Modelling of Intra-Field Winter Wheat Crop Growth Variability Using in Situ Measurements, Unmanned Aerial Vehicle-Derived Vegetation Indices, Soil Properties, and Machine Learning Algorithms-Reference-Cited by-同舟云学术

Modelling of Intra-Field Winter Wheat Crop Growth Variability Using in Situ Measurements, Unmanned Aerial Vehicle-Derived Vegetation Indices, Soil Properties, and Machine Learning Algorithms

Published:2023-11-21 Issue: Volume:3 Page:24
ISSN:
Container-title:ECRS 2023
language:
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Author:

Nduku Lwandile¹²^ORCID,Munghemezulu Cilence²^ORCID,Mashaba-Munghemezulu Zinhle²^ORCID,Masiza Wonga²^ORCID,Ratshiedana Phathutshedzo Eugene²^ORCID,Kalumba Ahmed Mukalazi³^ORCID,Chirima Johannes George¹²^ORCID

Affiliation:

1. Department of Geography, Geoinformatics & Meteorology, University of Pretoria, Pretoria 0028, South Africa

2. Geoinformation Science Division, Agricultural Research Council, Institute for Soil, Natural Resources and Engineering, Pretoria 0001, South Africa

3. GACCES Lab., Department of Geography & Environmental Science, University of Fort Hare, Alice 5700, South Africa

Publisher

MDPI

Link

https://www.mdpi.com/2673-4931/29/1/24/pdf

Reference5 articles.

1. Global Research Trends for Unmanned Aerial Vehicle Remote Sensing Application in Wheat Crop Monitoring;Nduku;Geomatics,2023

2. Salinity Stress in Wheat (Triticum Aestivum L.) in the Changing Climate: Adaptation and Management Strategies;Islam;Front. Agron.,2021

3. Goffart, D., Dvorakova, K., Crucil, G., Curnel, Y., Limbourg, Q., Van Oost, K., and van Wesemael, B. (2022). UAV Remote Sensing for Detecting Within-Field Spatial Variation of Winter Wheat Growth and Links to Soil Properties and Historical Management Practices. A Case Study on Belgian Loamy Soil. Remote Sens., 14.

4. Skendžić, S., Zovko, M., Lešić, V., Pajač Živković, I., and Lemić, D. (2023). Detection and Evaluation of Environmental Stress in Winter Wheat Using Remote and Proximal Sensing Methods and Vegetation Indices—A Review. Diversity, 15.

5. Upreti, D., Huang, W., Kong, W., Pascucci, S., Pignatti, S., Zhou, X., and Casa, R. (2019). A Comparison of Hybrid Machine Learning Algorithms for the Retrieval of Wheat Biophysical Variables from Sentinel-2. Remote Sens., 11.

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1. Synergetic Use of Sentinel-1 and Sentinel-2 Data for Wheat-Crop Height Monitoring Using Machine Learning;AgriEngineering;2024-04-22

2. Field-Scale Winter Wheat Growth Prediction Applying Machine Learning Methods with Unmanned Aerial Vehicle Imagery and Soil Properties;Land;2024-02-27