTHOR]

Cutting blades for harvesting applications are used in a variety of agricultural machines. These parts are in contact with highly abrasive lawn clippings and often wear out within hours which results in high expensive re-sharpening maintenance. This paper relates to locally graded steel materials as semi-finished products for self-sharpening cutting blades. As these materials comprise a layered structure consisting of a hard, wear resistant layer and a relatively softer layer which is lesser wear resistant. As the cutting blade is subjected to wear conditions, the less wear resistant layer wears faster than the relatively more wear resistant harder layer. As a result, the more wear resistant layer is always exposed at the tip of the blade and determines its geometry. As the hard layer’s thickness is almost the same within the part, the cutting edge remains unchanged and provides constant performance in service. This paper displays suitable designs for such cutting blades, test setups for its characterization and capable manufacturing techniques for making locally graded steel materials as semi-finished products. [ABSTRACT FROM AUTHOR]

The development of agriculture modernization leads to a flourish in high-tech equipment agricultural production, IoT-based greenhouse seedling production is the most presentative one. However, for the great majority of the seedling production factories, the data stream collected from IoT sensors only be applied for display, failing to assist the resource utilization and order target completion. To fill this gap, this paper provides an IoT-based system implementation architecture and a hierarchical control method for greenhouse seedling production, which make use of history monitoring data, real-time monitoring data, and seedling growth model, aiming at achieving the target growth along the predefined timeline as well as saving the additional energy cost. The proposed method is easy to implement in practical production and offers a valuable reference to other precise agricultural projects. [ABSTRACT FROM AUTHOR]

• SSPM can be installed on tractors computers to increase safety of their operation. • This program estimates critical slope angles of mobile agricultural machines. • The algorithm combines principles of theoretical mechanics and analytical geometry. • Output results are represented in two manners: graphic and numerical. • The algorithm is verified by experiment with a tractor model. This paper presents an analytical algorithm with appropriate software specified for the approximation of the allowed critical slope of the solid flat terrain that guarantees static and/or dynamic stability of the specified self-propelled agricultural machines and their aggregates. This algorithm assumes machine as a rigid body, having 3 or 4 contact points (defined by wheels or crawlers), under uniform motion at different constant velocities and radii of curvature trajectories. Using this algorithm, based on the principles of theoretical mechanics combined with 3D analytical geometry, the computer program SSPM (s tability of the s elf- p ropelled agricultural m achines) has been coded. This software is intended to facilitate the analysis, comparison and optimization of different configurations of self-propelled agricultural machines in operation on horizontal and sloped flat terrains at constant velocities and radii of trajectory with respect to their static and dynamic stability. It calculates critical pitch and roll angles of the self-propelled machine and the maximum allowed slope of the flat terrain under the given conditions. The algorithm and the appropriate SSPM software were experimentally verified using the platform and low-scale tractor model. Average difference between calculated and experimental critical values of roll and pitch angles were about 4° [ABSTRACT FROM AUTHOR]

• A vibration control system based on intelligent sensor nodes was developed. • Each intelligent detection node can work independently or networking as IoT system. • The vibration isolation system can effectively reduce the engine vibration. Vibration of agricultural machinery not only affects the quality and effect of the operation, but also reduces the service life of machinery. A vibration detection and control system based on intelligent sensor nodes was developed to detect and control the frame vibration caused by high-power engine in agricultural machinery. Each intelligent detection node can work independently or networking as Internet of Things system. The effect of the vibration isolation system can be assessed by installing the test nodes at the measuring points of the two-stage vibration isolation system. In this paper, intelligent sensor nodes were used to reduce the vibration of an agricultural machinery engine. According to the optimized stiffness of the double-layer vibration isolation system, a test platform system is designed. The intelligent sensor nodes were used to assess the vibration spectrum characteristics of engine under various working conditions, and acceleration values of upper and lower measuring points of stage 1 and stage 2 isolators. The relationship between acceleration transfer rate and engine speed was analyzed before and after double-layer vibration isolation was used, and the vibration isolation effect of double-layer vibration isolation system is verified. The results show that the double-layer vibration isolation system can play a significant role in changing the stiffness of the whole system and avoiding the occurrence of coupling resonance. The transmission rate of vibration acceleration decreases from 0.94 to 0.54 at the engine speed of 2700 r/min, with a decrease of 42.3%. The results show that the proposed intelligent detection node is able to detect the engine vibration in agricultural machinery, and the proposed double-layer vibration isolation system can effectively reduce the engine vibration. [ABSTRACT FROM AUTHOR]

Fuel cell distributed drive electric tractor (FCDET) provided a novel pathway for the development of green agricultural machinery technology. However, FCDET faced problems such as low traction efficiency, short endurance, and high hydrogen consumption. Relevant studies have shown that reasonable and effective power allocation strategy can improve the efficiency and reduce the energy consumption of fuel cell systems. In this paper, a drive power allocation strategy based on adaptive multi-resolution analysis theory (AMRA) was proposed. The strategy could realize effective decoupling between various energy sources, reduce the problems of frequent start-stop and large power fluctuation for fuel cells. We started with an equivalent circuit model of the fuel cell system, a total efficiency solution model, an energy dissipation model, and a drive motor response model. The first-time reconstruction strategy was based on Tunable Q-factor Wavelet Transform (TQWT) to obtain a subsequence that responds to the oscillatory characteristics of the power signal. The second-time reconstruction strategy took the low-frequency subsequence through Variational Mode Decomposition (VMD) into a number of discrete sub-signals with special sparse properties. Meanwhile, the Sparrow Search Algorithm (SSA) was used to obtain the optimal combined values of the modal decomposition layers and the quadratic penalty factor in the VMD on real time. Finally, the second-time decomposed subsequences and sub-signals were reconstructed according to the frequency characteristics, and the reconstructed power signals were redistributed among the individual energy sources. In order to validate the proposed power allocation strategy, we used the ET504-H prototype as a research object. The power information of plowing and transportation operating conditions was collected in Mengjin test base of China YTO Group. The MATLAB/Simulink-PXI joint simulation platform was set up and the driving motor bench test was carried out in the New Energy Key Laboratory of Henan Province. The results show that the AMRA-based FCDET drive power allocation strategy can effectively improve the energy utilization of the fuel cell system and the economy of the whole tractor operating unit. Compared to the power-following strategy under plowing and transportation conditions, the average efficiency of the fuel cell system was improved by 8.3 % and 3.3 %, the equivalent hydrogen consumption was reduced by 35.6 % and 43.89 %, respectively. The average efficiency of the drive motor was improved by 2.06 % and 2.08 %, the total energy consumption was reduced by 3.73 % and 2.6 %, respectively. This study can provide a theoretical foundation and a novel technical approach for the development of FCDET control system. • The fuel cell system total efficiency solution model and the energy consumption model were established. • The AMRA based FCDET drive power allocation strategy is proposed. • A joint MATLAB/Simulink-PXI simulation platform was constructed for real-time validation. • The motor cooperative operation characteristics of the distributed drive system were tested. [ABSTRACT FROM AUTHOR]

• A goal point determination method is proposed for the problem of selecting the optimal path point by Pure Pursuit. • An evaluation function is established to select the optimal goal point based on tractor position prediction model. • The performance of the proposed path tracking algorithm is studied by field test. The development of precision agriculture requires the implementation of automatic navigation technology for agricultural machinery. Path tracking control as one of the key steps in automatic navigation technology has great research value. Pure Pursuit algorithm as a popular control algorithm for automatic navigation technology for agricultural machinery, the look-ahead distance is the key to the tracking effect, while the calculation of look-ahead distance has the problem that many influencing factors are difficult to be accurately described by mathematical expressions, which leads to the difficulty to select a suitable goal point tracking path. In order to solve the above problems, this paper proposes a path tracking algorithm for agricultural machinery based on the optimal goal point. The algorithm simulates the look-ahead behavior of the driver and searches for optimal goal point in the look-ahead area according to the evaluation function. The objective of the study is to minimize the lateral error and heading error to achieve the adaptive optimization of the goal point. Finally, the feasibility of the algorithm proposed in this paper was verified in simulations and bumpy field tests under various different conditions, with tracking errors reduced by more than 20% compared to the pure pursuit algorithm. The tracking accuracy is significantly improved. [ABSTRACT FROM AUTHOR]

Abstract: This paper proposes a new method, oriented to crop row detection in images from maize fields with high weed pressure. The vision system is designed to be installed onboard a mobile agricultural vehicle, i.e. submitted to gyros, vibrations and undesired movements. The images are captured under image perspective, being affected by the above undesired effects. The image processing consists of three main processes: image segmentation, double thresholding, based on the Otsu’s method, and crop row detection. Image segmentation is based on the application of a vegetation index, the double thresholding achieves the separation between weeds and crops and the crop row detection applies least squares linear regression for line adjustment. Crop and weed separation becomes effective and the crop row detection can be favorably compared against the classical approach based on the Hough transform. Both gain effectiveness and accuracy thanks to the double thresholding that makes the main finding of the paper. [Copyright &y& Elsevier]

Highlights • The scheduling of equipment under a sharing resource system is first studied. • The ALNS metaheuristics employing the idea of DE is developed to solve the problem. • The new destroy and repair methods have been developed as unique and effective. Abstract This paper presents the ALNS metaheuristics, employing the idea of DE to solve the mechanical harvester assignment and routing problem with time windows (HARPTW) to maximize the total area serviced by a mechanical harvester under a sharing infield resource system. The effective ALNS is designed to solve large-scale problems integrating the mechanical harvester assignment problem (HAP) and the mechanical harvester routing problem (HRP). The newly developed destroy and repair methods are unique and effective. Additionally, four new formulas have been developed to calculate the probability to accept the worse solution using linear and parabola functions instead of the exponential function that is used mostly in the literature. The numerical results show that the parabola function, which uses the information about the solution quality, outperforms all other proposed heuristics. This demonstrates that the proposed heuristics are very efficient and are not only useful for reducing the infield operations costs of small growers, but also for efficient management of the inbound logistics equipment and machinery of the sugarcane supply system. [ABSTRACT FROM AUTHOR]

Highlights • The maintenance network for agricultural machinery is defined. • A general framework is developed to select the service provider for maintenance network. • The integrated method of balanced score card and analytic network process is employed to analyze criteria. • The results of the case studies prove that the methodology is effective. Abstract Constructing maintenance networks is becoming an urgent requirement for ensuring the continuous reliable operation and fast response of agricultural machinery maintenance in busy farming seasons. According to dynamic characteristics, time variation, and the cooperativity of agricultural machinery cluster working, this paper proposes a service provider selection framework for the layout of dynamic maintenance network nodes. Furthermore, an integrated analytic network process (ANP) and balanced score card (BSC) service provider selection approach is provided to help construct the maintenance network. Concerning four determinants of BSC: service satisfaction, process capability, knowledge assets, and service cost, this method provides qualitative and quantitative indicators, four evaluative dimensions and a three-layer network evaluation system index. Moreover, this method is applied to maintenance services for agricultural machinery clusters working in cross-regional operations in China. The results indicate that process capability is the most important determinant for a service network, and the proposed method contributes to the ability of decision-makers to comprehend the complex relationship among factors during the service provider selection process. Meanwhile, this method improves the reliability of service provider selection during the construction of a maintenance network. [ABSTRACT FROM AUTHOR]

Highlights • Dynamic spatially distributed modelling of stored agro-material. • Complete storage model, containing fast (air) and slow (product-related) dynamics. • Decoupled and coupled CFD simulations, including controls (fan and hatches). • Model calibration-validation on data obtained from a large-scale storage facility. Abstract To maintain a high quality and limit storage losses of agro-materials in bulk storage, the dynamic interaction between climate in the facility and product needs to be better understood. In this paper, we present a (2-D) spatially distributed, dynamic full-scale bulk storage model. Also, model calibration and validation results using data from a full-scale potato storage facility are presented. The model predicts convection and diffusion of heat, mass and carbon dioxide as well as the heat, mass and carbon dioxide transfer between bulk and air, respiration and evaporation of the food product and the natural and forced convection in the storage facility. In the CFD model two actuators, a moving hatch and a fan, were successfully implemented. The validated model was used as a tool to investigate the cooling process within the full-scale storage facility. When applying a combination of under- and overpressure instead of the conventionally applied overpressure, a reduction of the temperature gradient in vertical direction and an increased temperature gradient of the temperature gradient in horizontal direction was obtained. A reduction on this horizontal temperature gradient was accomplished by adding an air channel near the roof. [ABSTRACT FROM AUTHOR]

Abstract: A new trend in the farming business is to replace conventional farming devices with computerized farming devices. Accordingly, numerous computer-based farming devices for logging, processing and exchanging data have recently been installed on moving farm machinery such as tractors. The exchange of data generally takes place between the devices and farming systems, mostly installed at the premises of farmers, contractors, advisory services, etc. In most cases, data exchange is based on farming data exchange standards and is bi-directional. Bi-directional data exchange allows different devices and systems to exchange data based on a predefined set of rules. In consequence, many hand-coded data exchange solutions have been developed in the farming business. Although efforts regarding incorporating data exchange standards have been made, their actual usage so far has been limited, due to the fact that hand-coded bi-directional data exchange solutions have proven themselves too complex to maintain. Maintenance need to occur as requirements change frequently and the existing data exchange techniques lack flexibility with regards to efficient requirements change management. In addition, requirements change results in managing low-level code rather than a high-level specifications. This paper presents a bi-directional data exchange solution in the farming business that is based on high-level specifications without any need for hand-coding. The proposed solution supports ISOBUS based bi-directional data exchange as well as efficient requirements change management through a graphical user interface. The paper also demonstrates the use of the proposed solution based on a farming case study and open source technologies. [ABSTRACT FROM AUTHOR]

Abstract The Fruit And Vegetable Energy (FAVE) model was developed to quantify the energy budget and fossil CO 2 emissions in this paper. The energy terms considered were fieldwork, irrigation, on-farm cooling, farm building maintenance, off-farm transport, and N fertilizer supply, with an estimate of farm machinery supply energy integrated with the farm fieldwork energy CO 2 emission intensity estimates. The 31 vegetable crops and 18 fruit crops identified in the socio-economic Canadian data base (CANSIM) were divided into six vegetable groups and five fruit groups, along with potatoes. The data provided by the 1996 Farm Energy Use Survey (FEUS) and the guidance provided by two grower focus groups were essential to the development of this model. Total energy use in Canada between 2007 and 2016 was 2.3 PJ by the vegetable industry and 2.32 PJ by the fruit industry. These included 0.77 PJ for fieldwork, 1.05 PJ for irrigation, 0.36 PJ for on-farm coolers, 0.67 PJ for farm building maintenance, 0.70 PJ for off-farm transport and 1.07 PJ for fertilizer supply. The FAVE model was used to determine the fossil CO 2 emission intensities of 12 individual crops. [ABSTRACT FROM AUTHOR]

Highlights • A novel approach to localize hot-spots in tubular welded joints is proposed. • Initial crack growth direction is also estimated. • The novel approach is validated by considering a welded T-joint of an agricultural sprayer H component. • Agreement between experimental data and numerical results is quite satisfactory. Abstract The tubular welded joints in H-shaped structural components of agricultural sprayers are responsible for failure and the localisation of hot-spots is very important in multiaxial fatigue assessment of such components. Therefore, the purpose of the present paper is to develop a novel approach both to localize hot-spots in tubular welded joints and to estimate the initial crack growth direction. Such an approach is based on the application of the hot-spot stress approach together with the multiaxial critical plane-based criterion proposed by Carpinteri et al. Comparison between experimental data and numerical results has shown a satisfactory agreement in terms of both hot-spots localisation and initial crack growth direction evaluation. [ABSTRACT FROM AUTHOR]

Abstract: The paper discusses the problems in identifying parameters characterizing the interaction of a tyre with different ground surfaces. Corresponding methods of tyre–surface contact monitoring are proposed on the basis of the fuzzy sets theory. Cases of a wheel rolling on both a road and on deformable ground are considered with particular emphasis on the identification of the friction and rolling resistance parameters in relation to environmental properties and tyre loading modes. The theory developed is illustrated by examples of cascade fuzzy observers using the off-board and on-board information space. To verify the proposed theoretical propositions, the work also contains the corresponding results of the hardware- and software-in-the-loop simulation. These fuzzy computing procedures can be used in various control systems of cars, off-road vehicles, and agricultural machines as well as in applications of Intelligent Transportation Systems. This paper is an extended version of the work “Identification and Prediction of Tyre–Surface Interaction Parameters” presented at the 16th ISTVS Conference. [Copyright &y& Elsevier]

Abstract: Soil tillage and its interaction with climate change are widely discussed as a measure fostering carbon sequestration. To determine possible carbon sinks in agriculture, it is necessary to study carbon sequestration potentials in relation to agricultural management. The aim of this paper is to evaluate the soil carbon sequestration potential of a site in north-eastern Switzerland under different tillage systems. The study was performed as a long-term (19-year) trial on an Orthic Luvisol (sandy loam) with a mean annual air temperature of 8.4°C and a long-term precipitation mean of 1183mm. The soil organic carbon (SOC) concentration was determined five times during the study period, with the paper focussing mainly on the year 2006. The main objective was to quantify the influence of mouldboard ploughing (PL), shallow tillage (ST), no-tillage (NT) practices, and grassland (GL) on soil organic carbon content, the latter''s different fractions (labile, intermediate, and stable), and its distribution by depth. In calculating the SOC content of the whole soil profile, we included a correction factor accounting for variations in bulk density (equivalent soil mass). The total SOC stock at a depth of 0–40cm was 65MgCha−1, and although higher under GL, did not differ significantly between PL, ST, and NT. SOC concentrations per soil layer were significantly greater for NT and ST (0–10cm) than for PL, which had greater SOC concentrations than NT and ST at 20–30cm depth. Both SOC concentrations and stocks (0–20cm) were largest under GL. In all treatments, most of the carbon was found in the intermediate carbon fraction. There was no significant difference in any of the three SOC fractions between NT and ST, although there was between ST and PL. A sharp decrease in C-concentrations was observed in the first 7 years after the transition from grassland to arable land, with a new equilibrium of the carbon concentration in the 0–40cm layer being reached 12 years later, with no significant difference between the tillage treatments. Overall, the results indicate that effects of tillage on soil carbon are small in moist, cold-temperate soils, challenging conversion into no-till as a measure for sequestering C. [Copyright &y& Elsevier]

Abstract: Mathematical models capable of describing the interaction between traction devices and soils have been effective in predicting the performance of off-road vehicles. Such a model capable of predicting the performance of bias-ply tires in agricultural soils was first developed by Brixius [Brixius WW. Traction prediction equations for bias-ply tires. ASAE Paper No. 871622. St. Joseph, MI: ASAE; 1987]. When the soil and vehicle parameters are known, this model uses an iterative procedure to predict the tractive performance of a vehicle including pull, tractive efficiency, and motion resistance. Al-Hamad et al. [Al-Hamad SA, Grisso RD, Zoz FM, Von Bargen K. Tractor performance spreadsheet for radial tires. Comput Electron Agr 1994:10(1):45–62] modified the Brixius equations to predict the performance of radial tires. Zoz and Grisso [Zoz, FM, Grisso RD. Traction and tractor performance. ASAE Distinguished Lecture Series #27. St. Joseph, MI: ASAE; 2003] have demonstrated that the use of spreadsheet templates is more efficient than the original iterative procedure used to predict the performance of 2WD and 4WD/MFWD tractors. As tractors equipped with rubber-tracks are becoming popular, it is important that we have the capability to predict the performance for off-road vehicles equipped with rubber-tracks during agricultural operations. This paper discusses the development of an empirical model to accomplish this goal and its validity by comparing the predicted results with published experimental results. [Copyright &y& Elsevier]

• A multi-view field-road classification method was proposed for GNSS trajectories. • Trajectories were converted to trajectory images to encode rich motion information. • Pixel-level visual features were extracted by a specific image segmentation model. The understanding of agricultural machinery, whether it is used "in field" or "on road", plays an essential role in optimizing the efficiency of cross-regional agricultural mechanization services that have been available in China for decades. With the widespread availability of Global Navigation Satellite System (GNSS)-enabled devices, developing a method for automatically identifying the activity associated with each point in GNSS recorded trajectories can significantly enhance the optimization of these services. In this paper, we propose a novel field-road classification method that utilizes two feature vectors to represent each point in a GNSS trajectory. The first feature vector is a statistical feature vector extracted with existing methods, while the second feature vector is a visual feature vector obtained through an image segmentation model. To extract visual features, we first convert each GNSS trajectory into a trajectory image that encodes the motion information (e.g., speed, direction) of each point and the spatiotemporal relationship between points. Then, an image segmentation model specifically for field-road classification was developed, which can effectively extract a visual feature vector for each pixel in a trajectory image, corresponding to each point in the GNSS trajectory. The image segmentation model used in our approach has a robust feature extraction capability that enhances the statistical feature representation of each point by adding pixel-level visual features. We evaluated the effectiveness of our multi-view field-road classification method using trajectories of corn, wheat, and paddy harvesters. The results demonstrated that our method achieved accuracy scores of 94.17%, 90.93%, and 83.43% for respective datasets. Our method outperformed state-of-the-art field-road classification methods, especially for trajectories with high-frequency GNSS acquisition. [ABSTRACT FROM AUTHOR]

This publication series describes the development of the Hohenheim Tyre Model – an approach that considers the properties of high volume, agricultural tyres. The research project was conducted in accordance with the V-Model, which proposes a standardised development methodology for mechatronic systems. The previous publication described amongst others the model structure and parameterisation. This paper elucidates the validation, which is an essential part of the V-Model. Validation received special attention and is divided into three parts. First, three-dimensional tyre behaviour on level surfaces was investigated. Within the second step, single tyre behaviour is validated during obstacle passages. Similar obstacles were then used in the final step that shows up the correlation between measured and simulated whole vehicle behaviour. Throughout the validation a very high level of accuracy is achieved. [ABSTRACT FROM AUTHOR]

Navigation system and its navigation algorithm are the crucial parts for intelligent paddy field weeding robot. The environments of paddy fields are complicated in South China. The colors of weed, duckweed and cyanobacteria, which grow in paddy fields, are very similar with rice seedlings. Moreover, the rice seedlings present various morphological features during the growth progress. Therefore, how to extract the guidance lines for navigation system and weeding robot presents various challenges. In order to deal with the above mentioned problems, a navigation method for weeding robot based on SUSAN (smallest univalue segment assimilating nucleus) corner and improved sequential clustering algorithm is proposed in this paper. Firstly, gray feature in paddy field image is extracted by using the adaptive graying algorithm. Secondly, the SUSAN corners are extracted as characteristic points. Thirdly, the seedling navigation line is detected by applying the improved sequential clustering algorithm and Hough Transform. Finally, the position error and angle error are calculated, and a fuzzy controller is designed to control the robot. Experimental results show desirable performances of the proposed method. The proposed segmentation method is effective in complicated environment. [ABSTRACT FROM AUTHOR]

Many relevant studies about screening of agriculture materials have been conducted from different perspectives such as mechanism design, optimization of parameters, and particle motion. Some studies suggest that a screen driven by parallel mechanisms is more adaptable than a traditional one while the traditional reciprocating screen still plays a positive role in agricultural production. Based on previous research regarding the variable-amplitude screening method, 4 indices of particle movement have been defined and computed in this paper to investigate the quantitative dispersion and migration characteristics of agricultural particles by using DEM (Discrete Element Method) simulation data. It shows that the turning angle of the front swing bar has a significant effect on the horizontal expansion coefficient δ x ( t ) , and the particles will be thin quickly at first and be stabilize within the next process. It also shows that the increase in the turning angle of the front swing bar leads to a negative stratification effect, but results in a positive migration effect of all particles on the screen. This research could provide a useful reference for solving the retention problem of agricultural particles in any position on the screen. [ABSTRACT FROM AUTHOR]

This paper proposes a new machine vision system, focused on crop row detection with three main goals: (1) to achieve a correct orientation at the starting point in the field for each path; (2) to achieve a precise guidance during path following and (3) to determine the weed density and overlapping. The vision system is designed to be installed onboard a mobile agricultural vehicle, i.e. submitted to turns, vibrations and undesired movements. The images are captured under image perspective projection, being affected by the above undesired effects. To achieve the above goals, we have designed an image processing strategy comprising two main modules: (a) crop rows detection with precise adjustment and (b) data extraction for determining weeds densities for site specific-treatments, guidance and the overlapping of the area where the treatment is to be applied. Both modules form a full process, and each module contains different processes, some of them coming from existing approaches (image segmentation and crop row estimation with adjustment) and other specifically designed for the proposed approach (exposure time control, position and orientation of the tractor and weeds density matrix computation with overlapping control). The proposed strategy has been conveniently tested and also the different processes defining each module, verifying their performances in terms of accuracies and processing times. Comparative analyses have also been carried out against some existing processes, also with acceptable performances. [ABSTRACT FROM AUTHOR]

In their efforts to understand why and how farmers adopt new technologies, techniques and programmes, rural sociologists and geographers have typically focused on the social and cultural relations in which farming knowledge and practices are embedded. However, limited scholarly attention has been given to the important ways in which materials and materiality are a constitutive element in how farmers come to know and engage with technology. This paper addresses this issue through the application of theoretical work on ordering, which focuses on the materially heterogeneous processes and implicit strategies that hold together and perform particular social and organisational arrangements. Drawing upon qualitative data from a research project on adoption of precision agriculture (PA) in the Australian rice industry, we identify two principal modes of ordering: (1) commercial-technological, in which lack of compatibility between technologies produced by different machinery manufacturers creates challenges for farmers in integrating and adapting PA to existing farming practices and systems; and (2) biophysical , where drought and low water allocations create uncertainty and a reluctance by farmers to make large capital outlays for PA technology. While these modes of ordering constrain rice growers’ capacities to adopt PA technology, we argue that growers also engage in their own alternative ordering practices to negotiate, work with, and work around these constraints. We refer to this work as tinkering and argue that it is a powerful, yet little recognised, form of ordering enabling growers to take advantage of the material benefits of PA in a way that is flexible, adaptable, and fits their immediate farming circumstances. In concluding, we contend that an ordering approach provides a fruitful way forward in recognising the more-than-cultural dimensions through which farmers engage with technology, and particularly the complex ways in which materiality intertwines with, shapes, and is shaped by, farming knowledge and practices. [ABSTRACT FROM AUTHOR]

An operation profile is a detailed description of machinery use and provides information about the production process (e.g. time and energy requirements). The high complexity of agricultural machine use over time with its various implements and production processes makes it difficult to automate the operation profile generation for agricultural machinery. Today, modern communication interfaces, like the ISO 11783 (ISOBUS), allow a comfortable data acquisition with comprehensive parameter information of the machine and production process status. This paper describes how the generation of operation profiles can be automated and what kind of machine data and status information are required. Experiments were conducted with a tractor-machine combination during field cultivation. The machine was equipped with a CAN data logger to record ISOBUS messages and GNSS position data during normal operation. A software tool was setup and programmed to drive the data logger. Fields were automatically detected by algorithms to avoid the time consuming manual definition of field boundaries. A graphical user interface simplified the setup and use of the software application. Furthermore, the algorithms analyzed the machine communication regarding the actual machine state based on position, machine activity and work state. The KTBL time classification 2013 was used as a basis for working time analysis as it is designed to divide the overall operational time of agricultural machines into clearly defined, qualitatively different partial time fractions. The machine state could be visualized in the software by a map to show the user where problems occurred e.g. in time losses while waiting or machine breakdown. A diagram presented the timespans spent in different time fractions for comparison to other, similar operations. Combined with other infrastructure data, specific operation profiles could be used to gain more detailed knowledge about machine use or to improve future operations. [ABSTRACT FROM AUTHOR]

The equivalent settling area factor allows for comparison amongst different centrifuge separators. For hydrocyclones, the so far developed factor does not consider the effect of concentration of solid particles c in the feed stream, because particle interactions at high concentrations cause hindered settling and reduce hydrocyclone performance. The focus of this paper is a modification of this factor to allow prediction of the influence of higher particle concentration in the feed stream. In particular, the equivalent area factor is modified at high particle concentration by applying different forms of hindered settling concentration functions and using data obtained from experiment or from existing empirical correlations. This results in a set of modified models that are evaluated using statistical techniques. Through statistical analysis, the function f ( c ) = c 0.0488 exp ( - 9.445 c ) is selected to modify the equivalent settling area for hydrocyclones. A performance chart is developed for hydrocyclones by undertaking the modified equivalent area model that can be used in hydrocyclone design applications. The developed performance chart is validated and is shown to be capable of predicting the hydrocyclone performance over a wide range of hydrocyclone flow rates and separation cut sizes. This chart is compared with a performance chart available in the literature and the chart in the literature is shown to over-predict the hydrocyclone performance. [ABSTRACT FROM AUTHOR]

Farm Management Information Systems provide decision-makers (farmers) with a set of predefined Key Performance Indicators (KPIs) that are issued manually from collected data or via sensors installed on farm equipment. However, agricultural decision-makers need ad-hoc KPIs issued from low-cost sensors. Therefore, we present our VBoxReporting system in this paper. VBoxReporting is a complete system that allows the rapid design and implementation of reporting KPIs issued from VBox data. VBox, which was developed by Exotic Systems, is a low-cost sensor for tracing the energy use of agricultural equipment. Once the indicators are modeled, VBoxReporting automatically generates all of the scripts required to load data from VBox and create the indicators. We also provide a usability study to validate our user interface layer. [ABSTRACT FROM AUTHOR]

Agricultural machinery (AM) is an important input in the agricultural process. It, thus, has an impact on achieving sustainable agriculture. This paper tackles the question of developing a process for the design of sustainable AM and its associated service ecosystem in the context of small farms of developing countries. The design and development efforts of agricultural mechanization in these contexts are preoccupied with concerns for the cost of the AM, increasing cropping intensity and reducing input costs like labor, seeds, and fertilizers. They push towards the timely accomplishment of tasks, reduction in post-harvest losses, and enhancement of crop output, resulting in increased farm income. However, how these are accomplished more often results in several detrimental effects on the farmland and the overall environment. Also, in the long run, if these approaches are not designed, manufactured, marketed, operated, serviced, and disposed of with due diligence on the sustainability of the overall agroecosystem, disruptions in the socio-economic fabric of the local agriculture-based economy can occur. This paper examines if the current sustainability-orienting design (SoD) approaches, in the form of guidelines or approaches, address sustainability holistically (on the social, economic, and environmental dimensions) during the design process. At the design phase, we consider the entire life cycle of these AM and its associated ecosystem. The life cycle here includes the following stages: design, development, manufacturing, sale, use, repair, maintenance, and end-of-life. In the paper, we first present a review of existing SoD approaches and guidelines from the domain of agriculture and design for sustainability. From the domain of agriculture, we found lifecycle assessment-based methods with a strong focus on the environmental dimension. We also identified some researchers suggesting the need for a holistic approach and attempting analysis and design of agricultural machinery using integrated approaches like the product-service systems (PSS) approach. From the domain of design for sustainability, we identified that SoD approaches from sustainable PSS design and life cycle assessment domains are apt for the given context. Then, we picked up one methodology from sustainable PSS design domain called Methodology for System Design for Sustainability (MSDS). We also picked up a life cycle assessment based design toolkit called ICS toolkit. We checked their applicability for designing sustainable AM and its associated service ecosystem and identified the pros, cons, and gaps in them. The case study - design of a two-wheeled power tiller-based bed planter and its associated service ecosystem for small farms of Bangladesh were used for the same. Drawing insights, we conclude the need for an appropriate SoD approach that combines three perspectives: sustainable PSS design, sustainable agriculture and environmentally sustainable product design. We next propose a possible SoD methodology for the same. The novelty of this research lies in bringing together the knowledge and expertise from two domains, agriculture, and product design. We use this combination to devise an SoD methodology that addresses sustainability holistically for the design of sustainable AM and its associated service ecosystem. Image 1 • Accesses current Sustainability-orienting Design (SoD) guidelines or mechanisms for sufficiency and efficacy for design of agricultural machinery and its allied service ecosystem. • Accesses on the three dimensions of sustainability - social, economic and environmental. • Accesses for the design phase, considering the entire life cycle of the agricultural machinery in small farms of developing countries. • Presents a set of guidelines for a Sustainability-orienting Design of agricultural machinery and its allied service ecosystem. [ABSTRACT FROM AUTHOR]

• This paper concentrates on an independent research data collection platform. • Both ISO 11783 compliant machines as well as proprietary systems are supported. • How the data collection can be realized in order to support research is presented. • How the cloud connection of the data collection system can be realized is presented. • The examples verify that the solution works in real farming environment. The agricultural machinery produces an increasing number of measurements during operations. The primary use of these measurements is to control agricultural operations on the farm. Data that describes the in-field variation in plant growth potential and growing conditions is the basis for precision farming. The secondary use for the gathered information is documentation of work and work performance for business purposes. Researcher also benefits from the increasing measurement capabilities. Biologists and agronomists can model the crops and agronomic phenomena. Work scientists can analyse the agricultural work processes. And finally, machines with additional accurate sensors can be used for agricultural machine product development and technological research purposes. This paper concentrates on an independent research data collection platform (Cropinfra) which can be used to collect data for all above mentioned purposes. Data can be collected both from ISOBUS (ISO 11783) compliant machines as well as older and proprietary systems and stored to database for further analysis. The farm machines in Cropinfra are supplemented with extra sensors that are more accurate than existing in commercial machines. Therefore, the Cropinfra can be used as a reference measurement system to verify the correct operation of the machines as well as to produce data for biological research purposes. This paper will also present how the cloud connection of the data collection system can be realized. The solution was designed to be compatible with the existing ISO 11783-10 standard. The examples presented in this paper verify that the solution works in real farming environment. The data has been used in numerous research projects already, and in the future the data will be an important asset when machine learning and other artificial intelligence methods will be studied and utilized. [ABSTRACT FROM AUTHOR]

The upscaling of farming operations in China has been advancing rapidly in recent years. This trend is likely to increase the use of agricultural machinery inputs, and possibly also the use of chemical pesticides, which is already extremely high. The relationship between farm size and the intensity of chemical pesticide usage is a subject of debate, and little is known about the mechanisms that might underlie this relationship. In this paper, we apply an integrated framework of larger-scale farming, agricultural mechanization and the intensity of chemical pesticide usage to examine and quantify the mediating effect of agricultural mechanization on the relationship between larger-scale farming and the use of chemical pesticides. Results obtained by applying seemingly unrelated regression (SUR) considering two-way fixed-effects and mediation analysis to county-level data for Jiangsu Province (2002–2019) suggest that an increase of 1% in the ratio of scale of farming is related to a total decline of 0.070% in chemical pesticide usage per hectare of land. Mediation analysis subsequently identifies mechanical sowing/transplanting is the only mediating variable, through which an increase of 1% in the ratio of scale of farming reduces the unit consumption of chemical pesticides by 0.017%. [ABSTRACT FROM AUTHOR]

Agricultural carbon emissions have long attracted scholars' interest. In recent decades, China has gradually explored the path of agricultural specialization without large-scale operation based on agricultural machinery service outsourcing driven by structural transformation. To date, our understanding of the impact of China's unique agricultural specialization on agricultural carbon emissions remains scant. Nevertheless, this issue is important for promoting China's carbon emission reduction and exploring a path of agricultural transformation that is both competitive and sustainable. Based on China's provincial panel data from 2000 to 2019, this paper constructed a mediating model to test empirically the impacts of agricultural specialization on agricultural carbon emissions. The results showed that agricultural specialization exerted a significant positive effect on agricultural carbon emissions by increasing agricultural external inputs. Specifically, in addition to promoting increased mechanization, agricultural specialization caused an even greater, excessive application of chemical fertilizers, which largely follows metabolic rift theory and substitution and remittance effects. The farmland operation scale had no significant effect on agricultural carbon emissions due to the offset of its significant negative effect on chemicalization and significant positive effect on mechanization. This finding demonstrates that, in the context of smallholder farming, the moderate expansion of farm size can reduce agricultural environmental pollution, but it cannot be proved to reduce agricultural carbon emissions. These findings challenge China's current unique path of agricultural specialization from the perspective of carbon emission reduction and demonstrate the applicability of the theory of metabolic rift to the current path of agricultural development in China. Accordingly, corresponding short-term and long-term implications related to agrarian transition and agricultural carbon emissions are provided. [Display omitted] • China's unique agricultural specialization promotes agricultural carbon emissions. • The moderate expansion of farm size can reduce agricultural environmental pollution. • The impact of agricultural specialization on external inputs follows the metabolic rift. • Excessive inputs of chemical fertilizers play a significant mediating effect. [ABSTRACT FROM AUTHOR]

Tractor rollover is one of the main case of severe accident in agriculture. Since such vehicles move on natural ground with varying conditions and different kind of terrain, the risk of rollover is difficult to estimate and predict using classical on-road approaches. This paper proposes an online adaptive observer to assess and avoid rollover risk in agricultural vehicles which move in off-road terrain. In particular, the approach focuses on reconfigurable tractor dedicated to move in terrain with important slope (such as grape harvester). It is based on the coupling between an intermittent measurement and an estimation of a stability metric, namely the Lateral Load Transfer (LLT). Thanks to this adaptive method, terrain and vehicle parameters are updated in order to take into account for the effects of changes in center of gravity height and total vehicle mass. This then allows to monitor the stability of the vehicle whatever the state of the slope correction system, the soil type and the load of the machine. The algorithm capabilities are tested through experiments using a grape harvester equipped with hydraulic actuators. [ABSTRACT FROM AUTHOR]

There is strong advocacy for agricultural machinery appropriate for smallholder farmers in South Asia. Such ‘scale-appropriate’ machinery can increase returns to land and labour, although the still substantial capital investment required can preclude smallholder ownership. Increasing machinery demand has resulted in relatively well-developed markets for rental services for tillage, irrigation, and post-harvest operations. Many smallholders thereby access agricultural machinery that may have otherwise been cost prohibitive to purchase through fee-for-service arrangements, though opportunity for expansion remains. To more effectively facilitate the development and investment in scale-appropriate machinery, there is a need to better understand the factors associated with agricultural machinery purchases and service provision. This paper first reviews Bangladesh’s historical policy environment that facilitated the development of agricultural machinery markets. It then uses recent Bangladesh census data from 814,058 farm households to identify variables associated with the adoption of the most common smallholder agricultural machinery – irrigation pumps, threshers, and power tillers (mainly driven by two-wheel tractors). Multinomial probit model results indicate that machinery ownership is positively associated with household assets, credit availability, electrification, and road density. These findings suggest that donors and policy makers should focus not only on short-term projects to boost machinery adoption. Rather, sustained emphasis on improving physical and civil infrastructure and services, as well as assuring credit availability, is also necessary to create an enabling environment in which the adoption of scale-appropriate farm machinery is most likely. [ABSTRACT FROM AUTHOR]

Reduction of both pollutant emissions and fossil fuel dependency is an objective of energy policies worldwide. In many countries, governments promote the use of efficient vehicles like the hybrid electric vehicle. Incorporation of electric drives in tractor and agricultural machinery presents advantages in terms of increased energy efficiency and expanded functionalities. Higher efficiency means reduction in fuel consumption and subsequent decrease in CO 2 emission. New functionalities improve work quality and increase operator comfort. Tractor electrification takes advantage of decoupling loads and drives from the engine, which allows operating the latter at its highest efficiency point. Major advantages of machinery electrification are torque and speed control, noise reduction, and a more flexible design. In this paper, a review of the state-of-the-art of agricultural machinery high voltage electrification is presented. [ABSTRACT FROM AUTHOR]

Fatigue life of agricultural machinery is strongly affected by the surfaces that these machines operate on. In the present paper a sensor-frame was developed to acquire road and field profiles in absolute geo-referenced coordinates. The sensor-frame was validated by measuring discrete trapezoidal bumps with known dimensions resulting in a root mean squared (RMS) error of 6–8 mm. Profiles were acquired from a country road and from a mowed grass field. Using the quarter-car vehicle model, the movement of an agricultural vehicle was simulated for various speeds. The resulted vertical loads were rainflow-counted and the accumulated fatigue pseudo damage was calculated using Palmgren–Miner linear rule. Based on the derived Power Spectrum Density (PSD), the profiles were classified according to ISO 8608 standard. Two methodologies were followed to model and create a number of synthetic realisations for each profile: Direct Spectrum Estimate (DSE) and ISO based modelling. Simulating the produced synthetic profiles with the quarter-car vehicle model, the corresponding pseudo damage was calculated. The accumulated damages from the DSE models were closer to the corresponding ones from the measured profiles. ISO based models could not model the profile irregularities, which proved to contribute the largest part of the accumulated fatigue damage. [ABSTRACT FROM AUTHOR]

The use of vehicles to control pests and crop diseases is needed to maintain agricultural production. This paper describes how to design and implement a low cost intelligent telecontrol system applied for agricultural machinery that are designed for use in places where human presence is not adequate, such as pesticide spraying tasks in farming environments as greenhouses. The intelligent system designed basically consists of these parts: States Encoding, Console Automaton, Vehicle Automaton, Communication System and Security of Communications. This system has been developed with a special-purpose UHF narrow band modem that interchanges digital operative commands/states from the console to the vehicle. The intelligent telecontrol system acts as a security system to protect against outsiders, and loss of communications due to fading or others interference. The originality of this system is based on the fact that it is 10 times cheaper than autonomous systems; those systems need electronic systems, GPS, a complex sensorial system, and a beforehand mapping performed on the new work environment. Related to classic remote control, our system protects from potential oversights of the operator. In addition, in the proposed system, the vehicle state is shown in the console, due the bidirectional communications. The system was successfully applied in prototype vehicle used for spraying tasks, and the result shows that the system operated stably and has confirmed the effectiveness of this intelligent telecontrol system. The proposed technology will help providing solutions for humans and robots working together in agricultural environments considered to be harmful to humans. [ABSTRACT FROM AUTHOR]

The mechanization of the Greek agriculture began in the early 60s as a way to overcome the lack of labor force, due to the rural exodus. The approval from the farmers was encouraged by the financial support provided via national and European programs. Nowadays, thanks to this support, the index of mechanization of the Greek agriculture exceeds country's needs. However, there is inefficient use of farm machinery, due to the lack of the necessary complementary parts or overwhelming power of the tractors. The aim of this paper is to examine the management of farm machinery in Greece, during the economic crisis, a period of volatility in the markets and need for reduced production costs. It is shown that farmers got used to a mentality of fast-track farm management and farm machinery selection which can no longer be continued. [ABSTRACT FROM AUTHOR]

The fieldwork pattern, which is the ordered traversal sequence of in-field tracks by the machine, plays an important role in operational efficiency. A sub-optimal fieldwork pattern is a main reason for lost time in fieldwork operations due to excessive non-working distance and time. Even though algorithms exist that can calculate an optimal route plan of a specific operation that covers the field with minimum non-working distance and time, so far no commercial navigation-aiding system for agricultural vehicles exist that can implement this type of patterns. Yet, the implementation of applicable standard fieldwork pattern in real life operations can provide near-optimal solutions compared to the simple fieldwork patterns generally selected by operators. In this paper, the potential savings from following an alternative fieldwork pattern instead of the one selected by an experienced operator are assessed. We simulate the distance and time corresponding to five selected common fieldwork patterns and compare them with the operators’ selected fieldwork pattern. In order to simulate the fieldwork patterns, four standard turn models, which are formulas for calculating the turning distance of various types of turns, are fitted to actual turns recorded in field experiments, and the turn models together with a model of in-field transport and material reloading are evaluated. As a case study three sequential operations of potato cultivation (bed forming, stone separation and planting) were recorded and analysed in three different fields. The simulation results showed significant potential savings of both distance and time in all three operations and all three case study fields by selecting the best fieldwork pattern instead of the actual selected pattern: For bed forming the savings were up to 18.4% in distance and 32.7% in time; For stone separation the savings of distance and time were up to 35.0% and 60.9%; For the planting the savings were up to 22.6% in distance and 24.8% in time. The increase in time-based field efficiency was up to 2.7%, 7.2% and 7.1% for bed forming, stone separation and planting, respectively. [ABSTRACT FROM AUTHOR]

Cleaning and screening of agricultural materials is an important process in agricultural production. Many researchers have carried out important works in this area based on mechanism, parameter optimization, and particles’ motion. In this paper, both advantages and disadvantages of the traditional reciprocating screen widely used in combine harvester were analyzed. Furthermore, a variable-amplitude screening mechanism developed from classical swing bar type reciprocating screens in Adams software was studied, and the trajectories of 11 marker points on the screen under different conditions were obtained. After transforming the motion of a variable-amplitude screen to 3 DOF (degree-of-freedom) motions (two translations and one rotation, 2T–1R), DEM (discrete-element method) simulations of agricultural particles (grain and stalk) in a variable-amplitude screen box were performed based on the Hertz–Mindlin contact model in an EDEM software environment. The results show that the variable-amplitude screen could make the agricultural particles at the front of the screen be quickly thrown up and moved back but could not favor their segregation and separation at the screen front. This variable-amplitude screening method can provide reference for solving the retention problem of agricultural materials at the screen front. [ABSTRACT FROM AUTHOR]

The characteristics of maize tassel trait are important cues to improve the farming operation for production enhancement. Currently, the information obtained from the maize tassel mainly depends on human labor, which is subjective and labor-intensive. Recent researches have introduced several image-based approaches to overcome the shortage with a modest degree of success. However, due to the variation of cultivar, pose and illumination, and the clustered background, characterizing the maize tassel trait with computer vision remains a challenging problem. To this end, an automatic fine-grained machine vision system termed mTASSEL is developed in this paper. We proposed to characterize the maize tassel with multi-view representations that combine multiple feature views and different channel views, which can alleviate the influence of environmental variations. In addition to the total tassel number trait, some fine-grained tassel traits, including the tassel color, branch number, length, width, perimeter and diameter, are further characterized to execute the time-series monitoring. To boost the related research, a relatively large-scale maize tassel dataset (10 sequences with 16,031 samples) is first constructed by our team. The experimental results demonstrate that both system modules significantly outperform other state-of-the-art approaches by large margins (26.0% for the detection and 7.8% for the segmentation). Results of this research can serve the automatic growth stage detection, accurate yield estimation and machine detasseling, as well as the field-based phenotyping research. The dataset and source code of the system are available online. [ABSTRACT FROM AUTHOR]

It is essential to design a product service system with the adequate location of maintenance facilities for guaranteeing the fast and dependable response to failures of extremely busy agricultural machinery during harvest time. This paper aims to optimize where to locate/relocate two hierarchical levels of facilities respectively composed of maintenance stations and mobile service fleets over multiple periods of a harvest season to support the safe and stable operation of agricultural machinery. We formulate a multi-objective dynamic covering location problem for these hierarchical facilities that seeks to maximize the total demand covered within some response radii while minimizing the modification to facility locations between the periods. The augmented ɛ -constraint method based on lexicographic optimization is then developed to obtain a set of non-inferior solutions that allow us to evaluate the trade-off between multiple decision objectives. Lastly, a real-life problem is handled to illustrate the effectiveness of our multi-objective decision model and solution method. Computational results recommend the optimal decision of facility locations for implementation, examine the sensitivity analysis of selected parameters, and compare the solution obtained from the proposed model with the period-specific solutions that take no account of the changing locations of facilities between the periods. [ABSTRACT FROM AUTHOR]

• A multi-view density-based field-road classification method was proposed. • An object detection method was used for field-road classification. • The combination of two field-road classification results used the DBI metric. Field-road classification that automatically identifies the operation modes (either in-field or on-road) of GNSS (Global Navigation Satellite System) points plays an important role for the operational performance analysis of agricultural vehicles. Intuitively, a field often has high point density because in-field driving speed is rather low and the distance between consecutive strips is closed. In this paper, two methods were used to capture the in-field high-density characteristic: DBSCAN (Density-Based Spatial Clustering of Applications with Noise) and an object detection model. DBSCAN is a widely-used density-based clustering algorithm, which clusters the points with high point density into a cluster. Alternatively, a trajectory can be transformed into an image, and an object detection model can be applied to detect objects in the image, where an object is a set of pixels with high pixel density (i.e., a set of points with high point density). Finally, the two field-road classification results are combined using DBI (Davis Bouldin index), a metric which can evaluate the quality of either classification result. The developed method was validated by the harvesting trajectories of two crops (wheat and paddy), and the density-based field-road classification achieved 85.97% and 73.34% accuracy on the wheat data and the paddy data, respectively. [ABSTRACT FROM AUTHOR]

• Correcting the agricultural machine body posture using the attitude of implement. • Establishment of kinematics model of agricultural machinery implement. • The Model Predictive Control path tracking control method is designed. The annual rice planting area in China is approximately 30 million ha. With continuous reductions in the rural labour force and increasing production costs, it is urgent to develop unmanned technology for paddy field agricultural machinery to solve the problem of "who will farm the land". In paddy field environments with slippery mud and uneven hard underlying, side slip and slip of paddy farm machinery can easily occur. Due to the relative movement and attitude changes between paddy farm machinery and implements and the inconsistent driving track of the farm machinery and implements, it is difficult to track and control the path of unmanned paddy farm machinery. In view of the above problems, this paper takes the paddy field agricultural machinery body as the control object and the agricultural machinery pose as the observation quantity and establishes an agricultural machinery kinematics model based on agricultural machinery pose correction. Based on the model and model predictive control (MPC), the linear model, objective function and constraint function of paddy field agricultural machinery are designed, an MPC path tracking control method is established based on the pose of the agricultural machinery, and field experiments are conducted. The results show that the average root mean square error of the three-line straight-line path tracking is 0.043 m, the average absolute error is 0.033 m, and the effect is favourable. The MPC path tracking method based on the pose correction of agricultural machinery can effectively suppress abrupt lateral position deviations caused by the relative position and attitude changes of the machine, can improve the control accuracy, and can meet the control accuracy requirements of unmanned paddy field agricultural machinery operations. [ABSTRACT FROM AUTHOR]

• A graph convolutional neural network was designed for field-road classification. • A spatio-temporal graph was constructed for a trajectory. • A graph convolution process was used to propagate features between nodes in a graph. Field-road classification that automatically identifies in-field activities or out-of-field activities is important for the activity analysis of agricultural machinery. The objective of this paper is to develop a field-road classification method based on GNSS recordings of agricultural machinery. In order to improve the accuracy of activity identification, a field-road classification algorithm for GNSS trajectories was developed by using a graph convolutional network (GCN) that utilizes spatio-temporal relationships between GNSS points. The algorithm does not require the presence of field boundary as an input. Firstly, a spatio-temporal graph was constructed for a trajectory to capture spatio-temporal relationships between each point and its neighboring points where each point was considered as a node in the graph. Secondly, a graph convolution process was applied to propagate features between nodes in the graph, and thus, the information of the points in the trajectory was aggregated to generate a feature representation for each point. Finally, the aggregated feature representations were used to identify the activities of the points. The developed method was validated by the harvesting trajectories of two crops, wheat and paddy, GCN-based field-road classification achieved 88.14% and 85.93% accuracy for the wheat data and the paddy data, respectively. Moreover, the results of the comparison demonstrated that the developed method consistently outperformed current state-of-the-art field-road classification methods by about 2% for the wheat data and about 5% for the paddy data. The GCN-based field-road classification algorithm can provide high-quality statistic cost of in-field and out-of-field activities, which can effectively support the development of operation scheduling systems for machinery management. [ABSTRACT FROM AUTHOR]

This paper presents a novel image algorithm using template matching and pattern recognition frameworks for monitoring Cercospora leaf spot (CLS) development on sugar beets on a single leaf scale under real field conditions. Due to the variety and complexity of the open field, it is a great challenge to achieve continuous and robust foliar disease observation in real field conditions. We propose a novel and compact algorithm, composed of two frameworks and a post-processing. The algorithm has continuous and highly discriminative capabilities for observing the process of disease in a single leaf from plant-level time sequence images. The first framework is based on robust template matching by orientation code matching (OCM), which implements successive tracking of a single leaf from a beet plant against severe illumination changes and non-rigid plant movements. The second framework uses a pattern recognition method of support vector machine (SVM) for achieving further disease classification from clutter field background. Prior to SVM, we propose a three feature combination of L ∗ , a ∗ , Entropy × Density, which has strong discrimination power to classify CLS disease from the clutter scene containing sandy soil, leaves, leaf stalks, and specular reflection. Additionally, post-processing is introduced to filter false positive noise to enhance the precision of the classification. Field experiment results demonstrate the feasibility and applicability of the proposed algorithm for disease monitoring under real field conditions. Meanwhile, comparative results with other conventional matching methods and feature combinations show the effectiveness of our proposed algorithm in both foliage tracking and disease classification. [ABSTRACT FROM AUTHOR]