干旱区生态环境知识资源中心(Arid): Assessment of Soil Suitability Using Machine Learning in Arid and Semi-Arid Regions

Arid

DOI	10.3390/agronomy13010165
	Assessment of Soil Suitability Using Machine Learning in Arid and Semi-Arid Regions
	Ismaili, Maryem; Krimissa, Samira; Namous, Mustapha; Htitiou, Abdelaziz; Abdelrahman, Kamal; Fnais, Mohammed S.; Lhissou, Rachid; Eloudi, Hasna; Faouzi, Elhousna; Benabdelouahab, Tarik
通讯作者	Ismaili, M
来源期刊	AGRONOMY-BASEL
EISSN	2073-4395
出版年	2023
卷号	13 期号:1
英文摘要	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.
英文关键词	precision agriculture sentinel-2 random forest XgbTree digital soil mapping remote sensing agricultural management
类型	Article
语种	英语
开放获取类型	Green Published, gold
收录类别	SCI-E
WOS记录号	WOS:000914364000001
WOS关键词	MODIS TIME-SERIES ; CLASSIFICATION ; SALINITY ; EROSION ; SYSTEMS
WOS类目	Agronomy ; Plant Sciences
WOS研究方向	Agriculture ; Plant Sciences
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/395177
推荐引用方式 GB/T 7714	Ismaili, Maryem,Krimissa, Samira,Namous, Mustapha,et al. Assessment of Soil Suitability Using Machine Learning in Arid and Semi-Arid Regions[J],2023,13(1).
APA	Ismaili, Maryem.,Krimissa, Samira.,Namous, Mustapha.,Htitiou, Abdelaziz.,Abdelrahman, Kamal.,...&Benabdelouahab, Tarik.(2023).Assessment of Soil Suitability Using Machine Learning in Arid and Semi-Arid Regions.AGRONOMY-BASEL,13(1).
MLA	Ismaili, Maryem,et al."Assessment of Soil Suitability Using Machine Learning in Arid and Semi-Arid Regions".AGRONOMY-BASEL 13.1(2023).