In recent years, agricultural remote sensing technology has made great progress. The
availability of sensors capable of detecting electromagnetic energy and/or heat emitted by …

Timely and cost-effective crop yield prediction is vital in crop management decision-making.
This study evaluates the efficacy of Unmanned Aerial Vehicle (UAV)-based Vegetation …

Abstract Soybean [Glycine max L.(Merr.)] seed composition is receiving increased attention
among farmers, agronomists, and commodity traders. Increasing the ability to predict seed …

Obtaining soybean genotypes that combine better nutrient uptake, higher oil and protein
levels in the grains, and high grain yield is one of the major challenges for current breeding …

Fast diagnostics from hyperspectral data and machine learning (ML) models to predict
nitrogen (N) and pigment content in maize crops is challenging to optimize nitrogen …

Using multispectral sensors attached to unmanned aerial vehicles (UAVs) can assist in the
collection of morphological and physiological information from several crops. This approach …

The identification of tree species is very useful for the management and monitoring of forest
resources. When paired with machine learning (ML) algorithms, species identification based …

Assessing different levels of red gum lerp psyllid (Glycaspis brimblecombei) can influence
the hyperspectral reflectance of leaves in different ways due to changes in chlorophyll. In …

A current challenge of genetic breeding programs is to increase grain yield and protein
content and at least maintain oil content. However, evaluations of industrial traits are time …

This research aimed to evaluate the accuracy of machine learning techniques in
distinguishing groups soybean genotypes according to grain industrial traits using …