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

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. © 2024 by the authors.

Authors
El Behairy R.A. , El Arwash H.M. , El Baroudy A.A. , Ibrahim M.M. , Mohamed E.S. , Kucher D.E. , Shokr M.S.
Journal
Publisher
MDPI AG
Number of issue
8
Language
English
Status
Published
Number
1682
Volume
14
Year
2024
Organizations
  • 1 Soil and Water Department, Faculty of Agriculture, Tanta University, Tanta, 31527, Egypt
  • 2 Mechatronics Engineering Department, Alexandria Higher Institute of Engineering & Technology (AIET), Alexandria, 21311, Egypt
  • 3 National Authority for Remote Sensing and Space Sciences, Cairo, 1564, Egypt
  • 4 Department of Environmental Management, Institute of Environmental Engineering, People’s Friendship University of Russia (RUDN University), 6 Miklukho-Maklaya Street, Moscow, 117198, Russian Federation
Keywords
deep learning; evaluation methods; food security; machine learning; soil quality indicators; sustainable use
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