An Overview of the Special Issue on “Precision Agriculture Using Hyperspectral Images”
Author:
Affiliation:
1. Institute for BioEconomy, National Research Council of Italy, Via Paolo Gaifami 18, 95126 Catania, Italy
Abstract
Publisher
MDPI AG
Subject
General Earth and Planetary Sciences
Link
https://www.mdpi.com/2072-4292/15/7/1917/pdf
Reference13 articles.
1. Li, K.-Y., Sampaio de Lima, R., Burnside, N.G., Vahtmäe, E., Kutser, T., Sepp, K., Cabral Pinheiro, V.H., Yang, M.-D., Vain, A., and Sepp, K. (2022). Toward Automated Machine Learning-Based Hyperspectral Image Analysis in Crop Yield and Biomass Estimation. Remote Sens., 14.
2. Wei, L., Wang, K., Lu, Q., Liang, Y., Li, H., Wang, Z., Wang, R., and Cao, L. (2021). Crops Fine Classification in Airborne Hyperspectral Imagery Based on Multi-Feature Fusion and Deep Learning. Remote Sens., 13.
3. Sadeghi-Tehran, P., Virlet, N., and Hawkesford, M.J. (2021). A Neural Network Method for Classification of Sunlit and Shaded Components of Wheat Canopies in the Field Using High-Resolution Hyperspectral Imagery. Remote Sens., 13.
4. Ma, D., Rehman, T.U., Zhang, L., Maki, H., Tuinstra, M.R., and Jin, J. (2021). Modeling of Diurnal Changing Patterns in Airborne Crop Remote Sensing Images. Remote Sens., 13.
5. Chancia, R., Bates, T., Vanden Heuvel, J., and van Aardt, J. (2021). Assessing Grapevine Nutrient Status from Unmanned Aerial System (UAS) Hyperspectral Imagery. Remote Sens., 13.
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