Combining an information-maximization-based attention mechanism and illumination invariance theory for the recognition of green apples in natural scenes

Accurate recognition of green fruit targets is one of the key technologies for fruit growth monitoring and yield estimation. To solve the problem of fruit misidentification due to the similarity between fruit skin and leaf colors, a progressive detection method of green apples in natural environments was proposed. Image enhancement based on fuzzy set theory was carried out to make the fruit targets more salient in the whole image. Then, the fruit areas were roughly determined by the attention-based information maximization (AIM) algorithm, and the recognized apple regions were cropped according to the adaptive pixel-extending method to remove the background information. After that, accurate segmentation of fruit targets was accomplished by fusing the illumination-invariant image andR-component of the cropped image. To evaluate the performance of this method, it was compared with the illumination invariance theory-based algorithm, mean shift algorithm, K-means clustering algorithm, manifold ranking algorithm and GrabCut algorithm. The test was conducted using 200 green apple images under different growth statuses. Experimental results showed that the segmentation rate of the proposed method was 86.91%, which was 3.26%, 6.35%, 16.43%, 3.08% and 4.7% higher than those of the other five methods, respectively. The false positive rate and false negative rate were 0.88% and 10.53%, which gained an advantage over those of the other five segmentation algorithms. The localization error was 3.65%. In conclusion, the proposed method can accurately segment green fruit targets, which can lay the foundation for intelligent management of fruits over the entire growing season.