Abstract
Recognizing tomatoes fruits based on color images faces two problems: tomato plants have a long fruit bearing period, the colors of fruits on the same plant are different; the growth of tomato plants generally has the problem of occlusion. In this article, we proposed a neural network classification technology to detect maturity (green, orange, red) and occlusion degree for automatic picking function. The depth images (geometric boundary information) information of the fruits were integrated to the original color images (visual boundary information) to facilitate the RGB and depth information fusion into an integrated set of compact features, named RD-SSD, the mAP performance of RD-SSD model in maturity and occlusion degree respectively reached 0.9147.
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