基于无人机的水肥一体化玉米出苗率估算方法与试验
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摘要
出苗率是西北地区春播玉米夺得高产的前提保障。针对宁夏大面积玉米种植过程中人工统计出苗状况工程量大、耗时费力、误查漏数等现象。本文设计不同氮素处理试验,运用无人机搭载数码相机获取玉米苗期高清图像,运用MATLAB软件中ORB算法与距离加权融合算法合成无人机图像,通过二值化、腐蚀膨胀等深度优化处理技术得出玉米苗期图像轮廓,然后运用MATLAB软件八位连通域和ARCMAP 10.3计算方法自动规划路线并计算出玉米的出苗数量。结合田间人工调查数据,采用线性回归分析方法,建立了人工计数和无人机获取玉米出苗株数之间的线性关系模型。结果表明,线性回归关系模型的决定系数、均方根误差和标准均方根误差分别为0.895、4.359和2.436%。因此,基于低空无人机平台快速获取大田玉米出苗株数,是一种省时省力、高效精准的出苗率获取方法。可为后续玉米高产的准确评估提供技术支持,对于优化宁夏玉米滴灌水肥一体化精准种植技术具有积极意义。
The seedling emergence rate is the prerequisite and guarantee for high yield of spring-sown maize in Northwest China. In view of the phenomenon of heavy workload, time-consuming, labor-consuming and error leakage by manual statistics in the process of large-scale cultivation of maize in Ningxia, different nitrogen treatment experiments were designed, which used a drone equipped with a digital camera to obtain HD images of maize seedlings. The ORB algorithm in MATLAB software and distance-weighted fusion algorithm were used to synthesize unmanned aerial vehicle images. The image outlines of maize seedlings were obtained through depth optimization techniques such as binarization and corrosion expansion, and then automatically planned routes and calculated using MATLAB eight-bit connected domain and ARCMAP 10.3 calculation method. The number of maize emergence was routed and calculated. Based on the field artificial survey data, a linear regression analysis method was introduced to establish a linear relationship model between manual counting and the number of maize seedlings obtained by drones. The results showed that the determination coefficient of the linear regression relationship model, root mean square error and normalized root mean square error were 0.895, 4.359 and 2.436%, respectively. Therefore, based on the low-altitude drone platform, the rapid acquisition of the number of emergence of maize in the field was a time-saving, efficient and accurate method for obtaining seedling rate. It could provide technical support for the accurate assessment of the subsequent high yield of maize, and was of great significance for optimizing the precise planting technology of water and fertilizer integration of maize in Ningxia.
The seedling emergence rate is the prerequisite and guarantee for high yield of spring-sown maize in Northwest China. In view of the phenomenon of heavy workload, time-consuming, labor-consuming and error leakage by manual statistics in the process of large-scale cultivation of maize in Ningxia, different nitrogen treatment experiments were designed, which used a drone equipped with a digital camera to obtain HD images of maize seedlings. The ORB algorithm in MATLAB software and distance-weighted fusion algorithm were used to synthesize unmanned aerial vehicle images. The image outlines of maize seedlings were obtained through depth optimization techniques such as binarization and corrosion expansion, and then automatically planned routes and calculated using MATLAB eight-bit connected domain and ARCMAP 10.3 calculation method. The number of maize emergence was routed and calculated. Based on the field artificial survey data, a linear regression analysis method was introduced to establish a linear relationship model between manual counting and the number of maize seedlings obtained by drones. The results showed that the determination coefficient of the linear regression relationship model, root mean square error and normalized root mean square error were 0.895, 4.359 and 2.436%, respectively. Therefore, based on the low-altitude drone platform, the rapid acquisition of the number of emergence of maize in the field was a time-saving, efficient and accurate method for obtaining seedling rate. It could provide technical support for the accurate assessment of the subsequent high yield of maize, and was of great significance for optimizing the precise planting technology of water and fertilizer integration of maize in Ningxia.
引文
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[17] 李晓鹏,胡鹏程,徐照丽,等.基于四旋翼无人机快速获取大田植株图像的方法及其应用[J].中国农业大学学报,2017,22(12):131-137.LI X P,HU P C,XU Z L,et al.Method for rapidly acquiring images of field-grown crops using aquad-rotor UAV and its application[J].Journal of China Agricultural University,2017,22(12):131-137.(in Chinese with English abstract)
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[2] 刘玉涛.多功能种衣剂对旱地玉米萌发生长及产量的影响[J].玉米科学,2000,8(4):85-86.LIU Y T.Effects of multifunctional seed coating agents on germination,growth and yield of dryland maize[J].Maize Sciences,2000,8(4):85-86.(in Chinese)
[3] 刘萍,尚林海,张军福,等.玉米种子室内发芽率与田间出苗率的相关性研究[J].玉米科学,2004,12(增刊1):129-131.LIU P,SHANG L H,ZHANG J F,et al.Correlation between indoor germination rate and germination rate of maize seeds[J].Journal of Maize Sciences,2004,12(S1):129-131.(in Chinese)
[4] 贾洪雷,王刚,郭明卓,等.基于机器视觉的玉米植株数量获取方法与试验[J].农业工程学报,2015,31(3):215-220.JIA H L,WANG G,GUO M Z,et al.Methods and experiments of obtaining corn population based on machine vision[J].Transactions of the Chinese Society of Agricultural Engineering,2015,31(3):215-220.(in Chinese with English abstract)
[5] 胡炼,罗锡文,曾山,等.基于机器视觉的株间机械除草装置的作物识别与定位方法[J].农业工程学报,2013,29(10):12-18.HU L,LUO X W,ZENG S,et al.Plant recognition and localization for intra-row mechanical weeding device based on machine vision[J].Transactions of the Chinese Society of Agricultural Engineering,2013,29(10):12-18.(in Chinese with English abstract)
[6] 雷亚平,韩迎春,王国平,等.无人机低空数字图像诊断棉花苗情技术[J].中国棉花,2017,44(5):23-25,35.LEI Y P,HAN Y C,WANG G P,et al.The identification of cotton seedling emergence based the UAV digital image[J].China Cotton,2017,44(5):23-25,35.(in Chinese with English abstract)
[7] MACIEL J,COSTEIRA J.Robust point correspondence by concave minimization[J].Image and Vision Computing,2002,20(9):683-690.
[8] 朱启兵,冯朝丽,黄敏,等.基于图像熵信息的玉米种子纯度高光谱图像识别[J].农业工程学报,2012,28(23):271-276.ZHU Q B,FENG C L,HUANG M,et al.Maize seed classification based on image entropy using hyperspectral imaging technology[J].Transactions of the Chinese Society of Agricultural Engineering,2012,28(23):271-276.(in Chinese with English abstract)
[9] 周竹,黄懿,李小昱,等.基于机器视觉的马铃薯自动分级方法[J].农业工程学报,2012,28(7):178-183.ZHOU Z,HUANG Y,LI X Y,et al.Automatic detecting and grading method of potatoes based on machine vision[J].Transactions of the Chinese Society of Agricultural Engineering,2012,28(7):178-183.(in Chinese with English abstract)
[10] 李文勇,李明,陈梅香,等.基于机器视觉的作物多姿态害虫特征提取与分类方法[J].农业工程学报,2014,30(14):154-162.LI W Y,LI M,CHEN M X,et al.Feature extraction and classification method of multi-pose pests using machine vision[J].Transactions of the Chinese Society of Agricultural Engineering,2014,30(14):154-162.(in Chinese with English abstract)
[11] 汪沛,罗锡文,周志艳,等.基于微小型无人机的遥感信息获取关键技术综述[J].农业工程学报,2014,30(18):1-12.WANG P,LUO X W,ZHOU Z Y,et al.Key technology for remote sensing information acquisition based on micro UAV[J].Transactions of the Chinese Society of Agricultural Engineering,2014,30(18):1-12.(in Chinese with English abstract)
[12] 田振坤,傅莺莺,刘素红,等.基于无人机低空遥感的农作物快速分类方法[J].农业工程学报,2013,29(7):109-116.TIAN Z K,FU Y Y,LIU S H,et al.Rapid crops classification based on UAV low-altitude remote sensing[J].Transactions of the Chinese Society of Agricultural Engineering,2013,29(7):109-116.(in Chinese with English abstract)
[13] HUNT E R,CAVIGELLI M,DAUGHTRY C S T,et al.Evaluation of digital photography from model aircraft for remote sensing of crop biomass and nitrogen status[J].Precision Agriculture,2005,6(4):359-378.
[14] HUNT E R JR,HIVELY W D,FUJIKAWA S J,et al.Acquisition of NIR-green-blue digital photographs from unmanned aircraft for crop monitoring[J].Remote Sensing,2010,2(1):290-305.
[15] SUGIURA R,NOGUCHI N,ISHII K.Remote-sensing technology for vegetation monitoring using an unmanned helicopter[J].Biosystems Engineering,2005,90(4):369-379.
[16] 牛庆林,冯海宽,杨贵军,等.基于无人机数码影像的玉米育种材料株高和LAI监测[J].农业工程学报,2018,34(5):73-82.NIU Q L,FENG H K,YANG G J,et al.Monitoring plant height and leaf area index of maize breeding material based on UAV digital images[J].Transactions of the Chinese Society of Agricultural Engineering,2018,34(5):73-82.(in Chinese with English abstract)
[17] 李晓鹏,胡鹏程,徐照丽,等.基于四旋翼无人机快速获取大田植株图像的方法及其应用[J].中国农业大学学报,2017,22(12):131-137.LI X P,HU P C,XU Z L,et al.Method for rapidly acquiring images of field-grown crops using aquad-rotor UAV and its application[J].Journal of China Agricultural University,2017,22(12):131-137.(in Chinese with English abstract)
[18] 刘婷婷,张惊雷.基于ORB特征的无人机遥感图像拼接改进算法[J].计算机工程与应用,2018,54(2):193-197.LIU T T,ZHANG J L.Improved image stitching algorithm based on ORB features by UAV remote sensing[J].Computer Engineering and Applications,2018,54(2):193-197.(in Chinese)
[19] 刘威,赵文杰,李成,等.基于改进ORB特征匹配的运动小目标检测[J].光电工程,2015,42(10):13-20.LIU W,ZHAO W J,LI C,et al.Detecting small moving target based on the improved ORB feature matching[J].Opto-Electronic Engineering,2015,42(10):13-20.(in Chinese with English abstract)