干旱区生态环境知识资源中心(Arid): An Accurate Approach for Predicting Soil Quality Based on Machine Learning in Drylands

Arid

DOI	10.3390/agriculture14040627
	An Accurate Approach for Predicting Soil Quality Based on Machine Learning in Drylands
	El Behairy, Radwa A.; El Arwash, Hasnaa M.; El Baroudy, Ahmed A.; Ibrahim, Mahmoud M.; Mohamed, Elsayed Said; Rebouh, Nazih Y.; Shokr, Mohamed S.
通讯作者	Shokr, MS
来源期刊	AGRICULTURE-BASEL
EISSN	2077-0472
出版年	2024
卷号	14 期号:4
英文摘要	Nowadays, machine learning (ML) is a useful technology due to its high accuracy in constructing non-linear models and algorithms that can adapt to the complexity and diversity of data. Thus, the current work aimed to predict the soil quality index (SQI) from extensive soil data, achieving high accuracy with the artificial neural networks (ANN) model. However, the efficiency of ANN depends on the accuracy of the data that is prepared for training. For this purpose, MATLAB programming language was used to enable the calculation, classification, and compilation of the results into databases within a few minutes. The proposed MATLAB program was highly efficient, accurate, and quick in calculating soil big data for training the machine compared with traditional methods. The database contains 306 vector sets, 80% of them are used for training and the remaining 20% are reserved for testing. The optimal model obtained comprises one hidden layer with 250 neurons and one output layer with a sigmoid function. The ANN achieved a high coefficient of determination (R2) values for SQI estimation, with around 0.97 and 0.98 for training and testing, respectively. The results indicate that 36.93% of the total soil samples belonged to the very high quality class (C1). In contrast, the high quality (C2), moderate quality (C3), low quality (C4), and very low quality (C5) classes accounted for 10.46%, 31.37%, 20.92%, and 0.33% of the samples, respectively. The high contents of CaCO3, pH, sodium saturation, salinity, and clay content were identified as limiting factors in certain areas. The results of this study indicated high accuracy of soil quality assessment using physical, chemical, and fertility soil features in regression analysis with ANN. This method, which is suitable for arid zones, enhances agricultural productivity and decision-making by identifying critical soil quality categories and constraints.
英文关键词	arid zones artificial neural networks big data MATLAB soil quality index
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:001210749200001
WOS关键词	NEURAL-NETWORK ; INDEX ; AGRICULTURE ; DEGRADATION ; ALGORITHM
WOS类目	Agronomy
WOS研究方向	Agriculture
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/402713
推荐引用方式 GB/T 7714	El Behairy, Radwa A.,El Arwash, Hasnaa M.,El Baroudy, Ahmed A.,et al. An Accurate Approach for Predicting Soil Quality Based on Machine Learning in Drylands[J],2024,14(4).
APA	El Behairy, Radwa A..,El Arwash, Hasnaa M..,El Baroudy, Ahmed A..,Ibrahim, Mahmoud M..,Mohamed, Elsayed Said.,...&Shokr, Mohamed S..(2024).An Accurate Approach for Predicting Soil Quality Based on Machine Learning in Drylands.AGRICULTURE-BASEL,14(4).
MLA	El Behairy, Radwa A.,et al."An Accurate Approach for Predicting Soil Quality Based on Machine Learning in Drylands".AGRICULTURE-BASEL 14.4(2024).