Application of Soft Computing Techniques for the Analysis of Tractive Properties of a Low-Power Agricultural Tractor under Various Soil Conditions

Considering the fuel consumption and soil compaction, optimization of the performance of tractors is crucial for modern agricultural practices. The tractive performance is influenced by many factors, making it difficult to be modeled. In this work, the traction force and tractive efficiency of a low-power tractor, as affected by soil coefficient, vertical load, horizontal deformation, soil compaction, and soil moisture, were studied. The optimal work of a tractor is a compromise between the maximum traction force and the maximum tractive efficiency. Optimizing these factors is complex and requires accurate models. To this end, the performances of soft computing approaches, including neural networks, genetic algorithms, and adaptive network fuzzy inference system, were evaluated. The optimal performance was realized by neural networks trained by backpropagation as well as backpropagation combined with a genetic algorithm, with a coefficient of determination of 0.955 for the traction force and 0.954 for the tractive efficiency. Based on models with the best accuracy, a sensitivity analysis was performed. The results showed that the traction performance is mainly influenced by the soil type; nevertheless, the vertical load and soil moisture also exhibited a relatively strong influence

Chosen Aspects of Teamwork Iimplemented in the Subject Informatics in Agricultural Production Engineering

W artykule przedstawiono wybrane aspekty pracy zespołowej. Celem jest nauczenie studentów pracy w zespole, zarówno jej aspektów praktycznych, jak i podstaw teoretycznych. W ramach przedmiotu realizowane są projekty, do wykonania których wykorzystywane jest różnorodne oprogramowanie do wspomagania działalności przedsiębiorstwa. Efektem końcowym jest obszerna dokumentacja etapów pracy zespołowej, strona internetowa oraz prezentacja z wynikami projektu.The paper presents realization of chosen aspects of teamwork. The aim is to teach students to work in teams, both its practical aspects, as well as theoretical bases. In the subject are implemented the projects and are used software, which is used to support the activities of the company. The end result is a comprehensive documentation of the teamwork stages, website and presentation of the project results

Teaching Computer Aided Design on Life Sciences Studies

Celem artykułu jest wskazanie znaczenia i korzyści z zastosowania map wiedzy w edukacji ze szczególnym uwzględnieniem oficjalnych stron internetowych organizacji. Opracowanie przygotowano na podstawie studiów literaturowych i analiz zawartości witryn internetowych pod względem zastosowania takich mechanizmów, jak: wyszukiwarka strony, mapa serwisu, baza ekspercka.The aim of this article is to present significance and advantage of using knowledge maps in education, including in particular official website of the organization. This paper has been prepared based on study of subject literature and analysis of website content regarding use of following mecha-nisms: website browser, map of the webpage and expert base

Vocational Education – Expectations and Needs

Artykuł opisuje współpracę uczelni wyższej z sektorem przedsiębiorstw w celu umożliwienia studentom pozyskania bardziej wyspecjalizowanej wiedzy i umiejętności niezbędnych przy realizacji projektów zespołowych. Autorzy pokazują na przykładzie zajęć, w jaki sposób taka współpraca przebiega i jaki jest wkład przedsiębiorstwa w proces edukacji. Do tej pory słyszało się, że studenci przychodzący do pracy mieli bardzo rozległą wiedzę teoretyczną, ale nie praktyczną. Przedstawiony w artykule przykład pokazuje, w jaki sposób firmy są w stanie pomóc w procesie kształcenia po to, aby ten dysonans między wiedzą praktyczną a teoretyczną został zminimalizowany.In this paper authors describe the cooperation between universities and enterprises to provide students the ability to acquire a more specialized knowledge and skills that are indispensable when working on a team project. Authors show on an example of a project class the course of such a cooperation and what is an impact of the enterprise on education process. So far we could hear opinions that graduates coming to work in a company had a very wide theoretical knowledge but very little experience. An example in the paper shows how enterprises can help and influence the educations process to reduce the dissonance between practical and theoretical knowledge

The Relationship between Soil Electrical Parameters and Compaction of Sandy Clay Loam Soil

Soil spatial variability mapping allows the delimitation of the number of soil samples investigated to describe agricultural areas; it is crucial in precision agriculture. Electrical soil parameters are promising factors for the delimitation of management zones. One of the soil parameters that affects yield is soil compaction. The objective of this work was to indicate electrical parameters useful for the delimitation of management zones connected with soil compaction. For this purpose, the measurement of apparent soil electrical conductivity and magnetic susceptibility was conducted at two depths: 0.5 and 1 m. Soil compaction was measured for a soil layer at 0–0.5 m. Relationships between electrical soil parameters and soil compaction were modelled with the use of two types of neural networks—multilayer perceptron (MLP) and radial basis function (RBF). Better prediction quality was observed for RBF models. It can be stated that in the mathematical model, the apparent soil electrical conductivity affects soil compaction significantly more than magnetic susceptibility. However, magnetic susceptibility gives additional information about soil properties, and therefore, both electrical parameters should be used simultaneously for the delimitation of management zones

Evaluation of Multiple Linear Regression and Machine Learning Approaches to Predict Soil Compaction and Shear Stress Based on Electrical Parameters

This study investigated the relationships between the electrical and selected mechanical properties of soil. The analyses focused on comparing various modeling relationships under study methods that included machine learning methods. The input parameters of the models were apparent soil electrical conductivity and magnetic susceptibility measured at depths of 0.5 m and 1 m. Based on the models, shear stress and soil compaction were predicted. Neural network models outperformed support vector machines and multiple linear regression techniques. Exceptional models were developed using a multilayer perceptron neural network for shear stress (R = 0.680) and a function neural network for soil compaction measured at a depth of 0–0.5 m and 0.4–0.5 m (R = 0.812 and R = 0.846, respectively). Models of very low accuracy (R < 0.5) were produced by the multiple linear regression

Evaluation of Multiple Linear Regression and Machine Learning Approaches to Predict Soil Compaction and Shear Stress Based on Electrical Parameters

This study investigated the relationships between the electrical and selected mechanical properties of soil. The analyses focused on comparing various modeling relationships under study methods that included machine learning methods. The input parameters of the models were apparent soil electrical conductivity and magnetic susceptibility measured at depths of 0.5 m and 1 m. Based on the models, shear stress and soil compaction were predicted. Neural network models outperformed support vector machines and multiple linear regression techniques. Exceptional models were developed using a multilayer perceptron neural network for shear stress (R = 0.680) and a function neural network for soil compaction measured at a depth of 0&ndash;0.5 m and 0.4&ndash;0.5 m (R = 0.812 and R = 0.846, respectively). Models of very low accuracy (R &lt; 0.5) were produced by the multiple linear regression