基于无人机热红外遥感图像提取滴灌棉花冠层温度及精度评价

doi:10.6048/j.issn.1001-4330.2024.03.005

新疆农业科学 ›› 2024, Vol. 61 ›› Issue (3): 565-575.DOI: 10.6048/j.issn.1001-4330.2024.03.005

• 作物遗传育种·种质资源·分子遗传学·生理生化 • 上一篇下一篇

基于无人机热红外遥感图像提取滴灌棉花冠层温度及精度评价

党旭伟¹(), 林馨园¹, 贺正¹, 陈燕¹, 慈宝霞¹, 马学花¹, 郭晨荔², 贺亚星¹, 刘扬¹^,³(), 马富裕¹^,³()

1.石河子大学农学院/新疆兵团绿洲生态农业重点实验室,新疆石河子 832003
2.南京农业大学农学院,南京 210095
3.现代农业生产信息化管理与应用技术国家地方联合工程研究中心(新疆兵团),新疆石河子 832003

收稿日期:2023-07-15 出版日期:2024-03-20 发布日期:2024-04-19
通信作者: 刘扬(1989-),女,青海民和人,副教授,博士,硕士生导师,研究方向为作物水肥高效利用及精准栽培,(E-mail)ly.0318@163.com;马富裕(1967-),男,甘肃环县人,教授,博士,硕士生/博士生导师,研究方向为作物精准栽培与水肥一体化,(E-mail)1469633844@qq.com
作者简介:党旭伟(1997-),男,新疆石河子人,硕士研究生,研究方向为作物水分高效利用与精准栽培,(E-mail)dxw202009@163.com
基金资助:
新疆生产建设兵团财政科技计划项目“新疆高品质棉花生产关键栽培技术集成与示范”(2020AB017);“主要作物精准水肥一体化技术和装备研发及应用示范”(2020AB018)

Extraction and accuracy evaluation of cotton canopy temperature under drip irrigation based on uav thermal infrared remote sensing

DANG Xuwei¹(), LIN Xinyuan¹, HE Zheng¹, CHEN Yan¹, CI Baoxia¹, MA Xuehua¹, GUO Chenli², HE Yaxing¹, LIU Yang¹^,³(), MA Fuyu¹^,³()

1. The Key Laboratory of Oasis Eco-Agriculture,Xinjiang Production and Construction Corps/ College of Agriculture,Shihezi University,Shihezi Xinjiang 832003,China
2. College of Agronomy,Nanjing Agricultural University,Nanjing 210095,China
3. National & Local Joint Engineering Research Center of Information Management and Application Technology for Modern Agricultural Production(XPCC),Shihezi Xinjiang 832003,China

Received:2023-07-15 Online:2024-03-20 Published:2024-04-19
Correspondence author: LIU Yang(1989-),male,from Qinghai,associate professor,doctoral student,research field:efficient use of water and fertilizer and precision cultivation of crops,(E-mail)ly.0318@163.com;MA Fuyu(1967-),male,from Gansu,professor,doctoral student,research field:crop precision cultivation and water and fertilizer integration,(E-mail)1469633844@qq.com
Supported by:
Xinjiang Production and Construction Corps Financial Science and Technotogy Plan Project“Key Cultivation Technology Integration and Demonstration for Xinjiang High-Quality Cotton Production”(2020AB017);“Development and Application Demonstration of Main Crop Precision Water and Fertilizer Integration Technology and Equipment Research”(2020AB018)

摘要/Abstract

摘要：

【目的】 提高基于热红外遥感图像滴灌棉花冠层温度提取精度,为棉花水分状况精准监测提供技术支撑。【方法】 以不同水分处理的苗期、蕾期棉花为研究对象,利用无人机获取试验小区热红外遥感图像,使用便携式手持测温仪测量田间辐射校正板及水桶中的水温,对热红外影像进行温度校正。采用Otsu算法、Canny边缘检测算法对热红外遥感图像进行掩膜处理剔除土壤背景,通过波段运算提取棉花冠层温度,绘制棉花冠层温度频率直方图并对其进行优化。利用便携式手持测温仪同步测量棉花冠层温度,与提取的冠层温度进行一致性分析,验证热红外遥感图像提取棉花冠层温度的精度。【结果】 Canny边缘检测算法剔除土壤背景提取冠层图像准确率大于Otsu算法(91.90%>82.52%、92.76%>80.60%),剔除土壤背景效果最优。Otsu算法和Canny边缘检测算法剔除土壤背景后构建的冠层温度直方图均呈偏态分布,但Canny边缘检测算法剔除土壤背景后构建的冠层温度直方图形状比Otsu算法光滑,噪声少,并且Canny边缘检测算法2年冠层平均温度最低(29.95、30.54℃),与实测温度差值最小(2.78、3.43℃)。去除Canny边缘检测算法的温度直方图两端1%温度信息后,提取的冠层温度与实测温度相关性最高(2年试验r由0.88、0.93提高到了0.94、0.95),RMSE最低(2年RMSE由2.78、2.87℃下降到1.59、1.43℃)。【结论】 Canny边缘检测算法提高了无人机热红外遥感图像棉花冠层温度提取精度,且温度直方图两端1%温度优化后有助于提高棉花冠层温度提取精度。

关键词: 滴灌棉花; 无人机; 热红外遥感图像; 冠层温度; 精度

Abstract:

【Objective】 To increase the accuracy of canopy temperature extraction derived from thermal infrared imagery of drip irrigated cotton in Xinjiangin the hope of providing a technical support for precise water status monitoring.【Methods】 Different soil moisture contents were set at cotton seedling and squaring stages.The thermal infrared images of different treatments were acquired by using UAV,and the temperature of radiation calibration plate in plot and water in the bucket were measured by using a portable handheld thermometer.For the above information,the latter temperature was used to calibrate the former temperature extracted from thermal imagery.The Otsu and Canny edge detection algorithms were used to mask thermal infrared images and remove soil background.Cotton canopy temperature was extracted by region of interest(ROI) and band math,and then the canopy temperature frequency histograms were plotted and optimized.Meanwhile,the actual cotton canopy temperature was obtained from a portable handheld thermometer.The consistency analysis was performed between actual canopy temperature and extracted canopy temperature to calibrate the accuracy of extracted temperature from thermal imagery.【Results】 Canny edge detection algorithm eliminated soil background and extracted canopy image with higher accuracy than Otsu algorithm(91.90%>82.52%、92.76%>80.60%),which reached the best effect.The canopy temperature histograms constructed by Otsu algorithm and Canny edge detection algorithm after removing soil background are skewed,but the canopy temperature histograms constructed by Canny edge detection algorithm after removing soil background were smoother and less noisy than Otsu algorithm,and the average canopy temperature of Canny edge detection algorithm in two years was the lowest(29.95,30.54℃),with the smallest difference from the measured temperature(2.78,3.43℃).Correlation analysis showed that the extracted canopy temperature based on Canny edge detection algorithm had the highest correlation with the measured temperature(r=0.94,0.95) and the lowest RMSE(1.59,1.43℃),where the 1% temperature information at both ends of the temperature histogram of Canny edge detection algorithm was dislogded.【Conclusion】 The Canny edge detection algorithm improves the precision of cotton canopy temperature extraction from UAV thermal infrared images,and the optimization of 1% temperature at both ends of the temperature histogram is helpful to improve the precision of cotton canopy temperature extraction.

Key words: drip-irrigated cotton; unmanned aerial vehicle; thermal infrared image; canopy temperature; accuracy

中图分类号:

S562
S123

党旭伟, 林馨园, 贺正, 陈燕, 慈宝霞, 马学花, 郭晨荔, 贺亚星, 刘扬, 马富裕. 基于无人机热红外遥感图像提取滴灌棉花冠层温度及精度评价[J]. 新疆农业科学, 2024, 61(3): 565-575.

DANG Xuwei, LIN Xinyuan, HE Zheng, CHEN Yan, CI Baoxia, MA Xuehua, GUO Chenli, HE Yaxing, LIU Yang, MA Fuyu. Extraction and accuracy evaluation of cotton canopy temperature under drip irrigation based on uav thermal infrared remote sensing[J]. Xinjiang Agricultural Sciences, 2024, 61(3): 565-575.

图/表 13

图1 试验点分布

Fig.1 Experimental site and experimental design

图2 试验期间气象条件

Fig.2 Meteorological conditions during the experiment period

图3 温度转换与校准

Fig.3 Temperature conversion and calibration

图4 不同剔除土壤背景方法比较(2021)

Fig.4 Comparison of different methods for removing soil background(2021)

图5 不同剔除土壤背景方法比较(2022)

Fig.5 Comparison of different methods for removing soil background(2022)

表1 不同提取方法的精度评价(2021)

Tab.1 Accuracy evaluation of different extraction methods(2021)(%)

评价指标 Evaluating indicator	算法 Algorithm	I1	I2	I3	I4	均值 Average
准确率 Precision	Otsu算法	80.88	79.29	77.41	78.50	81.52
准确率 Precision	Canny算法	93.48	91.60	90.02	92.50	91.90
召回率 Recall	Otsu算法	90.08	93.54	95.58	95.55	93.68
召回率 Recall	Canny算法	94.99	96.00	97.41	92.47	95.22

表2 不同提取方法的精度评价(2022)

Tab.1 Accuracy evaluation of different extraction methods(2022)(%)

评价指标 Evaluating indicator	算法 Algorithm	I1	I2	I3	I4	均值 Average
准确率 Precision	Otsu算法	78.52	80.89	81.32	81.67	80.60
准确率 Precision	Canny算法	93.14	96.11	90.21	91.59	92.76
召回率 Recall	Otsu算法	90.95	91.40	89.35	89.96	90.42
召回率 Recall	Canny算法	93.52	96.78	90.31	91.32	92.98

图6 棉花冠层和土壤温度频率(2021)

Fig.6 Histogram of cotton canopy and soil temperature frequencies(2021)

图7 棉花冠层和土壤温度频率(2022)

Fig.7 Histogram of cotton canopy and soil temperature frequencies(2022)

表3 冠层温度特征值(2021)

Tab.3 Characteristic values of canopy temperature(2021)

冠层温度 Canopy temperature(℃)	原始图像 Original image	Otsu算法 Otsu algorithm	Canny算法 Canny algorithm
最大值Max	65.38	62.40	59.65
最小值Min	11.34	11.34	11.34
平均值Mean	37.33	32.37	29.95
预测差值 Predicted difference	11.81	5.85	3.43

表4 冠层温度特征值(2022)

Tab.4 Characteristic values of canopy temperature(2022)

冠层温度 Canopy temperature(℃)	原始图像 Original image	Otsu算法 Otsu algorithm	Canny算法 Canny algorithm
最大值Max	68.8	64.69	63.54
最小值Min	11.34	11.34	11.34
平均值Mean	32.48	31.40	30.54
预测差值 Predicted difference	4.72	3.64	2.78

图8 图像提取温度与地面实测温度相关性(2021) 注:A:原始图像,B:Otsu算法,C:剔除Otsu算法直方图两端1%,D:Canny算法,E:剔除Canny算法两端1%,下同

Fig.8 Correlation analysis between image extraction temperature and ground measured temperature(2021) Note:A:original image,B:Otsu algorithm,C:removing 1% at both ends of Otsu algorithm histogram,D:Canny algorithm,E:removing 1% at both ends of Canny histogram,the same as below

图9 图像提取温度与地面实测温度相关性(2022)

Fig.9 Correlation analysis between image extraction temperature and ground measured temperature(2022)

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基于无人机热红外遥感图像提取滴灌棉花冠层温度及精度评价

Extraction and accuracy evaluation of cotton canopy temperature under drip irrigation based on uav thermal infrared remote sensing

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