How Can Soil Quality Be Accurately and Quickly Studied? A Review DOI Creative Commons
Radwa A. El Behairy, Hasnaa M. El Arwash, Ahmed A. El Baroudy

и другие.

Agronomy, Год журнала: 2024, Номер 14(8), С. 1682 - 1682

Опубликована: Июль 30, 2024

Evaluating soil quality is crucial for ensuring the sustainable use of agricultural lands. This review examines definition, evaluation methods, indicator selection, and relevant case studies. The concept supplements science research by deepening our understanding soils aiding in allocation resources as agriculture intensifies to meet rising global demand. Soil provides a framework educating stakeholders about essential functions offers tool assessing comparing different management techniques. Regular vital maintaining high crop yields addressing gap between production consumption. Nowadays, many researchers have explored machine learning (ML) deep (DL) techniques various algorithms model predict with satisfactory results. These chosen indicators can be influenced chemical, biological, or physical features. paper compares ML DL traditional examining their features, limitations, categories learning, applications assessment. Finally, we show that predicting has potential extremely accurate efficient DL. distinguishes application from other approaches since they anticipate index without need more intricate computations. Our suggestion future studies evaluate over broader regions it using accurate, modern, faster variety activation algorithms.

Язык: Английский

How Can Soil Quality Be Accurately and Quickly Studied? A Review DOI Creative Commons
Radwa A. El Behairy, Hasnaa M. El Arwash, Ahmed A. El Baroudy

и другие.

Agronomy, Год журнала: 2024, Номер 14(8), С. 1682 - 1682

Опубликована: Июль 30, 2024

Evaluating soil quality is crucial for ensuring the sustainable use of agricultural lands. This review examines definition, evaluation methods, indicator selection, and relevant case studies. The concept supplements science research by deepening our understanding soils aiding in allocation resources as agriculture intensifies to meet rising global demand. Soil provides a framework educating stakeholders about essential functions offers tool assessing comparing different management techniques. Regular vital maintaining high crop yields addressing gap between production consumption. Nowadays, many researchers have explored machine learning (ML) deep (DL) techniques various algorithms model predict with satisfactory results. These chosen indicators can be influenced chemical, biological, or physical features. paper compares ML DL traditional examining their features, limitations, categories learning, applications assessment. Finally, we show that predicting has potential extremely accurate efficient DL. distinguishes application from other approaches since they anticipate index without need more intricate computations. Our suggestion future studies evaluate over broader regions it using accurate, modern, faster variety activation algorithms.

Язык: Английский

Процитировано

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