Agricultural Engineering Feed

Design and Performance Evaluation of a Four-Row Vacuum Seeder Powered by a Walking Tractor for Precision Soybean Planting

Sat, 14 Feb 2026 00:00:00 GMT

A previously developed two-row vacuum-type seeder showed promising singulation performance but its operational productivity was limited due to its low row capacity. This study aimed to design, develop, and evaluate a four-row vacuum-type soybean seeder to improve sowing capacity while maintaining high precision. The seeder, mounted on and powered by a walking tractor, used a centrifugal suction blower to generate vacuum pressure for four seed metering devices. Seed disks were driven by a chain-and-sprocket mechanism linked to the tractor’s wheel shaft. Bench tests were conducted at two seed disk speeds (12.7 and 26.1 rpm) and six blower speeds (3500, 4000, 4500, 5000, 5500, and 6000 rpm) using two soybean varieties. Singulation efficiency and the percentages of multiple and missing seeds were recorded. Results showed that higher vacuum pressures (>1.70 kPa) at lower disk speeds (12.7 rpm) significantly improved singulation, achieving 99–100% efficiency with minimal missed and multiple seeds (<1.25%) and no seed damage. Higher disk speeds (26.1 rpm) reduced accuracy. Field tests at three blower speeds (5000–6000 rpm) and two tractor speeds (Low-1 and Low-2) confirmed consistent seed spacing (19.2–19.6 cm) and optimal planting depth (4.8–4.9 cm), with minimal variation due to wheel slip. The best field performance (98% singulation) was achieved at Low-1 tractor speed and 6000 rpm blower speed. Increasing tractor speed enhanced field capacity from 0.16 to 0.31 ha·h⁻¹. The results validate the prototype’s effectiveness for precise and efficient soybean planting.

Development and Evaluation of a Yolo Algorithm-Based Robotic Sprayer for Real-Time Weed Detection

Sat, 14 Feb 2026 00:00:00 GMT

Weed control with chemicals is a challenging process that should be performed in a rational way to reduce their negative impact on the surrounding environment. The growth of artificial intelligence algorithms encourages researchers to develop smart spraying robots that detect and spray weeds and distinguish them from the main crop which leads to sustainable use of these chemicals and achieves some of the sustainable development goals. However, few studies are available to comprehensively compare different versions of YOLO algorithm to detect weed. In this research, seven versions of YOLO algorithms were evaluated for their performance to detect and spray four types of weeds, namely, Cultivated licorice (Glycyrrhiza glabra L.), Dyer’s Croton (Chrozophora verbascifolia), Lambsquarters (Chenopodium album L.), and Puncturevine (Tribulus terrestris L.) using a locally manufactured remotely controlled spraying robot. The results showed that YOLOv6n surpassed other algorithms which achieved the highest precision (0.89), recall (0.80), F1-score (0.84), mAp@0.50 (0.86), inference speed (18.83 fps), in addition to the field indicators including true positive rate (0.83), false negative rate (0.17), false positive rate (0.19), true negative rate (0.81).

Dynamic Simulation and Experimental Validation of Hydraulic Performance for a Valve-Regulated Proportional Fertigator

Mon, 15 Dec 2025 00:00:00 GMT

With the sustainable development of precision agriculture, the valve regulated proportional fertigator has attracted research attention due to its high efficiency combined with low cost and incidence of damage. In order to investigate the hydraulic performance of the fertigator, the internal flow, force and motion of the piston were analysed by means of dynamic simulation and experimental validation. Results show that the inlet flow of fertilizer first increased and then decreased with the increase of the three-way valve angle under the same pressure conditions. The area with the highest flow velocity of 13 m/s in the fertigator concentrates on the inlet one-way valve and three-way valve part, while the fertilizer extraction part and the outlet part were in the low flow velocity area. The movement frequency increased with the increase of the threeway valve angle, except the middle part that showed variations in the wave change. With the increase of the inlet pressure, the variations in the three-way valve angles were also increased. At the initial stage of piston motion, the driving force was much greater than the resistance, and the driving force and the resistance fluctuated from the initial maximum to the same stable value. The resultant force of the piston fluctuates from the initial maximum driving force of 242 N to 0 N, and then the piston accelerates from 0 N to the minimum driving force of 8.5 N. When the resultant force was reduced, the acceleration also decreased. However, when the acceleration was reduced to zero (0), the piston started to move uniformly, which is useful for achieving consistency in the spread of fertilizer application in the artificially drained catchment. The maximum relative error between simulation and experiment was 15%. However, there was no difference in the overall trend, so we can draw a reasonable and accurate conclusion of the simulation. The study could be useful for achieving uniformity in the spread of fertilizer application in the artificially drained catchment.

Model-based Assessment of Manual Raspberry Harvesting Efficiency Using the CIOSTA Methodology in Tunnel-Grown Production Systems

Mon, 15 Dec 2025 00:00:00 GMT

The aim of this study was to assess how raspberry cultivar and work organization influence the efficiency of manual harvesting under intensive tunnel-grown production. The research was carried out in July and August 2022 on a 20-hectare plantation and included four cultivars: Kwanza, Malling Bella, Enrosadira, and Diamond Jubilee. The CIOSTA methodology was applied, combining detailed time measurements and full-day work observations, which enabled an indepth analysis of effective time, auxiliary time, transport time, and work losses. In July, the average total harvest time was 240.7 rbh·ha⁻¹, while in August it decreased to 153.2 rbh·ha⁻¹. The highest efficiency was recorded for the Diamond Jubilee cultivar (K07 = 0.82), whereas the lowest values were observed for Malling Bella in July and Enrosadira in August. Transport-related activities accounted for the largest share of time losses, which is reflected in the transport utilization coefficient Kt (0.43-0.44). The results indicate that harvest efficiency depends not only on the cultivar and its yield potential but also on work organization and internal logistics. The findings confirm that the structure of the harvesting proces-particularly transport Logistics-plays a key role in determining overall efficiency. The presented analysis provides valuable input data for developing simulation and optimization models that support the design of improved work organization solutions in horticultural farms. The study aligns with current research trends in organizational and logistical analysis within horticultural production and forms a basis for creating model approaches to soft-fruit harvesting systems.

Integrated Ultrasound-Assisted Convective–Microwave Drying for the Dehydration of Medicinal Plants

Mon, 15 Dec 2025 00:00:00 GMT

Ultrasound-assisted convective-microwave drying (UACMD) is an emerging hybrid drying method that combines hot-air convection, microwave heating and ultrasound cavitation to enhance moisture transfer and preserve bioactive compounds in medicinal herbs. This review summarizes its advantages, including a 30-80% reduction in drying time, improved energy efficiency, and better retention of antioxidants, essential oils, and color. However, challenges, such as thermal degradation and scale-up complexity remain. Future developments emphasize the integration of smart controls (artificial intelligence, computer vision, and hyperspectral imaging) and the tailoring of protocols for specific herb types, positioning UACMD as a sustainable and high-performance solution for modern herbal drying.

The State and Prospects of Digital Transformation in the Agricultural Sectors

Mon, 15 Dec 2025 00:00:00 GMT

The purpose of the study is to analyse trends and directions of digitalisation of agriculture, with a focus on technologies that contribute to improving the efficiency and sustainability of the industry. The methodology includes an analysis of the state of digitalisation of agriculture, key technologies, barriers to implementation, and development prospects based on statistical data and industry reports. The paper analyses the most developed crop and livestock industries in Kazakhstan, such as the production of cereals, oilseeds, dairy, and beef cattle, with a focus on the use of drones, the Internet of Things, artificial intelligence, and blockchain. The technical features of technologies, implementation costs, payback periods, and infrastructural requirements are considered. A comparative analysis of digitalisation in Kazakhstan and other countries is conducted, which identifies the main barriers, such as high costs, insufficient infrastructure, and a low level of digital literacy. It is determined that drones contribute to optimising field monitoring and accurate resource allocation, the Internet of Things provides data collection and analysis for real-time process management, artificial intelligence is used for forecasting and automation, and blockchain increases the transparency of supply chains. These technologies have improved resource management, increased yields, and minimised environmental impacts. The main barriers hindering the digitalisation of agriculture were high technology costs, insufficient infrastructure, low levels of digital literacy among industry workers, and resistance to change on the part of farmers.

Environmental Footprint and Water Footprint in Studies on the Impact of Agricultural Production on the Natural Environment

Wed, 26 Nov 2025 00:00:00 GMT

In the face of growing challenges related to climate change, environmental degradation, and limited natural resources, the assessment of the environmental impact of various economic sectors is becoming increasingly important. Agriculture, as one of the key branches of food production, significantly contributes to environmental degradation, water resource consumption, and ecosystem transformation. In this context, the concepts of Environmental Footprint (EF) and Water Footprint (WF) are particularly important, serving as tools for the quantitative assessment of the pressure exerted by agricultural production on the environment. The environmental footprint encompasses various impact indicators, such as greenhouse gas emissions, eutrophication, acidification, abiotic resource consumption, and biodiversity loss. The water footprint, on the other hand, focuses on quantifying freshwater use throughout the product life cycle, considering blue, green, and grey water. The combined use of these tools allows for a comprehensive analysis of the impact of agricultural practices on the natural environment and the identification of areas requiring improvement towards sustainable development. Consequently, they help shape environmental policies and sustainable resource management strategies. The aim of this article is to present current methods for assessing environmental and water footprints in agricultural production and to discuss the main factors influencing their values. The article also presents the current state of research in this field and practical examples of how these tools are applied in evaluating different farming systems.

Investigation of the Physical Properties of Fluids for Removing Industrial Contaminants

Sun, 05 Oct 2025 00:00:00 GMT

The aim of this study was to investigate the influence of concentration and temperature on the physical properties of selected industrial cleaning fluids, focusing on parameters relevant to the cleaning process, such as viscosity, shear stress, density, activation energy, and wettability. Four cleaning agents from the company Noyen were examined, including both concentrated products and their 10% aqueous solutions. The rheological properties of the fluids were evaluated using a rotational viscometer; density was measured with a pycnometer; surface tension and wettability were assessed using a goniometer; and activation energy was determined based on the Arrhenius equation. The results of the viscosity and shear stress tests indicated that all tested fluids exhibited Newtonian behaviour in both concentrated and diluted forms. Surface tension and wettability analysis showed good wetting properties for all tested fluids (contact angle <55°), although significant differences in wettability were observed depending on the type of surface and the specific fluid used.

Innovative Technology for the Inactivation of Allergenic Compounds on Component Surfaces of a Prototype Processing Line for the Manufacture of Products with Controlled Allergenicity

Wed, 10 Sep 2025 00:00:00 GMT

The study aimed to develop and validate effective procedures for cleaning and allergen deactivation in a prototype processing line for crisp-coated meat products. Reliable cleaning methods are critical for ensuring food safety and preventing cross-contamination when conventional and allergen-controlled products are manufactured on the same line. Critical points along the processing line were identified through preliminary assessments. At these points, allergen residues were monitored using immunoassay-based tests targeting major food allergens, including gluten, soy, milk, nuts, sesame, and others. Several cleaning procedures were compared with regard to their ability to eliminate allergenic proteins from equipment surfaces. Lower-concentration protocols proved inadequate, particularly in high-risk areas such as cutting, mixing, and cooling zones. In contrast, the optimized procedure achieved complete removal of allergens at all critical points. This outcome was further confirmed during validation trials involving thirty pilot production batches, where no allergenic proteins were detected in environmental swabs or final products. The results demonstrate that carefully designed cleaning strategies can enable dual use of processing lines for both standard and allergen-controlled products. The validated procedure offers a cost-effective and practical solution for the food industry, supporting compliance with regulatory requirements and improved consumer safety.

Pre-Sowing Seed Treatment in Electric Aerosol Cloud

Fri, 25 Jul 2025 00:00:00 GMT

The efficiency of the existing technologies for pre-sowing seed treat-ment, ensuring the high quality of seed material along with its proper clearing and sorting, has been analysed. One effective solution is the introduction of electrical technologies, the complexity of which lies in the scientific sophistication and the variety of tasks they address. The authors propose an electrotechnological complex (ETC) for pre-sowing seed treatment, which includes a working chamber, where charged grain is fed, while aerosol is delivered through adjustable sprinklers directed toward the seed flow, allowing for control over the spray stroke length. When the freely falling charged seeds enter the cloud of oppositely charged aerosol, they become intensively coated with the working solution. The degree of coverage depends on the length of the working chamber, the potential difference between the grain flow and the sprinklers, and the concentration of the aerosol cloud. Given the relative complexity of the process and the presence of multiple disturbances, optimisation can only be achieved through automation. The main objective of the paper is to study the stability and functional quality of the automated ETC for pre-sowing grain treatment with aerosol. To achieve this, a structural and functional algorithmic scheme has been developed, outlining the composition of its elements, signal direction, and the functional purpose of each component. This scheme facilitated the development of a system simulation model. The paper presents calculated results for key performance indicators of the ETC, with values aligning with those typical for this class of systems. A key advantage of this research is the proposed system for optimal correlation of all element characteristics and parameters of the automated object, ensuring stability and high performance.

Qualitative Characteristics of Selected Liquid Fruit Products Available on the Polish Market

Fri, 25 Jul 2025 00:00:00 GMT

The current study aimed to assess the quality of selected liquid food products made from apple juice and available on the domestic market. The study determined the extract content, pH and density of twenty products, i.e., juices, nectars, soft drinks and smoothies from different producers. The study shows that the apple juice-based liquid products tested differ in terms of quality. The analyses also show that there is a greater variation in the apple juice-based liquid products tested in terms of extract content, to a lesser extent in terms of acidity, whereas in terms of density, it is small but statistically significant. The highest extract content, density and pH values were noted for the smoothies and apple juices, whereas the lowest values were noted for soft fruit drinks and nectars.

An Iterated Local Search Method for Determining Routes in the Transport of Raw Materials and Agri-Food Processing Products

Thu, 19 Jun 2025 00:00:00 GMT

To acquire raw materials and distribute products, agri-food processing companies need to operate at long distances, which is associated with high energy costs and an increased negative impact on the natural environment. Many methods have been developed to reduce energy consumption in transport. One approach that stands out is optimization of transport routes, which provides benefits while incurring hardly any extra costs. In this paper, an attempt was made to modify the ILS-RVND metaheuristic by adjusting its components to the specific character of the problem being solved so that they could yield the expected results with regard to the quality of returned solutions and the time in which they were generated. A group of local search algorithms, Swap (2-1), Cross-exchange (2-1), 3-opt, Or-opt (2) and Displacement, were analyzed. The results of this analysis were used to formulate the final version of the metaheuristic. The efficiency of the algorithm was evaluated using test cases. The solutions generated by the metaheuristic produced considerable improvement in the objective function (70.99% on average) and were obtained within an acceptable time (on average 24.66 CPU seconds).

Fruit and Vegetable Waste Management Through Co-Digestion with Local Market Wastewater: Operating Conditions and System Kinetics

Thu, 19 Jun 2025 00:00:00 GMT

Environmental pollution from fruit and vegetable waste (FVW) produced by local markets in South Africa is inevitable. Nevertheless, the current management strategy of disposing of FVWs in landfills contributes to the emission of greenhouse gases. Therefore, valorizing agricultural waste into bioenergy is critical for achieving zero waste and reducing the carbon footprint. In this study, parametric optimization of the co-digestion of FVWs with market wastewater (MW) was conducted using the Box–Behnken design (BBD) adapted from response surface methodology (RSM). The study identified optimal combinations of process variables, i.e., temperature, pH, hydraulic retention time (HRT), and organic loading rate (OLR), to produce biogas while reducing volatile solids (VS) and chemical oxygen demand (COD) from wastewater. At optimal operating conditions of 40°C, HRT of 10 days, pH of 7.2, and an OLR of 3.98 kg VS·m−3·day−1, a desirability of 100% was achieved. A biogas production rate of 717 mL·day−1 was reported, with VS and COD removals of 73.37% and 79.24%, respectively. The robustness of the predictive models developed using RSM was corroborated by R² values greater than 0.9 for all output variables. The Modified Gompertz model was well-fitted to the experimental data, yielding an R² of 0.995 and a lower root mean square error (RMSE) of 21.08. The findings of the present study suggest that the valorization of FVW through co-digestion with wastewater can be considered a promising, environmentally sustainable technology for agro-waste management and bioenergy production.

Opportunities for the Use of Post-Production Raw Materials of the Fruit and Vegetable Industry in the Agri-Food Sector: A Review

Thu, 19 Jun 2025 00:00:00 GMT

In recent years, there has been growing interest in the reuse of fruit and vegetable processing of by-products due to their valuable components. This paper presents both conventional and innovative extraction methods that enable the recovery of many desirable bioactive compounds. In addition to their potential application in enriching soil with minerals and supplementing animal feed with dietary fiber, these compounds can primarily be used to enhance traditional food products by imparting health-promoting properties. Overall, the utilization of by-products generated in the fruit and vegetable sector offers numerous economic, environmental, and social benefits for the agri-food industry. These practices are in line with the principles of sustainable development and efficient resource management.

Smart Solutions in Agricultural Robotics

Thu, 19 Jun 2025 00:00:00 GMT

The article contains an overview and prospects for the introduction of smart and robotic technologies in the agricultural industry. Today, robotization of agriculture and food technology is not a leading industry compared to robotization of other sectors of the economy. Analytical review of the global robotics market revealed that robots in the food industry account for 2% of the total, and robots for agricultural operations account for less than 1%. We analyzed global trends in robotization and the introduction of artificial intelligence in agriculture in various countries. A review of smart robotic technologies, in particular, smart sensors and actuators, the Internet of Things, cloud technologies, methods of data analysis, forecasting, classification, and image recognition using neural networks, is provided. Methods and means are considered in relation to both crop production and livestock production. Four main classes of intelligent technologies in agriculture were identified: robotics, Internet of Things, machine learning, and UAVs. In crop production, robots are common in harvesting, controlling weeds, processing trees, monitoring trees at different stages of the growing season, including leaf and fruit diseases, counting the number of flowers and fruits, and other operations. Identification of fruits, leaves, weeds, as well as diseases of fruits and leaves is performed using computer vision, and in recent years convolutional neural networks have become widespread. Field monitoring is performed using UAVs with subsequent image processing using spectral analysis methods. In livestock farming, smart technologies are represented by feeding and milking robots, livestock monitoring, detection of predators, navigation in livestock buildings, monitoring animal health and behavior. Smart technologies are also used in fish farming to create smart farms. In this mini review, we have tried to integrate advanced smart and robotic technologies for various agricultural operations.

Effect of Corn Canopy on Application Rate and Uniformity of Water Distribution Using a Sprinkler

Mon, 09 Jun 2025 00:00:00 GMT

The study evaluated the effects of cultivation under a canopy, concerning plant density, row spacing, and cultivation without a canopy, on the application rate and the uniformity coefficient with different catch-can heights. The experiment was conducted at the Fishery and Aquaculture Technology Farm in the Volta Region of Ghana. Catch-cans were arranged at the heights of 0.5, 1.0, 1.5, and 2.0 m, respectively, and the working pressure varied from 150~250 kPa. The average infiltration rate with the canopy and without it was 31.2 mm h−1 and 37.08 mm h−1, respectively. Corn under the canopy significantly influenced (p-values 0.001) the infiltration depth before runoff. However, the effect of the cultivation without a canopy was insignificant (p = 0.05). The average application rates obtained under the canopy at the catch-can heights of 0.5, 1.0, 1.5, and 2.0 m were 56.6, 61.4, 68.0, and 52.0 m h−1, respectively. The maximum and the average uniformity coefficient (Cu) values obtained under the canopy were 79% and 64%, respectively. The average sediment productions (gm−2) were 2.0, 2.53, 3.0, 3.63, 5.95 and 70.2, respectively, in the treatments of 40.1 mm·h−1 and duration (Dur) 80 min, 45.2 mm·h−1 and Dur 60 min, with 30.4 mm·h−1 and Dur 110 min, 28.4 mm·h−1 and Dur 120 min, and 59.2 mm·h−1 and Dur 40min.

Mathematical Model of Thin Layer Drying of Purple Yam by Infrared Assisted Heat Pump Drying

Mon, 09 Jun 2025 00:00:00 GMT

This study focused on the thin-layer drying of yam by infrared-assisted heat pump drying to determine the thin-layer drying model, the effective moisture diffusivity, and the activation energy of moisture within the yam. The thin-layer drying experiment was conducted with input drying parameters such as drying temperatures of 40, 45, and 50°C, drying air velocity of 2.5 m·s⁻¹, and infrared power of 250, 300, and 350 W. In order to determine a suitable thin-layer drying model for describing the yam drying process, six different thin-layer drying models (Lewis, Page, Modified Page, Henderson and Pabis, Wang and Singh, and Midilli) were chosen for nonlinear regression with the experimental drying data. The Midilli model was found to be the most suitable drying model for describing the thin-layer drying of yam. The average effective moisture diffusivity was in the range of 4.184×10⁻⁹ to 8.142×10⁻⁹ m²·s⁻¹, and the activation energy was in the range of 16.78 to 21.01 kJ·mol⁻¹ over the proposed range of drying input parameters

Investigating the Effect of Ozonation on Mechanical Parameters of Lonicera caerulea L. Fruits Using Machine Learning

Mon, 09 Jun 2025 00:00:00 GMT

Lonicera caerulea L. - well - known in Poland as Kamchatka berry, has been gaining increasing popularity in recent years. The tests carried out on newly established Japanese haskap clones aimed at demonstrating the suitability of the fruits for mechanical harvesting and storage. This study focused on evaluation of mechanical properties and assessment of three distinct machine learning techniques to create predictive models that elucidate the connection between key mechanical attributes of the fruit and storage conditions of L. caerulea. The average force needed to puncture the fruits skin and flesh of L. caerulea var. emphyllocalyx varieties is 16.91% higher than that required for the tested L. caerulea var. kamtschatica varieties. L. caerulea var. emphyllocalyx fruits exhibited a significantly higher respiration rate, with C₂H₄ and CO₂ levels during storage being 25.5% and 10.5% higher, respectively, compared to L. caerulea var. kamtschatica varieties. The machine learning algorithms tested yielded accurate models for deformation and energy prediction. The mean absolute percentage error (MAPE) of these models was determined to be between 14.87 and 20.65%. Models with significantly lower accuracy were obtained for force prediction, with the MAPE reaching 28.95% for L. var. kamtschatica fruit and 42.33% for L. emphyllocalyx fruit. The cultivation and improvement of Lonicera caerulea L. varieties is of great importance for the advancement of mechanized harvesting methods and the development of improved storage technologies for this species. The creation of machine learning methods will facilitate the development of predictive models that can serve as a predictive tool for the relationship between selected mechanical properties

RenewGeo: An Innovative Geothermal Technology Augmented by Solar Energy

Thu, 24 Apr 2025 00:00:00 GMT

The article presents information on geothermal energy as one of the renewable energy sources, discussing its resources, types, energy potential, and economic utilization methods, with a particular emphasis on electricity generation. It examines geothermal resources in Poland, highlighting their untapped potential for electricity production. Examples of commercial hybrid systems for electricity generation in binary geothermal power plants are provided. The concept of an innovative geothermal-solar hybrid system for electricity generation is introduced, along with the potential benefits, such as the widespread use of geothermal resources, solar energy storage, and the production of emission-free energy.

Motion Analysis of a Trailed Harvester with Combing Working Units

Wed, 02 Apr 2025 00:00:00 GMT

The article presents the motion analysis of a trailed harvester equipped with combing working units. The harvesting unit comprises a wheeled tractor, a harvesting machine, and a trailer for collecting the combed heap. The research aims to model the behaviour of the machine under various simulation conditions. To facilitate the analysis of the motion of the three-link harvesting aggregate, constraints of the harvester to the tractor and trailer were replaced with their reactions, and the motion of a single harvester was considered. In the first stage of the studies, a calculation scheme was drawn up indicating the forces and moments of forces influencing the machine and the constraint reactions. Lagrange’s equation of the second kind in generalised coordinates was used to derive a differential equation of the machine’s motion. The rotation angle of the harvester relative to the hitch point with the tractor was taken as a generalised coordinate. After algebraic transformations, a differential equation for the harvester’s motion was obtained. By solving the differential equation, a function was found, which made it possible to analyse the change in the rotation angle of the machine. Further analysis of the motion of the harvester was carried out using experimental methods. Experimental data is used to verify the model’s accuracy and its correspondence to real processes. If the model accurately predicts the behaviour of the combed heap, it confirms its adequacy. The parameters of the mathematical model can be adjusted based on the results of experiments to improve the accuracy of the prediction. The mathematical model allows predicting the behaviour of the combed heap under various conditions, which can help optimise the process parameters. Experiments can produce results that are difficult to interpret without a theoretical framework. The model helps to explain the mechanisms underlying the observed phenomena. The programme of experimental studies included obtaining the statistical characteristics of the horizontal oscillation amplitude of the harvester within the speed range of 1.2-2.8 m·s−1. The minimum deviation of the mass centre of the harvester from its linear motion was adopted as an estimation criterion for its linear motion. As a result of experimental studies which will be presented in the next article, it was determined that the most acceptable mode of motion that meets this requirement and provides the maximum efficiency of the harvester is the motion speed of 2.0 m·s−1.