Estimating Above-Ground Biomass of Maize Using Features Derived From UAV-Based RGB Imagery

Remote Sensing - Switzerland
doi 10.3390/rs11111261
Full Text
Abstract

Available in full text

Date
Authors
Publisher

MDPI AG

Related search

Modeling Maize Above-Ground Biomass Based on Machine Learning Approaches Using UAV Remote-Sensing Data

Plant Methods
BiotechnologyPlant ScienceGenetics
2019English

Estimating Crop Density From Multi-Spectral Uav Imagery in Maize Crop

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
2019English

Automatic Apple Tree Blossom Estimation From Uav RGB Imagery

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
2019English

Estimating Biomass and Nitrogen Amount of Barley and Grass Using UAV and Aircraft Based Spectral and Photogrammetric 3D Features

Remote Sensing
EarthPlanetary Sciences
2018English

Classification of Maize in Complex Smallholder Farming Systems Using UAV Imagery

Drones
2018English

Towards Remote Estimation of Radiation Use Efficiency in Maize Using UAV-Based Low-Cost Camera Imagery

Agronomy
AgronomyCrop Science
2018English

Automated Measurement of Plant Height of Wheat Genotypes Using a DSM Derived From Uav Imagery

Proceedings
2018English

Estimation of Forest Above-Ground Biomass by Geographically Weighted Regression and Machine Learning With Sentinel Imagery

Forests
Forestry
2018English

Estimation of Maize Above-Ground Biomass Based on Stem-Leaf Separation Strategy Integrated With LiDAR and Optical Remote Sensing Data

PeerJ
GeneticsMolecular BiologyBiochemistryBiological SciencesMedicineAgriculturalNeuroscience
2019English

Amanote Research

Note-taking for researchers

Follow Amanote

© 2024 Amaplex Software S.P.R.L. All rights reserved.

Privacy PolicyRefund Policy