Applying an artificial neural network approach to the analysis of tractive properties in changing soil conditions

Pentoś, Katarzyna; Pieczarka, Krzysztof

doi:10.1016/j.still.2016.08.005

Cited by 30 publications

(16 citation statements)

References 34 publications

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“…Similar results were recently reported by Pentoś and Pieczarka (2017) who observed that soil texture and moisture, both affecting, to a great extent, the mechanical response of the soil, had the highest influences on traction force and traction efficiency.…”

Section: Simulations Of Traction Coefficient  Tr Motion Resistancesupporting

confidence: 91%

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Influence of Soil on the Traction Performance of a 65 kW MFWD Tractor

Battiato¹,

Diserens²

2019

JAS

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This study aimed to investigate the influence of the mechanical behaviour of the soil surface on the traction performance and the fuel consumption of an agricultural tractor, both in qualitative and in quantitative terms, in order to increase the consciousness about the major role of the soil mechanical response in the optimisation of the energy aspects involved in the traction developed by a tractor and promote the development of new strategies to reduce costs of tillage management and improve agricultural sustainability. The traction performance of a 65 kW MFWD tractor at tyre pressures of 60 and 160 kPa was compared on four Swiss agricultural soils: a clay with corn stubbles, a clay loam with wheat stubbles, a silty loam and a loamy sand both with corn stubbles. Tests performed with a bevameter pointed out noticeable differences in the mechanical behaviour of the soils. According to such differences, the drawbar pull on the four soils was significantly disparate with differences in maximal values of about 16% at a tyre pressure of 60 kPa and up to 37% at a tyre pressure of 160 kPa. Simulations with a semi-empirical tractor-soil interaction model also showed dissimilarities in traction coefficient, motion resistance, and traction efficiency. Measurements of the fuel consumption pointed out the presence of a narrow slip range where the specific fuel consumption SFC is minimised. This range doesn’t vary significantly among the considered soils as well as with the tyre pressure and doesn’t differ very much from the range where the power delivery efficiency is maximised. The SFC differed for almost 20% among the considered soils at a tyre pressure of 60 kPa and for ca. 10% at a tyre pressure of 160 kPa. The increase in tyre pressure from 60 to 160 kPa produced an increment in SFC up to 16%. The results of this study clearly pointed out how the traction performance is a characteristic of the tractor-soil system and not of the tractor only, therefore, a proper knowledge of the soil mechanical behaviour should aid in developing strategies oriented towards reducing fossil fuel consumption.

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Section: Simulations Of Traction Coefficient  Tr Motion Resistancesupporting

confidence: 91%

“…In spite of the recognised main role of the mechanical reaction of soil in controlling the traction performance of a tractor, only few examples of quantitative assessment of such a role have been presented so far (Schreiber & Kutzbach, 2008;Lyasko, 2010;Pentoś & Pieczarka, 2017).…”

Section: Introductionmentioning

confidence: 99%

Influence of Soil on the Traction Performance of a 65 kW MFWD Tractor

Battiato¹,

Diserens²

2019

JAS

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“…Published literature shows that the application of the neural network model in predicting the urea hydrolysis rate requires further study. Some reports have also shown that the weight values of the neural network could be used to analyze the relative importance degrees of the input variables quantitatively (Zhang et al 2015;Pentoś and Pieczarka 2017). However, the application of this quantitative method on urea hydrolysis also deserves to be studied further.…”

Section: Introductionmentioning

confidence: 99%

Quantifying the relative importance of soil moisture, nitrogen, and temperature on the urea hydrolysis rate

Tao

Sun

Guo

et al. 2017

Soil Science and Plant Nutrition

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Urea hydrolysis is a complicated process influenced by multiple factors. Most previous studies only examined the causative factors of the process without ranking these factors' relative importance quantitatively. In this work, the experimental analysis method, ANOVA method, and backpropagation (BP) neural network method were used to rank the effects of moisture content (W), nitrogen amount (F), and temperature (T) on the urea hydrolysis rate. A group of 22 artificial neural network structures with different numbers of neurons in the hidden layer (2 ≤ L ≤ 12), different initial connection weights, and different learning algorithms was trained and validated using the data set. The relative importance of the factors that affect urea hydrolysis was studied on the basis of information on the weight and threshold value. Results showed that the individual factors and the interaction between any two factors exerted an extremely significant effect (p < 0.01) on the hydrolysis rate, except for the interaction among the three factors. The ANOVA method achieved the importance ranking of T > for the hydrolysis rate. The incremental backpropagation-BP-K model with the topological structure of 3-6-1 expressed the relationship between the input variables and the output variable accurately. All of the methods indicated that the degree of influence of the factors on urea hydrolysis followed the descending order of T > F > W. Temperature was the key factor in the hydrolytic process. The results of the importance analysis of the three methods were consistent. ARTICLE HISTORY

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“…where NV is the input or output normalized vector, V is the input or output data, V max is the maximum of the input or output data, and V min is the minimum of the input or output data. We followed the methods of Pentoś and Pieczarka [20].…”

Section: Experimental Data Acquisitionmentioning

confidence: 99%

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

2020

Self Cite

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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.

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Applying an artificial neural network approach to the analysis of tractive properties in changing soil conditions

Cited by 30 publications

References 34 publications

Influence of Soil on the Traction Performance of a 65 kW MFWD Tractor

Influence of Soil on the Traction Performance of a 65 kW MFWD Tractor

Quantifying the relative importance of soil moisture, nitrogen, and temperature on the urea hydrolysis rate

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

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