In cotton, an optimal balance between vegetative and reproductive growth will lead to high
yields and water-use efficiency. Remote sensing estimations of vegetation variables such as …

Public domain synthetic-aperture radar (SAR) imagery, particularly from Sentinel-1, has
widened the scope of day and night vegetation monitoring, even when cloud cover limits …

Recent advances in remote sensing and machine learning show potential for improving
irrigation use efficiency. In this study, two independent methods to determine the irrigation …

Monitoring agricultural water usage with remote sensing technology has shown to be an
effective strategy for dealing with water security in the digital era. Given the massive …

In this paper, we propose an innovative approach for robust prediction of processing tomato
yield using open-source AutoML techniques and statistical analysis. Sentinel-2 satellite …

Although it is common to consider crop height in agricultural management, variation in plant
height within the field is seldom addressed because it is challenging to assess from discrete …

Spatial information embedded in a crop model can improve yield prediction. Leaf area index
(LAI) is a well-known crop variable often estimated from remote-sensing data and used as …

Satellite remote sensing is a useful tool for estimating crop variables, particularly Leaf Area
Index (LAI), which plays a pivotal role in monitoring crop development. The goal of this study …

Smartphone application usage has increased exponentially over the past decade. The
potentiality of smartphone applications as a tool for various decision-making processes is …

This paper presents the development and update of a multi-scale yield prediction model for
processing tomatoes. The study was carried out under the EU-funded programme “Support …