The image recognition of urban greening tree species based on deep learning and CAMP-MKNet model |
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| Institution: | 1. State Key Laboratory of Subtropical Silviculture, Zhejiang A&F University, Lin’an 311300, China;2. Zhejiang Provincial Collaborative Innovation Center for Bamboo Resources and High-Efficiency Utilization, Zhejiang A&F University, Lin’an 311300, China;3. Key Laboratory of Carbon Cycling in Forest Ecosystems and Carbon Sequestration of Zhejiang Province, Zhejiang A&F University, Lin’an 311300, China;4. School of Environmental and Resources Science, Zhejiang A&F University, Lin’an 311300, China;1. Department of Biological Sciences (M/C 066), University of Illinois Chicago, 3346 SES, 845 W. Taylor Street, Chicago, IL 60607, USA;2. Forest Preserve District of DuPage County, 3S580 Naperville Rd, Wheaton, IL 60189, USA;1. Faculty of Forestry, University of British Columbia, 2424 Main Mall, Vancouver, BC, Canada V6T 1Z4;2. North Carolina State Extension and Union County Planning Department, 500 N. Main St., Monroe, NC 28112, United States;3. College of Natural Resources, University of Wisconsin – Stevens Point, 800 Reserve St., Stevens Point, WI 54481, United States;4. Department of Environmental Science and Studies Department, DePaul University, 1110 West Belden Ave, Chicago, IL 60614, United States;1. Department of Land Management, School of Public Affairs, Zhejiang University, Hangzhou, China;2. Department of Urban Development and Management, School of Public Affairs, Zhejiang University, Hangzhou, China;1. Department of Urban and Regional Planning, University of Illinois at Urbana-Champaign, Champaign, IL, USA;2. Department of Geography, University of Central Arkansas, Conway, AR, USA;3. Urban Forestry Division, District Department of Transportation, Washington DC, USA;1. School of Architecture and Design Southwest Jiaotong University, no 99 Campus, Mianshi, Xidiwan, Building 7, Unit 1, Room 3004, Pixian County, Xipu, Chengdu, Sichuan, China;2. School of Architecture and design, Southwest Jiaotong University, Chengdu, Sichuan, China;1. Department of Human Geography and Regional Development, University of Ostrava, Chittussiho 10, Ostrava 71000, Czechia;2. Department of Human Geography and Regional Development, University of Ostrava, Czechia;3. School of Architecture Planning and Environmental Policy, University College Dublin, Ireland |
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| Abstract: | The information of urban tree species resources is of vital significance to the planning and design of urban green spaces. Tree organs, such as the bark are used as the primary features of identifying tree species. However, traditional tree identification methods need to consume a lot of manpower and time costs. In addition, the application of machine image recognition technology to tree species recognition still has problems such as heavy data preprocessing workload, small number of tree species images, uneven distribution of categories, and low recognition accuracy. In order to promote the intelligent management of urban forestry and solve the above problems, it is necessary to establish an automatic image recognition model for urban greening tree species. We captured bark images of 21 urban afforestation tree species in their natural environment and constructed a dataset that was divided into a train set, validation set, and test set in the ratio of 7:1:2. Combining Channel Attention Module (CAM) with algorithms such as Spatial Pyramid Pooling (SPP) and Mixed Depthwise Dilated Convolutional Kernels. The core algorithm is Mixed Convolution Kernel (MK), and a CAMP-MKNet Convolutional Neural Network (CNN) is constructed as a bark image classification model for urban greening tree species. The overall accuracy of the generic models ranged from 41.06% to 82.03%, whereas the overall accuracy of the experimental CAMP-MKNet model was 84.25%, with lower prediction cost. Our study shows that the CAMP-MKNet CNN model with better prediction performance and computational cost and can provide crucial insights and technical support for developing automated urban tree species image recognition systems. |
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| Keywords: | Tree species recognition Convolutional neural network (CNN) Bark images Spatial Pyramid Pooling (SPP) Residual networks |
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