SEGMENTATION ON RIPE FUJI APPLE WITH FUZZY 2D ENTROPY BASED ON 2D HISTOGRAM AND GA OPTIMIZATION

In this work we developed a novel approach for the automatic recognition of red Fuji apples in natural scenes using L*a*b* color model and fuzzy two dimensional (2D) entropy based on 2D histogram in order to achieve intelligent harvesting. The L*a*b* model is applied to detect the fruit under different lighting conditions because the red Fuji apple has the highest red color among the objects in the image. The fuzzy 2D entropy, which could discriminate the object and the background in grayscale images, is obtained from the 2D histogram. The genetic algorithm (GA), compared to the heuristic searching method, is optimized to increase the precision of segmentation of Fuji apples under complex backgrounds with partially occluded branches and reflective lights. A series of morphological operations are applied to eliminate segmental fragments. Finally, the proposed approach is validated on apple images taken in natural orchards. The contributions reported in this work, is the whole effective approach which recognizes and segments apples under different natural scenes regardless of the recognition accuracy.