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Copyright of China Sciencepaper is the property of China Sciencepaper and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of China Sciencepaper is the property of China Sciencepaper and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of China Sciencepaper is the property of China Sciencepaper and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of China Sciencepaper is the property of China Sciencepaper and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

To solve the problem that colors of green apple and background are too similar in natural scenes to identify, a method combining SSD deep learning algorithm and related image processing algorithms proposed. Firstly, the green apple in the image was detected by SSD algorithm, and the apple area was marked by a rectangular box with its regression position coordinates. Secondly, according to the coordinates of this position, only the pixels in the rectangular box were preserved, and all other non-apple areas were removed. Finally, k-means algorithm, R-B chromatic aberration method in RGB space and U component threshold value n YUV color space were used to identify the fruit area n the rectangular box respectively. And the optimal fruit recognition method was obtained by comparing the recognition effects of the three methods. Experiment results show that SSD algorithm combined with U component threshold segmentation has the highest recognition accuracy, with an average segmentation error of 8.04%, a false positive rate of 0.55%, a false negative rate of 9.05% and a stacking coefficient of 88.60%. [ABSTRACT FROM AUTHOR]