A deep learning method for recognizing elevated mature strawberries

Strawberry picking by machines confronts a complex environment where a targe may be sheltered by leaves or overlap with each other. Also, it is a challenge for machines to recognize mature strawberries among those in different maturity. This work presents a fast recognition method for elevated mature strawberries by the approach of deep learning. It uses an Ostu algorithm to separate targets from background and then the resulted effective image areas designated by the minimum external rectangular marking method are used to train CaffeNet for automatic target recognition. For comparison, we also design a SVM classifer that uses HOG gradient direction feature and H component of the color feature of the mature strawberries. The experimental results show that the average recognition rate of mature strawberries by CaffeNet can reach 95%, higher than that by SVM by 11%.