Feasibility assessment of tree-level flower intensity quantification from UAV RGB imagery: A triennial study in an apple orchard

Chenglong Zhang*, João Valente, Wensheng Wang, Leifeng Guo, Aina Tubau Comas, Pieter van Dalfsen, Bert Rijk, Lammert Kooistra

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

4 Citations (Scopus)


A timely and accurate spatial inventory of flowering characteristics benefits both the floral phenology monitoring in ecology and various crop management activities in agricultural systems. Recent advancement has proven the superiority of computer vision in flower classification at image level. Yet progress in the flowering intensity estimation at tree level is much less and still far from satisfactory. To tackle this problem, a novel approach was designed for the use of single raw aerial images to quantify flower intensity. With pre-prepared dataset, flower-associated pixels were extracted for individual trees using a pixel-based classification method, the color thresholding. Next, three flowering indices retrieved from unmanned aerial vehicle (UAV) were evaluated, the index percentage (IPG), index pixel (IP), and index area (IA). Finally, linear correlation of the flowering indices to flower cluster number and expert-assessed floridity recorded in the field were calculated. Results indicated that IPG yielded the highest correlation to flower cluster (R2 = 0.93, RMSE = 8) and floridity estimation (R2 = 0.78, RMSE = 0.9). A UAV-based floridity scoring method was also designed for automatic estimation tasks in practice, and a comparable and even better performance to the expert-based approach was demonstrated. Furthermore, effects of vertical (nadir) and horizontal (angular) overlapping of flower clusters within the canopy were evaluated, showing excellent potential to improve the estimation accuracy.

Original languageEnglish
Pages (from-to)256-273
Number of pages18
JournalISPRS Journal of Photogrammetry and Remote Sensing
Publication statusPublished - Mar 2023


  • Computer vision
  • Floridity
  • Flower cluster
  • Flower occlusion
  • UAV images


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    Pereira Valente, J. & Kooistra, L.


    Project: PostDoc

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