Immature citrus fruit detection based on local binary pattern feature and hierarchical contour analysis

Detecting immature fruit in groves provides a promising benefit for growers to plan application of nutrients and estimate their yield and profit prior to harvesting. The goal of this study was to develop a robust algorithm to detect and count immature citrus fruit in images of the tree canopy. Images were all taken in low natural light conditions with a flashlight, and the green component of the colour images was used for further analysis. Local intensity maxima were detected and local binary pattern (LBP) features around them were extracted as an input of an ensemble classifier-RUSBoost. The positive predictions were considered as candidates and the hierarchical contour maps around them were extracted and fitted with Circular Hough Transform. The fitted circles were predicted as fruit targets if its radius were in a predetermined range. The algorithm was evaluated with a test set of 25 images, achieved 80.4% true positive rate and 82.3% precision rate, and F-measure was 81.3%. The good performance of occlusion tolerance of the proposed method was mainly coming from the robust LBP texture descriptor and hierarchical contour analysis (HCA) which used the pattern of light intensity distribution on fruit surface. This study proposed an innovative method to detect green fruit in images of trees only by using texture and intensity distribution.