A Design of an Unmanned Electric Tractor Platform

The tractor is a vehicle often used in agriculture. It is mainly used to tow other unpowered agricultural machinery for farming, harvesting, and seeding. They consume a lot of fuel with emissions that often contain a large amount of toxic gases, which seriously jeopardize human health and the ecological environment. Therefore, the electrical tractor is bound to become a future trend. The objective of this study is to design and implement a lightweight, energy-saving, and less polluting electric tractor, which meets the requirements of existing smallholder farmers, equipped with unmanned technology and multi-functions to assist labor and to provide the potential for unmanned operation. We reduced the weight of the tractor body structure to 101 kg, and the bending rigidity and torsional rigidity reached 11,579 N/mm and 4923 Nm/deg, respectively. Two 7.5 kW induction motors driven by lithium batteries were applied, which allows at least 3.5 h of working time.

A Low-Cost Global Navigation Satellite System Positioning Accuracy Assessment Method for Agricultural Machinery

The high-precision positioning and navigation of agricultural machinery represent a backbone for precision agriculture, while its worldwide implementation is in rapid growth. Previous studies improved low-cost global navigation satellite system (GNSS) hardware solutions and fused GNSS data with complementary sources, but there is still no affordable and flexible framework for positioning accuracy assessment of agricultural machinery. Such a low-cost method was proposed in this study, simulating the actual movement of the agricultural machinery during agrotechnical operations. Four of the most commonly used GNSS corrections in Croatia were evaluated in two repetitions: Croatian Positioning System (CROPOS), individual base station, Satellite-based Augmentation Systems (SBASs), and an absolute positioning method using a smartphone. CROPOS and base station produced the highest mean GNSS positioning accuracy of 2.4 and 2.9 cm, respectively, but both of these corrections produced lower accuracy than declared. All evaluated corrections produced significantly different median values in two repetitions, representing inconsistency of the positioning accuracy regarding field conditions. While the proposed method allowed flexible and effective application in the field, future studies will be directed towards the reduction of the operator’s subjective impact, mainly by implementing autosteering solutions in agricultural machinery.

Statistical models expressing relations between soil moisture, aggregate speed, and tillage depth at plowing and cultivation

The study is based on a one-year field experiment (2019) in the land of the Chirpan region located in central Bulgaria. The agrotechnical operations of plowing and cultivation, applied in technology for the production of sunflower, are studied. Four models (Linear, Exponential, Logarithmic, and Quadratic) were compared at p < 0.05, defining the relation between soil moisture, aggregate speed, and the uniformity of the soil index Tillage depth during plowing and cultivation. It was found that in plowing at a speed of 4 km/h the Quadratic model described the relation between soil moisture and tillage depth with the highest coefficient of determination (R2 = 0.682). Relating to plowing at a speed of 4.5 km/h the most suitable is the Exponential model (R2 = 0.729), i.e. about 68.2% and 72.9% of the variations in tillage depth are due to the influence of the moisture of the soil. The coefficients of determination, calculated when cultivating at speeds of 8 km/h (R2 = 0.526) and 9 km/h (R2 = 0.557), show that the Quadratic model most strongly (52.6% and 55.7%) determines the relation between soil moisture and tillage depth. The developed models could be used to optimize the control systems of agricultural machinery.

Perception of the sociology of absences in the agricultural machinery industry supply chain

Studies on supply chain management have been focused on competitiveness from the alignment of suppliers to the company's main strategic interests, with the assumption that this alignment benefits all chains. In this approach, unmet interests of these suppliers are hidden when submitted to the subordination of the main company. This work aimed to understand how the supply chain of the agricultural machinery industry is managed, with the sociology of absences as a category of analysis. This is a descriptive and qualitative approach research, with the participation of six respondents, executives of companies providing implements for the three main agricultural machinery companies in the world. During the research, we sought to connect seven dimensions of supply chain management with the macro-sociological procedure of investigation of the sociology of absences. The results showed that the supply chain management of the agricultural machinery industry promotes the five non-logical existence (monocultures), putting smaller companies' supply chain members in a situation of absence and concealment, disregarding their knowledge and interests.

The Digital Twin for Agricultural Machinery Restoration Processes

Introduction. Agricultural machinery provides the required level of mechanization. Sand abrasive, dirt, and open-air operations considerably accelerate the wear of mechanisms. An improper work plan and lack of complete information about the state of specific equipment units increase the time for repair and maintenance operations. The purpose of the study is to develop a digital twin model for the repair and restoration system of enterprises. The model will reduce material costs and allow for the best solutions to organize the work. Materials and Methods. The model is developed on the basis of simulation modeling. The authors used the approach based on discrete-event modeling with the logical-mathematical apparatus for describing events occurring in a real object. Results. Information support is formed taking into account the parameters of the production systems of repair enterprises and a mathematical model, which is a digital twin of the production system. This approach made it possible to automate the development of optimal plans for organizing repair work by repair enterprises, taking into account their interrelationships. Discussion and Conclusion. The digital twin for the generalized production system of repair organizations allows developing options for the resource allocation and verifying them promptly to choose the best options through accumulating information about the most successful solutions. This will reduce the time for repair and restoration works, improve their quality and save labor.