Assessment of Soil Suitability Using Machine Learning in Arid and Semi-Arid Regions

被引:10
|
作者
Ismaili, Maryem [1 ,2 ]
Krimissa, Samira [1 ]
Namous, Mustapha [1 ]
Htitiou, Abdelaziz [1 ]
Abdelrahman, Kamal [3 ]
Fnais, Mohammed S. [3 ]
Lhissou, Rachid [4 ]
Eloudi, Hasna [5 ]
Faouzi, Elhousna [1 ]
Benabdelouahab, Tarik [2 ]
机构
[1] Sultan Moulay Slimane Univ, Dept Geol, Lab Data4Earth, Beni Mellal 23000, Morocco
[2] Natl Agron Res Inst, Rabat 10000, Morocco
[3] King Saud Univ, Coll Sci, Dept Geol & Geophys, POB 2455, Riyadh 11451, Saudi Arabia
[4] INRS, Ctr ETE, 490 Rue Couronne, Quebec City, PQ GIK 9A9, Canada
[5] Ibn Zohr Univ, Dept Geol, Agadir 80000, Morocco
来源
AGRONOMY-BASEL | 2023年 / 13卷 / 01期
关键词
precision agriculture; sentinel-2; random forest; XgbTree; digital soil mapping; remote sensing; agricultural management; MODIS TIME-SERIES; CLASSIFICATION; SALINITY; EROSION; SYSTEMS;
D O I
10.3390/agronomy13010165
中图分类号
S3 [农学(农艺学)];
学科分类号
0901 ;
摘要
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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页数:16
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