张智韬,边江,韩文霆,付秋萍,陈硕博,崔婷.剔除土壤背景的棉花水分胁迫无人机热红外遥感诊断[J].农业机械学报,2018,49(10):250-260.
ZHANG Zhitao,BIAN Jiang,HAN Wenting,FU Qiuping,CHEN Shuobo,CUI Ting.Diagnosis of Cotton Water Stress Using Unmanned Aerial Vehicle Thermal Infrared Remote Sensing after Removing Soil Background[J].Transactions of the Chinese Society for Agricultural Machinery,2018,49(10):250-260.
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剔除土壤背景的棉花水分胁迫无人机热红外遥感诊断   [下载全文]
Diagnosis of Cotton Water Stress Using Unmanned Aerial Vehicle Thermal Infrared Remote Sensing after Removing Soil Background   [Download Pdf][in English]
投稿时间:2018-04-09  
DOI:10.6041/j.issn.1000-1298.2018.10.028
中文关键词:  棉花  无人机  热红外  Canny边缘检测  水分胁迫指数  气孔导度
基金项目:新疆科技支疆项目(2016E02105)、国家重点研发计划项目(2017YFC0403203)和陕西省水利科技项目(2017SLKJ-7)
作者单位
张智韬 西北农林科技大学 
边江 西北农林科技大学 
韩文霆 西北农林科技大学 
付秋萍 新疆农业大学 
陈硕博 西北农林科技大学 
崔婷 西北农林科技大学 
中文摘要:剔除无人机热红外影像中的土壤背景是提高作物水分诊断精度的有效途径,但也是热红外图像处理的难点问题。本文以不同水分处理的花铃期棉花为研究对象,分别在09:00、13:00和17:00等3个时刻,连续5d采集无人机高分辨率热红外影像,并采用二值化Ostu算法和Canny边缘检测算法对热红外图像进行掩膜处理,实现对土壤背景的剔除,然后分别计算二值化Ostu算法、Canny边缘检测算法和包含土壤背景下的3种棉花水分胁迫指数(Crop water stress index,CWSI),最后建立不同时刻下3种CWSI与棉花叶片气孔导度Gs的关系模型。研究结果表明,应用Canny边缘检测算法可有效剔除热红外影像中的土壤背景,剔除土壤背景后的温度直方图呈单峰的偏态分布;3种处理方法获得的作物水分胁迫指数CWSI中,Canny边缘检测算法的CWSI最小,二值化Ostu算法的CWSI较高,包含土壤背景的CWSI最大;采用Canny边缘检测算法剔除土壤背景后的CWSI与棉花叶片气孔导度Gs的决定系数R2达到0.84,Ostu算法的结果次之,包含土壤背景的最差。本研究可为无人机热红外遥感监测作物水分状况提供参考。
ZHANG Zhitao  BIAN Jiang  HAN Wenting  FU Qiuping  CHEN Shuobo  CUI Ting
Northwest A&F University,Northwest A&F University,Northwest A&F University,Xinjiang Agricultural University,Northwest A&F University and Northwest A&F University
Key Words:cotton  unmanned aerial vehicle  thermal infrared  Canny edge detection  crop water stress index  stomatal conductance
Abstract:With the rapid development of remote sensing platform of low altitude unmanned aerial vehicle (UAV), the dynamic, fast and inexpensive features, the UAV remote sensing platform has more research in various fields, especially in the precision agriculture irrigation technology. The unmanned aerial vehicle thermal infrared low altitude remote sensing technology can quickly monitor the canopy temperature information of the crop, which can further use the canopy temperature information to diagnose the water stress condition of the crop. However, the processing of high resolution thermal infrared image of UAV is the key to the diagnosis of crop moisture, eliminating the soil background of UAV thermal infrared image is an effective way to improve the accuracy of crop water diagnosis. However, it is also a difficult problem in thermal infrared image processing. Different water treatments were carried out, including I1 (50% of field holding water), I2 (65% of field holding water), I3 (80% of field holding water) and I4 (control group 95%~100% of field holding water), and each water treatment set three repeat tests, a total of 12 plots, each plot was 4m×5m). Flower boll cotton was taken as study object at 09:00, 13:00 and 17:00 of day, respectively, and UAV high resolution thermal infrared images were obtained. Firstly, the two-valued Ostu algorithm and the Canny edge detection algorithm were used to deal with the thermal infrared image, and achieve the elimination of soil background, then, the two-valued Ostu algorithm and Canny edge detection algorithm contained soil background were used to calculate the crop water stress index, finally, the relationship models between three kinds of crop water stress index (CWSI) and cotton leaf stomatal conductance at different times was established. The researchresults showed that the application of Canny edge detection algorithm can effectively eliminate the soil background in thermal infrared image, and there was a single peak distribution of the temperature histogram after removing the soil background. Among the crop water stress index obtained from three kinds of treatment methods, the CWSI of Canny edge detection algorithm was the minimal, the CWSI of two-valued Ostu algorithm was higher, and the CWSI with soil background was the largest. The determination coefficient between CWSI and cotton leaf stomatal conductance by using Canny edge detection algorithm to remove the soil background was up to 0.84, and that by using two-valued Ostu algorithm resulted the second, and that got by containing soil background was the worst. The research result can provide a reference method for monitoring water condition of the plant by UAV thermal infrared technology.

Transactions of the Chinese Society for Agriculture Machinery (CSAM), in charged of China Association for Science and Technology (CAST), sponsored by CSAM and Chinese Academy of Agricultural Mechanization Science(CAAMS), started publication in 1957. It is the earliest interdisciplinary journal in Chinese which combines agricultural and engineering. It always closely grasps the development direction of agriculture engineering disciplines and the published papers represent the highest academic level of agriculture engineering in China. Currently, nearly 8,000 papers have been already published. There are around 3,000 papers contributed to the journal each year, but only around 600 of them will be accepted. Transactions of CSAM focuses on a wide range of agricultural machinery, irrigation, electronics, robotics, agro-products engineering, biological energy, agricultural structures and environment and more. Subjects in Transactions of the CSAM have been embodied by many internationally well-known index systems, such as: EI Compendex, CA, CSA, etc.

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