Bark species recognition using fast Fourier transform-enhanced deep network
Rapid and accurate tree species identification is crucial for analyzing forest ecosystems,providing guidance for biodiversity understanding,habitat health monitoring,and conservation efforts.However,manual identification is time-consuming,inefficient,and requires specialized expertise,resulting in significant human resource wastage.To address these challenges,researchers have explored deep learning techniques for automated tree species identification using bark images.Yet,existing methods struggle with similar tree bark images in practical field operations.Moreover,models for identifying a larger number of tree species are complex and not easily deployable on resource-constrained end devices commonly used in the field.This study proposed a novel approach to tree species identification using a dual-branch net-work based on fast Fourier transform-enhanced deep neural networks.The first main branch incorporated a gate-con-trolled channel attention module(GCT)within the ResNet34 backbone.This module ensured the utilization of light-weight models while maintaining stable recognition accuracy.By focusing on informative regions of the bark images,GCT enhanced the model's ability to discriminate between similar tree species effectively.The second branch of the pro-posed network employed the fast Fourier transform as an auxiliary supervision mechanism.It transformed the grayscale distribution of bark images into spectrogram representations,enabling the analysis of image textures.This auxiliary su-pervision helped the main network learn features that were specifically related to texture differences,enhancing the dis-crimination capability of the overall model.Through the field collection of bark image datasets and the network collection of public data sets,the experiments were carried out on three different bark image datasets,and good results were achieved.In a 23-class bark image dataset,the proposed method achieved an impressive accuracy of 91.01％,pre-cision of 89.65％,and recall rate of 88.60％.Moreover,this approach significantly reduced the parameter size of the model by approximately 33％compared to the original ResNet34 architecture.This reduction in parameter size made the proposed method more suitable for deployment on end devices with limited computational resources.Overall,the results demonstrated that the proposed method improved the efficiency of tree species identification while reducing human re-source wastage.Additionally,the reduction in parameter size facilitated the deployment of the model on various end de-vices,making it more accessible for real-world applications in forest management and ecological research.
bark tree species identificationimage classificationfast Fourier transformFourier spectrogramattention mechanism
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