Objective To address the challenge of apple target recognition in different mature stages in a complex agricultural environment, an apple recognition algorithm based on image fusion was studied.

Method A mean-shift filter with good edge-preserving performance was used to preprocess the image and filter out small parts of background noise. The R−G component and the I component feature images were extracted from RGB color space and YIQ color space, respectively. The pixel-level image fusion algorithm was used to fuse the information of two feature images to highlight the fruit target area. The Otsu adaptive threshold algorithm was used to obtain the best threshold to segment the target apple from the background. In order to identify the apple target, an improved Hough transform circle detection algorithm based on gradient fields was proposed. The morphological reconstruction algorithm was introduced to clean up the small area remaining in the background so as to improve the detection efficiency. At the same time, the algorithm to eliminate false circles was constructed using the segmented binary image of apple as the judgment standard, so as to avoid false targets.

Result Fifty images of immature apples and fifty images of fully matured apples were recognized and compared with the minimum circumscribed circle method. The experiment results showed that the average recognition time of the algorithm in this paper was 0.367 s, and the recognition accuracy for completely exposed fruits was 100%, 92.46% for fruits with occluded areas ≤ 1/2, 81.87% for fruits with occluded areas > 1/2. The overall recognition accuracy was increased by 11.43 percentage points compared with that of the minimum circumscribed circle algorithm. The mean center relative error and the mean radius relative error of the algorithm in this paper were 0.216 and 0.048% respectively, and the mean center relative error and the mean radius relative error of the minimum circumscribed circle algorithm were 0.508 and 0.370% respectively.

Conclusion The method proposed in this paper can quickly identify the apple target, locate the fruit with high accuracy and efficiency, and can be used for fruit picking by the apple picking robot.