ArticleScientific reports2025
Predicting soil organic carbon with ensemble learning techniques by using satellite images for precision farming.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Unravelling depth-dependent pedoclimatic controls on measurable soil organic carbon fractions across climatic gradients in Australian agricultural soils.Scientific reports · 2026Article
- Soil organic carbon modeling in cropland under several climatic scenarios using machine learning in western India.Scientific reports · 2026Article
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Authors and funding
2 authors.
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Abstract
Soil plays a major role in the agricultural system. Soil composition detection can help farmers to take appropriate decision leading to proper crop growth. Soil organic carbon is crucial for many soil activities and ecological characteristics, is at the centre of sustainable agriculture. The goal of the research is to create a system for evaluating soil organic carbon based on topographic features and soil properties incorporating machine learning algorithms. A group of covariates has been chosen to function as potential predictor factors for soil properties, including four topographical variables, two soil-related remote sensing indices, and four climate variables which were retrieved from satellite images. Along with predictor variables, soil health card data as dependent variable was used for training the model. It was notified that bagging and boosting showed good results for training than for testing. XGBoost algorithm noted highest R
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