New approach for predicting nitrogen and pigments in maize from hyperspectral data and machine learning models

BC da Silva, R de Mello Prado, FHR Baio… - Remote Sensing …, 2024 - Elsevier
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 …

Karst vegetation coverage detection using UAV multispectral vegetation indices and machine learning algorithm

W Pan, X Wang, Y Sun, J Wang, Y Li, S Li - Plant Methods, 2023 - Springer
Background Karst vegetation is of great significance for ecological restoration in karst areas.
Vegetation Indices (VIs) are mainly related to plant yield which is helpful to understand the …

Eucalyptus Species Discrimination Using Hyperspectral Sensor Data and Machine Learning

L Pereira Ribeiro Teodoro, R Estevão, DC Santana… - Forests, 2023 - mdpi.com
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 …

Towards a Spectral Library of Medicinal and Aromatic Plant species (MAPs): Plant Discrimination and Wavelength Selection

S El Azizi, M Amharref, H Es-Saouini, AS Bernoussi… - Microchemical …, 2024 - Elsevier
The recognition and identification of Medicinal and Aromatic Plants species (MAPs) is a
challenge for researchers and professionals in the field. To address this issue, this study …

Evaluation of soybean plants affected by Aphelenchoides besseyi using remote sensing and machine learning techniques

JL Della-Silva, V de Oliveira Faleiro… - Remote Sensing …, 2025 - Elsevier
Abstract Soybeans (Glycine max (L.) Merrill) are a major player in food security, and pest
loss control is a major focus of research and technological development by the agricultural …