DETECTION OF DISEASE SYMPTOMS ON HYPERSPECTRAL 3D PLANT MODELS

10 citations as recorded by crossref.

1. Deploying Fourier Coefficients to Unravel Soybean Canopy Diversity T. Jubery et al. 10.3389/fpls.2016.02066
2. Hyperspectral imaging for high-throughput vitality monitoring in ornamental plant production M. Ruett et al. 10.1016/j.scienta.2021.110546
3. Assessing wheat responses to soil acidification and liming using hyperspectral imaging R. Hume et al. 10.1007/s11104-024-07029-3
4. Spectroscopy Imaging Techniques as In Vivo Analytical Tools to Detect Plant Traits R. Hernanda et al. 10.3390/app131810420
5. Plant disease detection by hyperspectral imaging: from the lab to the field A. Mahlein et al. 10.1017/S2040470017001248
6. Proximal Hyperspectral Imaging Detects Diurnal and Drought-Induced Changes in Maize Physiology S. Mertens et al. 10.3389/fpls.2021.640914
7. Combined Use of 3D and HSI for the Classification of Printed Circuit Board Components S. Polat et al. 10.3390/app11188424
8. Detection of a vascular wilt disease in potato (‘Blackleg’) based on UAV hyperspectral imagery: Can structural features from LiDAR or SfM improve plant-wise classification accuracy? M. Franceschini et al. 10.1016/j.compag.2024.109527
9. First Form, Then Function: 3D Reconstruction of Cucumber Plants (Cucumis sativus L.) Allows Early Detection of Stress Effects through Leaf Dimensions D. Moualeu-Ngangué et al. 10.3390/rs14051094
10. Soybean Canopy Stress Classification Using 3D Point Cloud Data T. Young et al. 10.3390/agronomy14061181