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Please use this identifier to cite or link to this item:
http://krishi.icar.gov.in/jspui/handle/123456789/83557
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Prem Veer Gautam | en_US |
dc.contributor.author | Kamal Nayan Agrawal | en_US |
dc.contributor.author | Ajay Kumar Roul | en_US |
dc.contributor.author | Shekh Mukhtar Mansuri | en_US |
dc.contributor.author | A. Subeesh | en_US |
dc.date.accessioned | 2024-06-11T15:03:23Z | - |
dc.date.available | 2024-06-11T15:03:23Z | - |
dc.date.issued | 2023-11-27 | - |
dc.identifier.citation | Gautam, P. V., Agrawal, K. N., Roul, A. K., Mansuri, S. M., & Subeesh, A. (2024). Predictive modelling of sweep's specific draft using machine learning regression approaches. Soil Use and Management, 40(1), e12996. | en_US |
dc.identifier.issn | 1475-2743 | - |
dc.identifier.uri | http://krishi.icar.gov.in/jspui/handle/123456789/83557 | - |
dc.description | Not Available | en_US |
dc.description.abstract | Modelling and optimizing soil-tool interaction parameters for tillage operations is crucial for developing efficient and precise tools. This study focused on a specific draft of sweep tools in the soil bin filled with vertisol, considering factors such as tool geometry, cone index, working depth and operational speed. Data analysis showed that the range of specific draft values, from 9.51 to 38.95kN/m2. Machine learning models, including artificial neural network (ANN), support vector ma chine (SVM), bagged trees (BT) and boosted trees (BoT), were developed using experimental data to predict the specific draft of sweep tools with hyperparameter configuration. The developed machine learning models have also been compared with the predictive multiple linear regression (MLR) model, and it was found that the predictive performance of the machine learning models was better than the MLR model during training and testing. The fine-tuned ANN model achieved impressive statistical performance with the lowest mean absolute error (MAE) of 0.489kN/m2, root mean square error (RMSE) of 0.619kN/m2, standard error of prediction (SEP) of 3.462% and highest coefficient of determination (R2) of .99 during testing. R2 values for BT, BoT, SVM and MLR models were .97, .96, .94 and .83, respectively, for specific draft predictions. The findings from this study have practical implications for optimizing sweep tool design and improving till age operation. Manufacturers and farmers can benefit from predictive modelling using machine learning to design and select appropriate tillage tools for specific soil conditions. This approach can lead to improved soil health, increased yields and reduced costs. | en_US |
dc.description.sponsorship | Not Available | en_US |
dc.language.iso | English | en_US |
dc.publisher | Soil Use and Management | en_US |
dc.relation.ispartofseries | e12996; | - |
dc.subject | ANN | en_US |
dc.subject | boosted trees | en_US |
dc.subject | machine learning | en_US |
dc.subject | soil-tool interaction | en_US |
dc.subject | specific draft | en_US |
dc.subject | tillage tools | en_US |
dc.title | Predictive modelling of sweep's specific draft using machine learning regression approaches | en_US |
dc.title.alternative | Not Available | en_US |
dc.type | Research Paper | en_US |
dc.publication.projectcode | Not Available | en_US |
dc.publication.journalname | Soil Use and Management | en_US |
dc.publication.volumeno | 40(1) | en_US |
dc.publication.pagenumber | 1-16 | en_US |
dc.publication.divisionUnit | Not Available | en_US |
dc.publication.sourceUrl | https://doi.org/10.1111/sum.12996 | en_US |
dc.publication.authorAffiliation | ICAR::Central Arid Zone Research Institute | en_US |
dc.publication.authorAffiliation | ICAR::Central Institute of Agricultural Engineering | en_US |
dc.ICARdataUseLicence | http://krishi.icar.gov.in/PDF/ICAR_Data_Use_Licence.pdf | en_US |
dc.publication.journaltype | NAAS | en_US |
dc.publication.naasrating | 9.38 | en_US |
dc.publication.impactfactor | 3.8 | en_US |
Appears in Collections: | NRM-CAZRI-Publication |
Files in This Item:
File | Description | Size | Format | |
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Predictive modelling of sweep s specific draft using machine learning regression.pdf | 90.98 kB | Adobe PDF | View/Open |
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