Agriculture is essential to a flourishing economy. Although soil is essential for sustainable
food production, its quality can decline as cultivation becomes more intensive and demand …

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

Soil moisture is an important variable of the environment that directly affects hydrological,
ecological, and climatic processes. However, owing to the influence of soil type, soil …

Amidst the growing food demands of an increasing population, agricultural intensification
frequently depends on excessive chemical and fertilizer applications. While this approach …

Rapid and accurate estimation of soil organic matter (SOM) content is of great significance
for agricultural production and carbon stock estimation. Visible-near-infrared spectroscopy …

The research presented in this paper is based on the hypothesis that the machine learning
approach improves the accuracy of soil properties prediction. The correlations obtained in …

Frost resistance in very cold areas is an important engineering issue for the durability of
concrete, and the efficient and accurate prediction of the frost resistance of concrete is a …

Proximal sensors have proven capable of predicting multiple soil properties under different
conditions. However, doubts remain about which sensor is preferable for delivering optimal …

Portable X-ray fluorescence (PXRF) spectrometry has already been established as a rapid
and cost-effective tool for predicting various soil physicochemical properties. This study used …

Soil fertility refers to the ability of soil in a particular area to provide favorable chemical,
physical and biological characteristics that help the plant in its growth. It is affected by …