Artificial neural networks (ANN), machine learning (ML), deep learning (DL), and ensemble
learning (EL) are four outstanding approaches that enable algorithms to extract information …

Understanding yield limiting factors requires high resolution multi-layer information about
factors affecting crop growth and yield. Therefore, on-line proximal soil sensing for …

The cuurent study was performed to predict spatial distribution of some heavy metals (Ni, Fe,
Cu, Mn) in western Iran, using environmental covariates and applying two machine learning …

Crop yield is controlled by different environmental factors. Multi-source data for site-specific
soils, climates, and remotely sensed vegetation indices are essential for yield prediction …

Microbial communities play a seminal role in biogeochemical cycles and can be influenced
by both biotic and abiotic factors. A few studies highlighting the importance of topographic …

This paper introduces the narrow strip irrigation (NSI) method and aims to estimate water-
use efficiency (WUE) and yield in apple orchards under NSI in the Miandoab region located …

This study evaluated the performance of deep learning (DL), artificial neural network (ANN)
and k-nearest neighbour (kNN) models to estimate field capacity (FC) and permanent wilting …

Detailed soil surveys involve costly and time-consuming work and require expert
knowledge. Since soil surveys provide information to meet a wide range of needs, new …

Yield prediction is essential for production planning, resource management, and competitive
advantages. The use of vegetation indices and machine learning for yield prediction is rapid …

The soil carbon pool which is the sum of soil organic carbon (SOC) and soil inorganic
carbon (SIC) is the second largest active store of carbon after the oceans and it is an …