Yield mapping for tree crops by mechanical harvesting requires automatic detection and counting of fruits in tree canopy. However, partial occlusion, shape irregularity, varying illumination, multiple sizes and similarity with the background make fruit identification a very difficult task to achieve. Therefore, immature green citrus-fruit detection within a green canopy is a challenging task due to all the above-mentioned problems. A novel algorithmic technique was used to detect immature green citrus fruit in tree canopy under natural outdoor conditions. Shape analysis and texture classification were two integral parts of the algorithm. Shape analysis was conducted to detect as many fruits as possible. Texture classification by a support vector machine (SVM), Canny edge detection combined with a graph-based connected component algorithm and Hough line detection, were used to remove false positives. Next, keypoints were detected using a scale invariant feature transform (SIFT) algorithm and to further remove false positives. A majority voting scheme was implemented to make the algorithm more robust. The algorithm was able to accurately detect and count 80.4% of citrus fruit in a validation set of images acquired from a citrus grove under natural outdoor conditions. The algorithm could be further improved to provide growers early yield estimation so that growers can manage grove more efficiently on a site-specific basis to increase yield and profit. © 2013 IAgrE.
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