Considering the difficulty of counting wheat sheaves in the field, this paper proposes an improved Yolov7 (YOU ONLY LOOKCE version 7) model for the automatic counting of wheat sheaves in the field. Based on Yolov7, the method adds a simple parameter-free attention module (SimAM) and full-dimensional dynamic convolution (ODConv), which can enhance the dimensional interactivity of the backbone network in extracting features. By introducing a centralised feature pyramid (CFP) into the neck structure, a comprehensive and differentiated feature representation can be effectively obtained. The improved Yolov7 model improves the applicability of automatic wheat counting and allows for better suppression of useless information in complex field environments. Several models were selected for comparative testing in the collected wheat head dataset, and the results showed that the improved Yolov7 achieved an average accuracy of 96.5%, outperforming other target detection models and allowing more accurate identification of wheat spike counts.
KEYWORDS: Data modeling, Image acquisition, Data acquisition, Control systems, Performance modeling, Data storage, Internet, Image processing, Computing systems, Motion controllers
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%.
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