An integrated cloud system based serverless android app for generalised tractor drawbar pull prediction model using machine learning
Knowing tractor drawbar pull is crucial to ensure the tractor can handle the required workload efficiently and safely, preventing soil damage and optimising field productivity. The present study proposes a novel approach for tractor drawbar pull prediction by utilising the tractor's geometric p...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-12-01
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| Series: | Systems Science & Control Engineering |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/21642583.2024.2385332 |
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| author | Harsh Nagar Rajendra Machavaram Ambuj Peeyush Soni Subhajit Saha T. Subhash Chandra Bose |
| author_facet | Harsh Nagar Rajendra Machavaram Ambuj Peeyush Soni Subhajit Saha T. Subhash Chandra Bose |
| author_sort | Harsh Nagar |
| collection | DOAJ |
| description | Knowing tractor drawbar pull is crucial to ensure the tractor can handle the required workload efficiently and safely, preventing soil damage and optimising field productivity. The present study proposes a novel approach for tractor drawbar pull prediction by utilising the tractor's geometric parameters and forward speed to develop a cloud-infused, server-less, machine learning-based real-time generalised tractor drawbar pull prediction model for any tractor between the 6-58 kW power range. The drawbar pull prediction models from ANN and six ML algorithms were developed, and the data analysis with hyperparameter tuning concluded that the Extreme Gradient Boosting (XGB) ML model outperformed the other ML models. A reasonable accuracy with R2 = 0.93 and MAPE = 6.77% was achieved using the XGB ML model for a separate validation dataset, which was not used for training. Furthermore, a cloud-based serverless Android App integrated with the XGB ML-based drawbar pull prediction model was developed for real-time tractor drawbar pull prediction and monitoring during tillage operations. The field validation demonstrated the XGB ML model's generalisation ability and effectiveness, with R2 = 0.90 and maximum MAPE of 9.86%. It can be used to simulate and optimize tractor performance, guiding manufacturers in selecting geometric parameters for tractor design. |
| format | Article |
| id | doaj-art-c0a3baaad9104e48af44c4e409709736 |
| institution | OA Journals |
| issn | 2164-2583 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Systems Science & Control Engineering |
| spelling | doaj-art-c0a3baaad9104e48af44c4e4097097362025-08-20T02:36:39ZengTaylor & Francis GroupSystems Science & Control Engineering2164-25832024-12-0112110.1080/21642583.2024.2385332An integrated cloud system based serverless android app for generalised tractor drawbar pull prediction model using machine learningHarsh Nagar0Rajendra Machavaram1Ambuj2Peeyush Soni3Subhajit Saha4T. Subhash Chandra Bose5Agricultural and Food Engineering Department, Indian Institute of Technology Kharagpur, West Bengal, IndiaAgricultural and Food Engineering Department, Indian Institute of Technology Kharagpur, West Bengal, IndiaAgricultural and Food Engineering Department, Indian Institute of Technology Kharagpur, West Bengal, IndiaAgricultural and Food Engineering Department, Indian Institute of Technology Kharagpur, West Bengal, IndiaAgricultural and Food Engineering Department, Indian Institute of Technology Kharagpur, West Bengal, IndiaAgricultural and Food Engineering Department, Indian Institute of Technology Kharagpur, West Bengal, IndiaKnowing tractor drawbar pull is crucial to ensure the tractor can handle the required workload efficiently and safely, preventing soil damage and optimising field productivity. The present study proposes a novel approach for tractor drawbar pull prediction by utilising the tractor's geometric parameters and forward speed to develop a cloud-infused, server-less, machine learning-based real-time generalised tractor drawbar pull prediction model for any tractor between the 6-58 kW power range. The drawbar pull prediction models from ANN and six ML algorithms were developed, and the data analysis with hyperparameter tuning concluded that the Extreme Gradient Boosting (XGB) ML model outperformed the other ML models. A reasonable accuracy with R2 = 0.93 and MAPE = 6.77% was achieved using the XGB ML model for a separate validation dataset, which was not used for training. Furthermore, a cloud-based serverless Android App integrated with the XGB ML-based drawbar pull prediction model was developed for real-time tractor drawbar pull prediction and monitoring during tillage operations. The field validation demonstrated the XGB ML model's generalisation ability and effectiveness, with R2 = 0.90 and maximum MAPE of 9.86%. It can be used to simulate and optimize tractor performance, guiding manufacturers in selecting geometric parameters for tractor design.https://www.tandfonline.com/doi/10.1080/21642583.2024.2385332Tractor drawbar pull predictiongeneralised modelmachine learningextreme gradient boosting algorithmcloud-based serverless android app |
| spellingShingle | Harsh Nagar Rajendra Machavaram Ambuj Peeyush Soni Subhajit Saha T. Subhash Chandra Bose An integrated cloud system based serverless android app for generalised tractor drawbar pull prediction model using machine learning Systems Science & Control Engineering Tractor drawbar pull prediction generalised model machine learning extreme gradient boosting algorithm cloud-based serverless android app |
| title | An integrated cloud system based serverless android app for generalised tractor drawbar pull prediction model using machine learning |
| title_full | An integrated cloud system based serverless android app for generalised tractor drawbar pull prediction model using machine learning |
| title_fullStr | An integrated cloud system based serverless android app for generalised tractor drawbar pull prediction model using machine learning |
| title_full_unstemmed | An integrated cloud system based serverless android app for generalised tractor drawbar pull prediction model using machine learning |
| title_short | An integrated cloud system based serverless android app for generalised tractor drawbar pull prediction model using machine learning |
| title_sort | integrated cloud system based serverless android app for generalised tractor drawbar pull prediction model using machine learning |
| topic | Tractor drawbar pull prediction generalised model machine learning extreme gradient boosting algorithm cloud-based serverless android app |
| url | https://www.tandfonline.com/doi/10.1080/21642583.2024.2385332 |
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