How Can Soil Quality Be Accurately and Quickly Studied? A Review

被引:10
作者
El Behairy, Radwa A. [1 ]
El Arwash, Hasnaa M. [2 ]
El Baroudy, Ahmed A. [1 ]
Ibrahim, Mahmoud M. [1 ]
Mohamed, Elsayed Said [3 ,4 ]
Kucher, Dmitry E. [4 ]
Shokr, Mohamed S. [1 ]
机构
[1] Tanta Univ, Fac Agr, Soil & Water Dept, Tanta 31527, Egypt
[2] Alexandria Higher Inst Engn & Technol AIET, Mechatron Engn Dept, Alexandria 21311, Egypt
[3] Natl Author Remote Sensing & Space Sci, Cairo 1564, Egypt
[4] Peoples Friendship Univ Russia RUDN Univ, Inst Environm Engn, Dept Environm Management, 6 Miklukho Maklaya St, Moscow 117198, Russia
来源
AGRONOMY-BASEL | 2024年 / 14卷 / 08期
关键词
deep learning; evaluation methods; food security; machine learning; soil quality indicators; sustainable use; MANAGEMENT ASSESSMENT FRAMEWORK; DIFFERENT LAND USES; HEAVY-METALS; GEOSTATISTICAL ANALYSES; SPATIAL-DISTRIBUTION; AGRICULTURAL LAND; RISK-ASSESSMENT; POWER-PLANT; INDEXES; MULTIVARIATE;
D O I
10.3390/agronomy14081682
中图分类号
S3 [农学(农艺学)];
学科分类号
0901 ;
摘要
Evaluating soil quality is crucial for ensuring the sustainable use of agricultural lands. This review examines the definition, evaluation methods, indicator selection, and relevant case studies. The concept of soil quality supplements soil science research by deepening our understanding of soils and aiding in the allocation of resources as agriculture intensifies to meet rising global demand. Soil quality provides a framework for educating stakeholders about the essential functions of soils and offers a tool for assessing and comparing different management techniques. Regular evaluation of soil quality is vital for maintaining high crop yields and addressing the gap between production and consumption. Nowadays, many researchers have explored machine learning (ML) and deep learning (DL) techniques and various algorithms to model and predict soil quality with satisfactory results. These chosen indicators can be influenced by chemical, biological, or physical features. This paper compares ML and DL with traditional methods, examining their features, limitations, different categories of machine learning, and their applications in soil quality assessment. Finally, we show that predicting soil quality has the potential to be extremely accurate and efficient with ML and DL. This distinguishes the application of DL and ML from other approaches since they can anticipate the soil quality index without the need for more intricate computations. Our suggestion for future studies is to evaluate soil quality over broader regions and predict it by using more accurate, modern, and faster methods, using a variety of activation functions and algorithms.
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页数:21
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