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Detection of the foreign object positions in agricultural soils using Mask-RCNN

International Journal of Agricultural and Biological Engineering 2023年第1期16卷 220-231页

作者： Yuanhong Li Chaofeng Wang Congyue Wang Xiaoling Deng Zuoxi Zhao Shengde Chen Yubin Lan South China Agricultural University College of Electronic EngineeringGuangzhou 510642China National Center for International Collaboration Research on Precision Agricultural Aviation Pesticides SprayingTechnology(NPAAC)South China Agricultural UniversityGuangzhou 510642China Department of Biological and Agricultural Engineering Texas A&M UniversityCollege StationTX77843USA Guangdong Laboratory for Lingnan Modern Agriculture Guangzhou 510642China

Objects in agricultural soils will seriously affect the farming operations of agricultural *** present,it still relies on human experience to judge abnormal Gounrd-penetrting Radar(gpr)*** is difficult for traditional image processing technology to form a general positioning method for the randomness and diversity characteristics of gpr signals in *** many scholars had researched a variety of image-processing techniques,most methods lack *** this study,the deep learning algorithm Mask Region-based Convolutional Neural Network(Mask-RCNN)and a geometric model were combined to improve the gpr positioning ***,a soil stratification experiment was set to classify the physical parameters of the soil and study the attenuation law of electromagnetic ***,a SOIL-gpr geometric model was proposed,which can be combined with Mask-RCNN's MASK geometric size to predict object *** results proved the effectiveness and accuracy of the model for position detection and evaluation of objects in soils;then,the improved Mask RCNN method was used to compare the feature extraction accuracy of U-Net and Fully Convolutional Networks(FCN);Finally,the operating speed of agricultural machinery was simulated and designed the A-B survey line *** detection accuracy was evaluated by several indicators,such as the survey line direction,soil depth false alarm rate,Mean Average Precision(mAP),and Intersection over Union(IoU).The results showed that pixel-level segmentation and positioning based on Mask RCNN can improve the accuracy of the position detection of objects in agricultural soil effectively,and the average error of depth prediction is 2.87 *** results showed that the detection technology proposed in this study integrates the advantage of soil environmental parameters,geometric models,and artificial intelligence algorithms to provide a high-precision and technical solution for the gpr non-destructive detection of soils

关键词： foreign object soil object position agricultural soil Mask R-CNN gpr image

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