Application of Artificial Intelligence and Sensor Fusion for Soil Organic Matter Prediction
Soil organic matter (SOM) is one of the best indicators to assess soil health and understand soil productivity and fertility. Therefore, measuring SOM content is a fundamental practice in soil science and agricultural research. The traditional approach (oven-dry) of measuring SOM is a costly, arduou...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-04-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/24/7/2357 |
| _version_ | 1797211951378464768 |
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| author | Md Jasim Uddin Jordan Sherrell Anahita Emami Meysam Khaleghian |
| author_facet | Md Jasim Uddin Jordan Sherrell Anahita Emami Meysam Khaleghian |
| author_sort | Md Jasim Uddin |
| collection | DOAJ |
| description | Soil organic matter (SOM) is one of the best indicators to assess soil health and understand soil productivity and fertility. Therefore, measuring SOM content is a fundamental practice in soil science and agricultural research. The traditional approach (oven-dry) of measuring SOM is a costly, arduous, and time-consuming process. However, the integration of cutting-edge technology can significantly aid in the prediction of SOM, presenting a promising alternative to traditional methods. In this study, we tested the hypothesis that an accurate estimate of SOM might be obtained by combining the ground-based sensor-captured soil parameters and soil analysis data along with drone images of the farm. The data are gathered using three different methods: ground-based sensors detect soil parameters such as temperature, pH, humidity, nitrogen, phosphorous, and potassium of the soil; aerial photos taken by UAVs display the vegetative index (NDVI); and the Haney test of soil analysis reports measured in a lab from collected samples. Our datasets combined the soil parameters collected using ground-based sensors, soil analysis reports, and NDVI content of farms to perform the data analysis to predict SOM using different machine learning algorithms. We incorporated regression and ANOVA for analyzing the dataset and explored seven different machine learning algorithms, such as linear regression, Ridge regression, Lasso regression, random forest regression, Elastic Net regression, support vector machine, and Stochastic Gradient Descent regression to predict the soil organic matter content using other parameters as predictors. |
| first_indexed | 2024-04-24T10:34:39Z |
| format | Article |
| id | doaj.art-71ace81ed1f14090b1f7def5e3ea8aa3 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-24T10:34:39Z |
| publishDate | 2024-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-71ace81ed1f14090b1f7def5e3ea8aa32024-04-12T13:26:51ZengMDPI AGSensors1424-82202024-04-01247235710.3390/s24072357Application of Artificial Intelligence and Sensor Fusion for Soil Organic Matter PredictionMd Jasim Uddin0Jordan Sherrell1Anahita Emami2Meysam Khaleghian3College of Science and Engineering, Texas State University, San Marcos, TX 78666, USACollege of Science and Engineering, Texas State University, San Marcos, TX 78666, USACollege of Science and Engineering, Texas State University, San Marcos, TX 78666, USACollege of Science and Engineering, Texas State University, San Marcos, TX 78666, USASoil organic matter (SOM) is one of the best indicators to assess soil health and understand soil productivity and fertility. Therefore, measuring SOM content is a fundamental practice in soil science and agricultural research. The traditional approach (oven-dry) of measuring SOM is a costly, arduous, and time-consuming process. However, the integration of cutting-edge technology can significantly aid in the prediction of SOM, presenting a promising alternative to traditional methods. In this study, we tested the hypothesis that an accurate estimate of SOM might be obtained by combining the ground-based sensor-captured soil parameters and soil analysis data along with drone images of the farm. The data are gathered using three different methods: ground-based sensors detect soil parameters such as temperature, pH, humidity, nitrogen, phosphorous, and potassium of the soil; aerial photos taken by UAVs display the vegetative index (NDVI); and the Haney test of soil analysis reports measured in a lab from collected samples. Our datasets combined the soil parameters collected using ground-based sensors, soil analysis reports, and NDVI content of farms to perform the data analysis to predict SOM using different machine learning algorithms. We incorporated regression and ANOVA for analyzing the dataset and explored seven different machine learning algorithms, such as linear regression, Ridge regression, Lasso regression, random forest regression, Elastic Net regression, support vector machine, and Stochastic Gradient Descent regression to predict the soil organic matter content using other parameters as predictors.https://www.mdpi.com/1424-8220/24/7/2357Artificial Intelligencesensor fusionsoil organic matter |
| spellingShingle | Md Jasim Uddin Jordan Sherrell Anahita Emami Meysam Khaleghian Application of Artificial Intelligence and Sensor Fusion for Soil Organic Matter Prediction Sensors Artificial Intelligence sensor fusion soil organic matter |
| title | Application of Artificial Intelligence and Sensor Fusion for Soil Organic Matter Prediction |
| title_full | Application of Artificial Intelligence and Sensor Fusion for Soil Organic Matter Prediction |
| title_fullStr | Application of Artificial Intelligence and Sensor Fusion for Soil Organic Matter Prediction |
| title_full_unstemmed | Application of Artificial Intelligence and Sensor Fusion for Soil Organic Matter Prediction |
| title_short | Application of Artificial Intelligence and Sensor Fusion for Soil Organic Matter Prediction |
| title_sort | application of artificial intelligence and sensor fusion for soil organic matter prediction |
| topic | Artificial Intelligence sensor fusion soil organic matter |
| url | https://www.mdpi.com/1424-8220/24/7/2357 |
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