Using spectral data to quantify nitrogen (N), phosphorus (P), and potassium (K) contents in
soybean plants can help breeding programs develop fertilizer-efficient genotypes …

Coffee has high relevance in the Brazilian agricultural scenario, as Brazil is the largest
producer and exporter of coffee in the world. Strategies to advance the production of coffee …

Predicting maize yield using spectral information, temperature, and different irrigation
management through machine learning algorithms provide information in a fast, accurate …

The last 20 years have been a period of significant advancement in the tools available for
remote sensing of soybean [Glycine max (L.) Merr.] in terms of price, ease of use, quality of …

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 …

Soybean is one of the most important oilseed crops grown worldwide. However, abiotic
stresses such as drought and salinity can seriously affect soybean production, especially in …

The mango fruit plays a crucial role in providing essential nutrients to the human body and
Pakistani mangoes are highly coveted worldwide. The escalating demand for agricultural …

Accurate extraction of soybean planting areas is inherently challenging owing to the close
similarity in growth stage and spectral characteristics with maize, particularly during the early …