Rapid identification of mature Xanthoceras sorbifolium bunge

Abstract

Xanthoceras sorbifolium bunge is a kind of edible oil tree in China, which has very high economic value, but the timely picking of mature fruits is a problem that has troubled farmers for a long time. To rapidly, automatically and accurately identify mature Xanthoceras sorbifolium bunge in the field, a mobile data acquisition and transmission system was firstly designed based on the architecture of the Internet of Things, which provides image acquisition and positioning tools for timely and accurate picking of Xanthoceras sorbifolium bunge. Secondly, a mature Xanthoceras sorbifolium bunge identification network model was constructed based on the lightweight efficient model YOLOv3 by using convolutional neural network (CNN) and flip residual network. The established optimal identification model was evaluated, the results of which indicate that the constructed optimal model can serve as a tool to identify the maturity of Xanthoceras sorbifolium bunge with the mAP of 97.04%.