Highly Efficient JR Optimization Technique for Solving Prediction Problem of Soil Organic Carbon on Large Scale
We utilized remote sensing and ground cover data to predict soil organic carbon (SOC) content across a vast geographic region. Employing a combination of machine learning and deep learning techniques, we developed a novel data fusion approach that integrated Digital Elevation Model (DEM) data, MODIS...
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MDPI AG
2024-11-01
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| author | Harsh Vazirani Xiaofeng Wu Anurag Srivastava Debajyoti Dhar Divyansh Pathak |
| author_facet | Harsh Vazirani Xiaofeng Wu Anurag Srivastava Debajyoti Dhar Divyansh Pathak |
| author_sort | Harsh Vazirani |
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| description | We utilized remote sensing and ground cover data to predict soil organic carbon (SOC) content across a vast geographic region. Employing a combination of machine learning and deep learning techniques, we developed a novel data fusion approach that integrated Digital Elevation Model (DEM) data, MODIS satellite imagery, WOSIS soil profile data, and CHELSA environmental data. This combined dataset, named GeoBlendMDWC, was specifically designed for SOC prediction. The primary aim of this research is to develop and evaluate a novel optimization algorithm for accurate SOC prediction by leveraging multi-source environmental data. Specifically, this study aims to (1) create an integrated dataset combining remote sensing and ground data for comprehensive SOC analysis, (2) develop a new optimization technique that enhances both machine learning and deep learning model performance, and (3) evaluate the algorithm’s efficiency and accuracy against established optimization methods like Jaya and GridSearchCV. This study focused on India, Australia, and South Africa, countries known for their significant agricultural activities. We introduced a novel optimization technique for both machine learning and deep neural networks, comparing its performance to established methods like the Jaya optimization technique and GridSearchCV. The models evaluated included XGBoost Regression, LightGBM, Gradient Boosting Regression (GBR), Random Forest Regression, Decision Tree Regression, and a Multilayer Perceptron (MLP) model. Our research demonstrated that the proposed optimization algorithm consistently outperformed existing methods in terms of execution time and performance. It achieved results comparable to GridSearchCV, reaching an R<sup>2</sup> of 90.16, which was a significant improvement over the base XGBoost model’s R<sup>2</sup> of 79.08. In deep learning optimization, it significantly outperformed the Jaya algorithm, achieving an R<sup>2</sup> of 61.34 compared to Jaya’s 30.04. Moreover, it was 20–30 times faster than GridSearchCV. Given its speed and accuracy, this algorithm can be applied to real-time data processing in remote sensing satellites. This advanced methodology will greatly benefit the agriculture and farming sectors by providing precise SOC predictions. |
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| institution | OA Journals |
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| language | English |
| publishDate | 2024-11-01 |
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| spelling | doaj-art-7c8dff85db4b41989fadf4df5a32bfd22025-08-20T02:27:39ZengMDPI AGSensors1424-82202024-11-012422731710.3390/s24227317Highly Efficient JR Optimization Technique for Solving Prediction Problem of Soil Organic Carbon on Large ScaleHarsh Vazirani0Xiaofeng Wu1Anurag Srivastava2Debajyoti Dhar3Divyansh Pathak4School of Aerospace, Mechanical and Mechatronic Engineering, University of Sydney, Sydney, NSW 2050, AustraliaSchool of Aerospace, Mechanical and Mechatronic Engineering, University of Sydney, Sydney, NSW 2050, AustraliaAtal Bihari Vajpayee Indian Institute of Information Technology and Management Gwalior, Gwalior 474015, IndiaAtal Bihari Vajpayee Indian Institute of Information Technology and Management Gwalior, Gwalior 474015, IndiaAtal Bihari Vajpayee Indian Institute of Information Technology and Management Gwalior, Gwalior 474015, IndiaWe utilized remote sensing and ground cover data to predict soil organic carbon (SOC) content across a vast geographic region. Employing a combination of machine learning and deep learning techniques, we developed a novel data fusion approach that integrated Digital Elevation Model (DEM) data, MODIS satellite imagery, WOSIS soil profile data, and CHELSA environmental data. This combined dataset, named GeoBlendMDWC, was specifically designed for SOC prediction. The primary aim of this research is to develop and evaluate a novel optimization algorithm for accurate SOC prediction by leveraging multi-source environmental data. Specifically, this study aims to (1) create an integrated dataset combining remote sensing and ground data for comprehensive SOC analysis, (2) develop a new optimization technique that enhances both machine learning and deep learning model performance, and (3) evaluate the algorithm’s efficiency and accuracy against established optimization methods like Jaya and GridSearchCV. This study focused on India, Australia, and South Africa, countries known for their significant agricultural activities. We introduced a novel optimization technique for both machine learning and deep neural networks, comparing its performance to established methods like the Jaya optimization technique and GridSearchCV. The models evaluated included XGBoost Regression, LightGBM, Gradient Boosting Regression (GBR), Random Forest Regression, Decision Tree Regression, and a Multilayer Perceptron (MLP) model. Our research demonstrated that the proposed optimization algorithm consistently outperformed existing methods in terms of execution time and performance. It achieved results comparable to GridSearchCV, reaching an R<sup>2</sup> of 90.16, which was a significant improvement over the base XGBoost model’s R<sup>2</sup> of 79.08. In deep learning optimization, it significantly outperformed the Jaya algorithm, achieving an R<sup>2</sup> of 61.34 compared to Jaya’s 30.04. Moreover, it was 20–30 times faster than GridSearchCV. Given its speed and accuracy, this algorithm can be applied to real-time data processing in remote sensing satellites. This advanced methodology will greatly benefit the agriculture and farming sectors by providing precise SOC predictions.https://www.mdpi.com/1424-8220/24/22/7317GeoBlendMDWCoptimizationregressionsoil organic carbon (SOC) detectionsatellite imagerymachine learning algorithms |
| spellingShingle | Harsh Vazirani Xiaofeng Wu Anurag Srivastava Debajyoti Dhar Divyansh Pathak Highly Efficient JR Optimization Technique for Solving Prediction Problem of Soil Organic Carbon on Large Scale Sensors GeoBlendMDWC optimization regression soil organic carbon (SOC) detection satellite imagery machine learning algorithms |
| title | Highly Efficient JR Optimization Technique for Solving Prediction Problem of Soil Organic Carbon on Large Scale |
| title_full | Highly Efficient JR Optimization Technique for Solving Prediction Problem of Soil Organic Carbon on Large Scale |
| title_fullStr | Highly Efficient JR Optimization Technique for Solving Prediction Problem of Soil Organic Carbon on Large Scale |
| title_full_unstemmed | Highly Efficient JR Optimization Technique for Solving Prediction Problem of Soil Organic Carbon on Large Scale |
| title_short | Highly Efficient JR Optimization Technique for Solving Prediction Problem of Soil Organic Carbon on Large Scale |
| title_sort | highly efficient jr optimization technique for solving prediction problem of soil organic carbon on large scale |
| topic | GeoBlendMDWC optimization regression soil organic carbon (SOC) detection satellite imagery machine learning algorithms |
| url | https://www.mdpi.com/1424-8220/24/22/7317 |
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