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

Pentoś, Katarzyna; Pieczarka, Krzysztof; Lejman, K.

doi:10.1155/2020/7607545

Cited by 12 publications

(6 citation statements)

References 33 publications

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“…Several studies were conducted in a laboratory setup with a single-wheel tester to study the influence of the traction device's operational parameters and soil properties on traction efficiency. Table 1 contains the various CI methods proposed to model the traction efficiency [106][107][108][109][110][111][112].…”

Section: Tractionmentioning

confidence: 99%

Application of Computational Intelligence Methods in Agricultural Soil–Machine Interaction: A Review

et al. 2023

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Rapid advancements in technology, particularly in soil tools and agricultural machinery, have led to the proliferation of mechanized agriculture. The interaction between such tools/machines and soil is a complex, dynamic process. The modeling of this interactive process is essential for reducing energy requirements, excessive soil pulverization, and soil compaction, thereby leading to sustainable crop production. Traditional methods that rely on simplistic physics-based models are not often the best approach. Computational intelligence-based approaches are an attractive alternative to traditional methods. These methods are highly versatile, can handle various forms of data, and are adaptive in nature. Recent years have witnessed a surge in adapting such methods in all domains of engineering, including agriculture. These applications leverage not only classical computational intelligence methods, but also emergent ones, such as deep learning. Although classical methods have routinely been applied to the soil–machine interaction studies, the field is yet to harness the more recent developments in computational intelligence. The purpose of this review article is twofold. Firstly, it provides an in-depth description of classical computational intelligence methods, including their underlying theoretical basis, along with a survey of their use in soil–machine interaction research. Hence, it serves as a concise and systematic reference for practicing engineers as well as researchers in this field. Next, this article provides an outline of various emergent methods in computational intelligence, with the aim of introducing state-of-the-art methods to the interested reader and motivating their application in soil–machine interaction research.

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Section: Tractionmentioning

confidence: 99%

Application of Computational Intelligence Methods in Agricultural Soil–Machine Interaction: A Review

et al. 2023

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“…A several studies were conducted in a laboratory setup (single wheel tester) to study the influence of traction device's operational parameters and soil conditions on traction efficiency. Table 1 listed a various CI methods proposed to model the traction efficiency [106][107][108][109][110][111][112].…”

Section: Tractionmentioning

confidence: 99%

Application of Computational Intelligence Methods in Agricultural Soil-Machine Interaction : A Review

Badgujar¹,

Figueroa²,

Das³

et al. 2022

Preprint

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Soil working tools, implements, and machines are inevitable in mechanized agriculture. The soil-tool/machine interaction is a multivariate, dynamic, and intricate process. The accurate interpretation, description, and modeling of a soil-machine interaction is key to providing a solution to sustainable crop production by reducing energy input, excessive soil pulverization, and compaction. The traditional method provides insight into soil-machine interaction but often provides inadequate solutions and lacks broad applicability. Computational intelligence (CI) is a comprehensive class of approaches that rely on approximate information to solve complex problems. The CI method has been extensively studied and applied in soil tillage and traction domain in recent decades. The study critically reviews the CI techniques implemented in soil-machine interactions, especially in the context of tillage, traction, and compaction. The traditional methods and their limitation are discussed. The fundamental of CI methods and a detailed overview of the most popular methods are provided. The study reviews and summarizes the 50 selected articles on soil-machine interaction studies where CI methods were employed. It discusses the strength and limitations of employed CI methods. It also suggests the emergent CI methods and future applications are discussed. The outlined study would serve as a concise reference and a quick and systematic way to understand the applicable CI methods that allow crucial farm management decision-making.

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“…Therefore, it is essential to apply neural networks having strong prediction ability and fast optimization capability for the optimization of parameters. To improve the tractive performances of tractors under low fuel consumption and a low degree of soil compaction, Pentoś et al (2020) introduced a high-precision mathematical model. They utilized the neural network and indicated soil type as the major influencing factor of tractive performances.…”

Section: Introductionmentioning

confidence: 99%

Technological parameter optimization for walnut shell-kernel winnowing device based on neural network

Tang²,

Zhang³

et al. 2023

Front. Bioeng. Biotechnol.

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The detection method for technological parameter is outdates as the traditional test cycle is long as well as the measurement error and the test amount are huge. Moreover, it is difficult to disclose the operation mechanism of devices as the operation is time-consuming and laborious. Therefore, numerical simulation was used in this study to reveal the mechanism of the walnut shell-kernel winnowing device. Moreover, the influence of baffle opening combinations, inlet wind velocity and inlet angle on cleaning rate and loss rate was predicted by the neural network model. The results demonstrated that inlet wind velocity was the primary influencing factor of cleaning rate, followed by baffle opening and inlet angle. Besides, inlet wind velocity was the primary influencing factor of loss rate, followed by inlet angle and baffle opening. The winnowing device performed best (79.91% cleaning rate, 14.37% loss rate) when the baffle opening, inlet wind velocity and inlet angle were 7.01 cm, 24.36 m/s, and 9.47°. In addition, 1/8 walnut shells and 1/4 walnut kernels were incorrectly classified due to the increase in inlet wind velocity. The inlet wind velocity was considered the major cause behind the deteriorating winnowing performance of the device. Finally, the bench test and simulation optimization results were compared. The cleaning rate and loss rate relative error during the simulation test was lower than 1.06%, which ascertained the feasibility and validity of the neural network as well as the combined numerical simulation method. This study could be useful for future research and development of shell-kernel winnowing devices for hard nuts.

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

Cited by 12 publications

References 33 publications

Application of Computational Intelligence Methods in Agricultural Soil–Machine Interaction: A Review

Application of Computational Intelligence Methods in Agricultural Soil–Machine Interaction: A Review

Application of Computational Intelligence Methods in Agricultural Soil-Machine Interaction : A Review

Technological parameter optimization for walnut shell-kernel winnowing device based on neural network

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