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

Objects in agricultural soils will seriously affect the farming operations of agricultural machinery.At present,it still relies on human experience to judge abnormal Gounrd-penetrting Radar(GPR)signals.It is difficult for traditional image processing technology to form a general positioning method for the randomness and diversity characteristics of GPR signals in soil.Although many scholars had researched a variety of image-processing techniques,most methods lack robustness.In 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 accuracy.First,a soil stratification experiment was set to classify the physical parameters of the soil and study the attenuation law of electromagnetic waves.Secondly,a SOIL-GPR geometric model was proposed,which can be combined with Mask-RCNN's MASK geometric size to predict object sizes.The 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 experiment.The 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 cm.The 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.

Point Cloud Completion of Plant Leaves under Occlusion Conditions Based on Deep Learning

The utilization of 3-dimensional point cloud technology for non-invasive measurement of plant phenotypic parameters can furnish important data for plant breeding,agricultural production,and diverse research applications.Nevertheless,the utilization of depth sensors and other tools for capturing plant point clouds often results in missing and incomplete data due to the limitations of 2.5D imaging features and leaf occlusion.This drawback obstructed the accurate extraction of phenotypic parameters.Hence,this study presented a solution for incomplete flowering Chinese Cabbage point clouds using Point Fractal Network-based techniques.The study performed experiments on flowering Chinese Cabbage by constructing a point cloud dataset of their leaves and training the network.The findings demonstrated that our network is stable and robust,as it can effectively complete diverse leaf point cloud morphologies,missing ratios,and multi-missing scenarios.A novel framework is presented for 3D plant reconstruction using a single-view RGB-D(Red,Green,Blue and Depth)image.This method leveraged deep learning to complete localized incomplete leaf point clouds acquired by RGB-D cameras under occlusion conditions.Additionally,the extracted leaf area parameters,based on triangular mesh,were compared with the measured values.The outcomes revealed that prior to the point cloud completion,the R^(2)value of the flowering Chinese Cabbage's estimated leaf area(in comparison to the standard reference value)was 0.9162.The root mean square error(RMSE)was 15.88 cm^(2),and the average relative error was 22.11%.However,post-completion,the estimated value of leaf area witnessed a significant improvement,with an R^(2)of 0.9637,an RMSE of 6.79 cm^(2),and average relative error of 8.82%.The accuracy of estimating the phenotypic parameters has been enhanced significantly,enabling efficient retrieval of such parameters.This development offers a fresh perspective for non-destructive identification of plant phenotypes.

大国博弈背景下的美对华移民政策变化之探析

2020年以来,在“美国优先”理念的本土主义内核、大国竞争的外交逻辑和新冠肺炎疫情引发的跨境移民社会治理模式转变三重因素作用下,美国通过对中国的“全政府-全社会”的弹压将其对华移民政策进一步推入复杂深水区。从内因看,美国社会的移民承载力与包容度持续下行;从中美互动的情况看,双边关系尚未理顺,拜登政府的对华战略继续紧扣自由、规则与竞争。故而身处两国关键竞争领域的中国移民很难快速脱离“瓶颈期”。现有研究通常将美国移民政策及其调整的考察限定在美国社会层面,较少置于中美博弈的大框架下讨论,有鉴于此,本文聚焦美对华移民政策调整的国内-国际双重维度,首先以探讨美国将移民新政打造为撬动中美关系的杠杆的理论为起点,在此基础上概述当前美国对华移民政策的波动及转向,进而结合中美大局变迁和美国内移民承载力收缩的现实,阐释美对华移民政策波动的基本动因以及美国未来借移民政策改革扰动双边关系的可能性。