AUTOMATIC APPLE TREE BLOSSOM ESTIMATION FROM UAV RGB IMAGERY

8 citations as recorded by crossref.

1. Phenological and Biophysical Mediterranean Orchard Assessment Using Ground-Based Methods and Sentinel 2 Data P. Rouault et al. https://doi.org/10.3390/rs16183393
2. Comparative analysis of aerial and terrestrial imaging techniques for assessing flowering intensity of diverse Malus cultivars N. Guilhot et al. https://doi.org/10.17660/ActaHortic.2025.1439.22
3. Estimating litchi flower number using a multicolumn convolutional neural network based on a density map J. Lin et al. https://doi.org/10.1007/s11119-022-09882-7
4. Yield sensing technologies for perennial and annual horticultural crops: a review L. Longchamps et al. https://doi.org/10.1007/s11119-022-09906-2
5. Feasibility assessment of tree-level flower intensity quantification from UAV RGB imagery: A triennial study in an apple orchard C. Zhang et al. https://doi.org/10.1016/j.isprsjprs.2023.02.003
6. Pear Flower Cluster Quantification Using RGB Drone Imagery Y. Vanbrabant et al. https://doi.org/10.3390/agronomy10030407
7. Automatic Estimation of Apple Orchard Blooming Levels Using the Improved YOLOv5 Z. Chen et al. https://doi.org/10.3390/agronomy12102483
8. UAV-g 2019: Unmanned Aerial Vehicles in Geomatics F. Nex https://doi.org/10.3390/drones3030074