Unmanned Aerial Vehicle-Measured Multispectral Vegetation Indices for Predicting LAI, SPAD Chlorophyll, and Yield of Maize
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- Brewer, K. & Clulow, A. & Sibanda, M. & Gokool, S. & Naiken, V. & Mabhaudhi, Tafadzwanashe, 2022. "Predicting the chlorophyll content of maize over phenotyping as a proxy for crop health in smallholder farming systems," Papers published in Journals (Open Access), International Water Management Institute, pages 1-14(3):518.
- Sellaperumal Pazhanivelan & Ramalingam Kumaraperumal & P. Shanmugapriya & N. S. Sudarmanian & A. P. Sivamurugan & S. Satheesh, 2023. "Quantification of Biophysical Parameters and Economic Yield in Cotton and Rice Using Drone Technology," Agriculture, MDPI, vol. 13(9), pages 1-16, August.
- Khalifa M. Al-Kindi & Rahma Al Nadhairi & Suleiman Al Akhzami, 2023. "Dynamic Change in Normalised Vegetation Index (NDVI) from 2015 to 2021 in Dhofar, Southern Oman in Response to the Climate Change," Agriculture, MDPI, vol. 13(3), pages 1-24, February.
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- Dennis Ccopi & Kevin Ortega & Italo Castañeda & Claudia Rios & Lucia Enriquez & Solanch Patricio & Zoila Ore & David Casanova & Alex Agurto & Noemi Zuñiga & Julio Urquizo, 2024. "Using UAV Images and Phenotypic Traits to Predict Potato Morphology and Yield in Peru," Agriculture, MDPI, vol. 14(11), pages 1-23, October.
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Keywords
remote sensing; multispectral images; leaf area index; chlorophyll value; stepwise regression; maize;All these keywords.
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