article · Agronomy
This research aimed to improve agricultural production and food security by developing a method for predicting soil suitability in arid and semi-arid regions. The study utilised machine learning algorithms, including Random Forest, XgbTree, Artificial Neural Networks, K-Nearest Neighbours, and Support Vector Machines. These models were trained using 238 suitability points, 14 physico-chemical parameters, and 4 remotely sensed phenological parameters. Findings indicated that phenological parameters were the most influential in predicting soil suitability. The XgbTree algorithm demonstrated the best performance, achieving an Area Under the Curve (AUC) of 0.97. The results confirm the excellent capability of machine learning models to predict soil suitability, offering a valuable tool for sustainable agricultural development and land assessment.
Accurately predicting soil suitability is vital for increasing agricultural output and ensuring food security, particularly in regions facing water scarcity. This research offers a data-driven method to identify the best areas for cultivation, helping farmers and agricultural planners make informed decisions to optimise land use and improve livelihoods.
This research provides an applied machine learning approach for creating precise soil-suitability maps. This could be integrated into decision-support systems for agricultural planners, land management organisations, and large-scale farming operations. The technology appears to be an applied research outcome, ready for further development into practical tools for land assessment and sustainable agricultural planning.
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Increasing agricultural production is a major concern that aims to increase income, reduce hunger, and improve other measures of well-being. Recently, the prediction of soil-suitability has become a primary topic of rising concern among academics, policymakers, and socio-economic analysts to assess dynamics of the agricultural production. This work aims to use physico-chemical and remotely sensed phenological parameters to produce soil-suitability maps (SSM) based on Machine Learning (ML) Algorithms in a semi-arid and arid region. Towards this goal an inventory of 238 suitability points has been carried out in addition to14 physico-chemical and 4 phenological parameters that have been used as inputs of machine-learning approaches which are five MLA prediction, namely RF, XgbTree, ANN, KNN and SVM. The results showed that phenological parameters were found to be the most influential in soil-suitability prediction. The validation of the Receiver Operating Characteristics (ROC) curve approach indicates an area under the curve and an AUC of more than 0.82 for all models. The best results were obtained using the XgbTree with an AUC = 0.97 in comparison to other MLA. Our findings demonstrate an excellent ability for ML models to predict the soil-suitability using physico-chemical and phenological parameters. The approach developed to map the soil-suitability is a valuable tool for sustainable agricultural development, and it can play an effective role in ensuring food security and conducting a land agriculture assessment.
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DOI: 10.3390/agronomy13010165
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