Film identification method based on improved deeplabv3+ for full-film double-ditch corn seedbed  

作　　者：Fei Dai Xiangzhou Li Ruijie Shi Fengwei Zhang Wuyun Zhao Wenjuan Guo 

机构地区：[1]College of Mechanical and Electrical Engineering,Gansu Agricultural University,Lanzhou 730070,China [2]School of Cyber Security,Gansu University of Political Science and Law,Lanzhou 730070,China

出　　处：《International Journal of Agricultural and Biological Engineering》2023年第5期165-172,共8页国际农业与生物工程学报（英文）

基　　金：supported by the National Natural Science Foundation of China(Grant No.52065005;52365029);Outstanding Youth Foundation of Gansu Province(Grant No.20JR10RA560),China Postdoctoral Science Foundation(Grant No.2021M700741).

摘　　要：This study aimed to investigate the task demand of intelligent unmanned fertilizer application in seedling stage of corn planted in full-film double-ditch seedbed,a film identification method based on improved DeepLabv3+ identification method for full-film double-ditch corn seedbed was proposed.The differences in performance indicators of the original Deeplabv3+ network taking Xception as the backbone network and the network model that replaced three lightweight backbone networks,MobileNetV2,MobileNetV3 and GhostNet were tested.At the same time,the network models,classical semantic segmentation was introduced to PSPNet and UNet for comparative test.The MIoU of DeepLabv3+ network model that replaced its backbone network increased by 5.01%,and FPS improved by 206%compared with original network,and the model size reduced by 90.3%.The three DeepLabv3+ models after replacing the backbone network were further compressed,and the two-layer expansion convolution with low expansion rate in ASPP was deleted,and the common convolution after feature fusion was replaced by the depthwise separable convolution to obtain a lightweight network model.After testing the improved network model,it was found that the average decline of precision indicators was only 0.17%,FPS raised to 66.5,with an average increase of 25.5%,and the size of the model was compressed to 10.53 MB.Test results showed that,the improved model showed excellent performance,and could provide important technology and method support for the research and development of intelligent topdressing and field management on full-film double-ditch corn seedbed during seedling stage.

关 键 词：fertilizer application intelligent unmanned machine full-film double-ditch corn seedbed film identification method deep learning semantic segmentation DeepLabv3+ 

分 类 号：TP39[自动化与计算机技术—计算机应用技术] S513[自动化与计算机技术—计算机科学与技术]