干旱区生态环境知识资源中心(Arid): Integration Vis-NIR Spectroscopy and Artificial Intelligence to Predict Some Soil Parameters in Arid Region: A Case Study of Wadi Elkobaneyya, South Egypt

Arid

DOI	10.3390/agronomy13030935
	Integration Vis-NIR Spectroscopy and Artificial Intelligence to Predict Some Soil Parameters in Arid Region: A Case Study of Wadi Elkobaneyya, South Egypt
	El-Sayed, Moatez A.; Abd-Elazem, Alaa H.; Moursy, Ali R. A.; Mohamed, Elsayed Said; Kucher, Dmitry E.; Fadl, Mohamed E.
通讯作者	Mohamed, ES
来源期刊	AGRONOMY-BASEL
EISSN	2073-4395
出版年	2023
卷号	13 期号:3
英文摘要	Understanding and determining soil properties is reflected in improving farm management and crop production. Soil salinity, pH and calcium carbonate are among the factors affecting the soil's physical and chemical properties. Hence, their estimation is very important for agricultural management, especially in arid regions (Wadi Elkobaneyya valley, located in the northwest of Aswan Governorate, Upper Egypt). The study objectives were to characterize and develop prediction models for soil salinity, pH and calcium carbonate (CaCO3) using integration soil analysis and spectral reflectance vis-NIR spectroscopy. To achieve the study objectives, three multivariate regression models: Partial Least Squares Regression (PLSR), Multivariate Adaptive Regression Splines (MARS) and Least Square-Support Vector Regression (LS-SVR)); and two machine learning algorithms, Random Forest (RF) and Artificial Neural Networks (ANN) were used. Ninety-six surface soil samples were collected from the study area at depths 0-5 cm. The data were divided into a calibration dataset (70% of the total) and a validation dataset (30% of the total dataset). The obtained results represent that the PLSR model was the best model for soil pH parameters where R-2 of calibration and validation predictability = 0.68 and 0.52, respectively. The LS-SVR model was the best model to predict soil Electrical Conductivity (EC) and soil Calcium Carbonate (CaCO3) content, with R-2 0.70 and 0.74 for calibration and R-2 0.26 and 0.47 for validation, respectively. On the other hand, the results of the implemented machine learning algorithm model showed that RF was the best model to predict soil pH and CaCO3, as the R-2 was 0.82 for calibration and 0.57 for validation, respectively. Nevertheless, the best model for predicting soil EC was ANN, with an R-2 of 0.96 for calibration and 64 for validation. The results show the advantages of machine learning models for predicting soil EC, pH and CaCO3 by Vis-NIR spectroscopy. Therefore, Vis-NIR spectroscopy is considered faster and more cost-efficient and can be further used in environmental monitoring and precision farming.
英文关键词	soil parameters vis-NIR statistical parameters remote sensing Wadi Elkobaneyya
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000956892000001
WOS关键词	NEAR-INFRARED SPECTROSCOPY ; REFLECTANCE SPECTROSCOPY ; REGRESSION ; GIS ; ACCURACY ; MODELS ; FIELD ; CLAY
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/2XILL650/395197
推荐引用方式 GB/T 7714	El-Sayed, Moatez A.,Abd-Elazem, Alaa H.,Moursy, Ali R. A.,et al. Integration Vis-NIR Spectroscopy and Artificial Intelligence to Predict Some Soil Parameters in Arid Region: A Case Study of Wadi Elkobaneyya, South Egypt[J],2023,13(3).
APA	El-Sayed, Moatez A.,Abd-Elazem, Alaa H.,Moursy, Ali R. A.,Mohamed, Elsayed Said,Kucher, Dmitry E.,&Fadl, Mohamed E..(2023).Integration Vis-NIR Spectroscopy and Artificial Intelligence to Predict Some Soil Parameters in Arid Region: A Case Study of Wadi Elkobaneyya, South Egypt.AGRONOMY-BASEL,13(3).
MLA	El-Sayed, Moatez A.,et al."Integration Vis-NIR Spectroscopy and Artificial Intelligence to Predict Some Soil Parameters in Arid Region: A Case Study of Wadi Elkobaneyya, South Egypt".AGRONOMY-BASEL 13.3(2023).