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Identification of growing points of cotton main stem is the key to realize intelligent and precise machine topping and chemical topping. The identification of growing points by applying the traditional target detection model is subject to a series of shortcomings such as low accuracy, slow identification speed, large number of model parameters, huge storage cost and high calculation workload. On the basis of the advantages and disadvantages of YOLOv3, this paper proposed a modified YOLOv3 lightweight model for identifying the growing points of cotton main stem, which realized the multiplexing integration of low-level semantic features and high-level semantics features by adding dense connection modules and modifying the nonlinear transformation within the dense modules. This model significantly reduced the number of model parameters by utilizing deep separable convolution and improved the learning ability of multi-scale features by applying the hierarchical multi-scale method. Our model achieved an accuracy rate of 90.93% based on a self-prepared dataset, which is higher than the accuracy of the original YOLOv3 model by 1.64%, while the number of training parameters was significantly reduced by 48.90%. Compared with other target detection models under different illumination conditions and actual complex environments, the modified YOLOv3 model proposed in this paper showed better robustness, higher accuracy and higher speed in identifying the growing points of cotton main stem.

Identification of Growing Points of Cotton Main Stem Based on Convolutional Neural Network