Intelligent identification method for watermelon seedlings based on improved FPN model
In order to improve the accuracy and efficiency of intelligent identification of watermelon seedlings in different periods,an intelligent recognition method with improved Feature Pyramid Network(FPN)is proposed by using deep learning technology.Firstly,a network model was designed by combining the feature pyramid network model and the Res2Net model,and the Efficient Channel Attention(ECA)mechanism was used to assign different weights to spatial features,and the channel parameter sharing was adopted to reduce the computational complexity of the model.Then,the residual structure was used to optimize and improve the model,which solved the problem of network degradation that occurred when the network depth continued to increase without increasing training parameters.Finally,the deep separable convolutions were used in the fully connected layer to replace traditional convolutions,significantly reducing computational complexity and achieving a lightweight design.The experimental results on watermelon seedling leaves at different growth stages show that the proposed recognition method has higher recognition accuracy and the shortest computational time,compared with several more advanced recognition algorithms,the recognition rate is up to 96.84％,an equal error rate is only 0.54％and the mAP is up to 91.68％,and the computational time is as low as 112 ms,providing technical support for promoting the development of intelligent agriculture and achieving intelligent agricultural management decision-making.
万方数据
维普
