摘要
【目的】玉米雄穗在玉米的生长过程和最终产量中起关键作用,使用无人机采集玉米抽穗期的RGB图像,研究不同的目标检测算法,构建适用于无人机智能检测玉米雄穗的模型,自动计算图像中雄穗的个数。【方法】使用无人飞行器(UAV)在25 m飞行高度下获得大量玉米抽穗时期的RGB图像,裁剪并标注出图像中玉米雄穗的位置和大小,训练数据和测试数据按照3:1的比例划分数据集;在深度学习框架MXNet下,利用这些数据集,分别训练基于ResNet50的Faster R-CNN、基于ResNet50的SSD、基于mobilenet的SSD和YOLOv3等4种模型,对比4种模型的准确率、检测速度和模型大小。【结果】使用无人机采集了236张图像,裁剪成1024×1024大小的图片,去除成像质量差的图像,利用标注软件labelme获得100张标注的玉米雄穗数据集;最终得到4个模型的mAP值分别为0.73、0.49、0.58和0.72。在测试数据集上进行测试,Faster R-CNN模型的准确率最高为93.79%,YOLOv3的准确率最低,仅有20.04%,基于ResNet50的SSD和基于mobilenet的SSD分别为89.9%和89.6%。在识别的速度上,SSD_mobilenet最快(8.9 samples·s^−1),Faster R-CNN最慢(2.6 samples·s^−1),YOLOv3检测速度为3.47 samples·s^−1,SDD_ResNet50检测速度为7.4 samples·s^−1。在模型大小上,YOLO v3的模型最大,为241 Mb,SSD_mobilenet的模型最小,为55.519 Mb。【结论】由于无人机的机载平台计算资源稀缺,综合模型的速度、准确率和模型大小考虑,SSD_mobilenet最适于部署在无人机机载系统上用于玉米雄穗的检测。
【Objective】Deep-learning and computation were applied to analyze the images collected by drones on the status of tassel on corn plants in the field for estimating crop growth and forecasting production.【Method】Drones,or unmanned aerial vehicles(UAV),flying at a height of 25 m above corn crop in the field were used to generate RGB images showing the position and size of tassel on the plants at heading stage.Under the deep-learning framework of MXNet,the data sets on a 3-to-1 training-to-testing ratio were fed into 4 models of the ResNet50-based Faster R-CNN,the ResNet50-based SSD,the mobilenet-based SSD,and YOLOv3.The algorithms provided by the models were tested to intelligently extract information from the images for an accurate and rapid report on the status of corn tassel.【Results】mThe 236 UAV-collected images were cropped individually into 1024×1024 size.Those of poor quality were discarded to result in 100 labeled datasets using the Labelme software.The mAPs of the 4 models were 0.73,0.49,0.58 and 0.72,respectively.The highest accuracy rate of 93.79%on the test was obtained from the Faster R-CNN model,followed by 89.9%from SSD_ResNet50,89.6%from SSD_mobilenet,and 20.04%from YOLOv3.On processing speed,SSD_mobilenet was the fastest at 8.9 samples·s^−1,followed by YOLOv3 at 3.47 samples·s^−1,SSD_ResNet50 at 7.4 samples·s^−1,and Faster R-CNN at 2.6 samples·s^−1.Among the 4 models,YOLOv3 was the largest,241 Mb in size,while SSD_mobilenet the smallest 55.519 Mb.【Conclusion】Considering the scarcity of available resources on the airborne UAV platform,as well as the detection accuracy,processing speed,and size of the programs,the SSD_mobilenet model was selected as the choice for the field surveying of corn tassel by UAV.
作者
梁胤豪
陈全
董彩霞
杨长才
LIANG Yin-hao;CHEN Quan;DONG Cai-xia;YANG Chang-cai(School of Mathematics,Northwest University,Xian,Shaanxi 710127,China;Institute of Big Data for Agriculture and Forestry,Fujian Agriculture and Forestry University,Fuzhou,Fujian 350002,China;Institute of Digital Research and Big Data/College of Computer and Information Science,Fujian Agriculture and Forestry University,Fuzhou,Fujian 350002,China;Key Laboratory of Smart Computing,Fujian Agriculture and Forestry University,Fuzhou,Fujian 350002,China)
出处
《福建农业学报》
CAS
CSCD
北大核心
2020年第4期456-464,共9页
Fujian Journal of Agricultural Sciences
基金
国家自然科学基金项目(61802064、61701117)
福建省自然科学基金项目(2019J01402)。
作者简介
梁胤豪(1998−),男,研究方向:计算机应用(E-mail:lyhmath@163.com);通信作者:杨长才(1981−),男,博士,副教授,研究方向:计算机视觉与作物表型识别(E-mail:changcaiyang@gmail.com)。
