Agriculture plays a vital role in the economic growth of any country. With the increase of
population, frequent changes in climatic conditions and limited resources, it becomes a …

The development of computer aid models for heavy metals (HMs) simulation has been
remarkably advanced over the past two decades. Several machine learning (ML) models …

Over the past two decades soil spectroscopy, particularly, in the infrared range, is becoming
a powerful technique to simplify analysis relative to the traditional chemical methods. It is …

Accurate monitoring of soil salinization plays a key role in the ecological security and
sustainable agricultural development of arid regions. As a branch of artificial intelligence …

Traditional soil salinity studies are time-consuming and expensive, especially over large
areas. This study proposed an innovative deep learning convolutional neural network (DL …

Alum sludge is a byproduct of water treatment plants, and its use as a soil stabilizer has
gained increasing attention due to its economic and environmental benefits. Its application …

Visible and near-infrared (Vis-NIR) spectroscopy technique has been recognized as a cost-
effective, rapid, non-destructive alternative to traditional soil physicochemical analysis to …

Many studies have attempted to predict soil organic matter (SOM), whereas mapping high-
precision and high-resolution SOM maps remains a challenge due to the difficulty of …

Understanding the spatial variability of soil texture classes is essential for agricultural
management and environment sustainability. Sentinel-2 data offer valuable vegetation …

This study investigated the feasibility of using machine learning (ML)-based models to
simulate the behavior of micropollutants (MPs) in the forward osmosis (FO) membrane water …