Automated mango flowering assessment via refinement segmentation

An automated flowering assessment system for mango orchards was proposed. Segmentation of flowers from a complex background (i.e. leaves, branches and ground) was achieved based on (i) colour correction via adjustment of the brightness and contrast to a reference level, to rectify the illumination variability spatially within and between images; (ii) colour thresholding with fixed thresholds to separate flowers, although with some branches and trunks; and (iii) SVM classification to refine the segmentation results, removing the branch and trunk errors. Mango tree canopy images (n=160) were acquired during a five-week flowering period, with 15 of the images used in calibration and 145 used in validation. The proposed method had a good correlation with human scoring, with coefficient of determination (R 2 ) of 0.87.