基于热红外图像的小麦品种抗旱性鉴定与评价

doi:10.3864/j.issn.0578-1752.2022.13.005

Abstract

Abstract:

【Objective】The information related to the temperature parameters of different genotypes of wheat canopy were analyzed to explore the indicators and methods for rapid and efficient screening of drought-resistant varieties of winter wheat, so as to provide a reference basis for the screening of drought-resistant varieties of winter wheat.【Method】In this study, the canopy thermal infrared images of 10 wheat varieties with different drought resistance under drought stress were obtained and extracted canopy temperature characteristic parameters by using temperature frequency histogram and other analysis methods, and the quantitative relationship between temperature characteristic parameters and drought resistance index were clarified, and then the effectiveness of canopy temperature characteristic parameters on screening drought-resistant varieties of winter wheat were analyzed.【Result】The grading criteria based on the yield drought resistance index (DRI) classified the measured wheat varieties into four drought resistance categories. The stronger the drought resistance, the more stable the maximum photosynthetic efficiency (F_v/F_m), plant water content (PWC), stomatal conductance (G_s), transpiration rate (T_r), and seed yield. The characteristic parameters of canopy temperature based on thermal infrared images were extracted, indicating the stronger the drought resistance of wheat, the smaller variability in canopy temperature and the less dispersion in canopy temperature. The yield DRI showed highly significant positive correlation with the deviation between crop canopy temperature and ambient temperature (CTD) at the jointing, booting and flowering stages with the correlation coefficient r=0.79-0.84, while the standard deviation of canopy temperature (CTSD), coefficient of variation (CTCV), water stress index (CWSI) and relative canopy temperature difference (CRTD) were significantly negatively correlated (r=-0.56- -0.78). The regression model of yield DRI was built based on canopy temperature characteristic parameters of a single growth period, and the estimation accuracy was r²=0.73-0.87, with the highest accuracy of the prediction model at the jointing stage. And the prediction model of yield DRI was constructed based on the combination of relevant canopy temperature parameters CTD, CTCV, and CTSD CWSI of three growth periods, and the accuracy of prediction was significantly higher (r²=0.95) than that based on a single growth period.【Conclusion】 Thermal infrared images can be used for early identification and rapid evaluation of drought resistance of wheat varieties, which is of great significance for promoting efficient and water-saving crop production.

Key words: winter wheat, drought stress, thermal infrared image, drought resistance index, variety identification

MENG Yu,WEN PengFei,DING ZhiQiang,TIAN WenZhong,ZHANG XuePin,HE Li,DUAN JianZhao,LIU WanDai,FENG Wei. Identification and Evaluation of Drought Resistance of Wheat Varieties Based on Thermal Infrared Image[J].Scientia Agricultura Sinica, 2022, 55(13): 2538-2551.

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References 48

[1]	SOUSSANA J F, GRAUX A L, TUBIELLO F N. Improving the use of modelling for projections of climate change impacts on crops and pastures. Journal of Experimental Botany, 2010, 61(8): 2217-2228. doi: 10.1093/jxb/erq100
[2]	BLOEM M W, SEMBA R D, KRAEMER K. Castel gandolfo workshop: An introduction to the impact of climate change, the economic crisis, and the increase in the food prices on malnutrition. Journal of Nutrition, 2010, 140: 132S-135S.
[3]	康绍忠. 水安全与粮食安全. 中国生态农业学报, 2014, 22(8): 880-885.
	KANG S Z. Towards water and food security in China. Chinese Journal of Eco-Agriculture, 2014, 22(8): 880-885. (in Chinese)
[4]	GUO Z J, SHI Y, YU Z W, ZHANG Y L. Supplemental irrigation affected flag leaves senescence post-anthesis and grain yield of winter wheat in the Huang-Huai-Hai Plain of China. Field Crops Research, 2015, 180: 100-109. doi: 10.1016/j.fcr.2015.05.015
[5]	李瑞雪, 孙任洁, 汪泰初, 陈丹丹, 李荣芳, 李龙, 赵卫国. 植物抗旱性鉴定评价方法及抗旱机制研究进展. 生物技术通报, 2017, 33(7): 40-48. doi: 10.13560/j.cnki.biotech.bull.1985.2017-0023
	LI R X, SUN R J, WANG T C, CHEN D D, LI R F, LI L, ZHAO W G. Research progress on identification and evaluation methods, and mechanism of drought resistance in plants. Biotechnology Bulletin, 2017, 33(7): 44-48. (in Chinese) doi: 10.13560/j.cnki.biotech.bull.1985.2017-0023
[6]	李龙, 毛新国, 王景一, 昌小平, 柳玉平, 景蕊莲. 小麦种质资源抗旱性鉴定评价. 作物学报, 2018, 44(7): 988-999. doi: 10.3724/SP.J.1006.2018.00988
	LI L, MAO X G, WANG J Y, CHANG X P, LIU Y P, JING R L. Drought tolerance evaluation of wheat germplasm resources. Acta Agronomica Sinica, 2018, 44(7): 988-999. (in Chinese) doi: 10.3724/SP.J.1006.2018.00988
[7]	BlUM A. Drought resistance, water-use efficiency, and yield potential—Are they compatible, dissonant, or mutually exclusive?. Australian Journal of Agricultural Research, 2005, 56: 1159-1168. doi: 10.1071/AR05069
[8]	NOURI-GANBALANI A, NOURI-GANBALANI G, HASSANPANAH D. Effects of drought stress condition on the yield and yield components of advanced wheat genotypes in Ardabil, Iran. Journal of Food Agriculture and Environment, 2009, 7(3): 228-234.
[9]	ALMESELMANI M, DESHMUKH P S, SAIRAM R K, KUSHWAHA S R, SINGH T P. Protective role of antioxidant enzymes under high temperature stress. Plant Science, 2006, 171: 382-388. doi: 10.1016/j.plantsci.2006.04.009
[10]	余斌, 杨宏羽, 王丽, 刘玉汇, 白江平, 张峰, 王蒂, 张俊莲. 马铃薯冠气温差变化特性与耐旱性的关系. 作物学报, 2018, 44(7): 1086-1094. doi: 10.3724/SP.J.1006.2018.01086
	YU B, YANG H Y, WANG L, LIU Y H, BAI J P, ZHANG F, WANG D, ZHANG J L. Relationship between potato canopy-air temperature difference and drought tolerance. Acta Agronomica Sinica, 2018, 44(7): 1086-1094. (in Chinese) doi: 10.3724/SP.J.1006.2018.01086
[11]	LUAN Y J, XU J Z, LV Y P, LIU X Y, WANG H Y, LIU S M. Improving the performance in crop water deficit diagnosis with canopy temperature spatial distribution information measured by thermal imaging. Agricultural Water Management, 2021, 246(401): 106699. doi: 10.1016/j.agwat.2020.106699
[12]	赵刚, 樊廷录, 李尚中, 王勇, 王磊, 党翼, 唐小明, 张建军, 王国宇. 不同品种冬小麦冠层温度与抗旱性和水分利用效率的关系研究. 农业现代化研究, 2010, 31(3): 334-337.
	ZHAO G, FAN T L, LI S Z, WANG Y, WANG L, DANG Y, TANG X M, ZHANG J J, WANG G Y. Study of relationship of canopy temperature with drought resistance and water use efficiency on different genotype winter wheat. Research of Agricultural Modernization, 2010, 31(3): 334-337. (in Chinese)
[13]	FANG J J, MA W Y, ZHAO X Q, HE X, LI B, TONG Y P, LI Z S. Lower canopy temperature is associated with higher cytokinin concentration in the flag leaf of wheat. Crop Science, 2012, 52: 2743-2756. doi: 10.2135/cropsci2012.03.0163
[14]	黄山, 王伟, 毕永基, 曾罗华, 刘庆友, 谭雪明, 潘晓华. 不同早稻品种冠层温度的差异及其与产量的关系. 江西农业大学学报, 2014, 36(6): 1179-1184.
	HUANG S, WANG W, BI Y J, ZENG L H, LIU Q Y, TAN X M, PANG X H. Genetic differences in canopy temperature of different early-rice varieties and its relationship with yield. Acta Agriculturae Universitatis Jiangxiensis, 2014, 36(6): 1179-1184. (in Chinese)
[15]	SADLER E J, CAMP C R, EVANS D E, MILLAN J A. Corn canopy temperatures measured with a moving infrared thermometerarray. Transactions of the ASAE, 2002, 45(3): 581-591.
[16]	WANG M, HUANG T. An integrated electric energy management system to improve fuel economy. Lecture Notes in Electrical Engineering, 2013, 194: 115-122.
[17]	WATANABE K, AGARIE H, APARATANA K, MITSUOKA M, TAIRA E, UENO M, KAWAMITSU Y. Fundamental study on water stress detection in sugarcane using thermal image combined with photosynthesis measurement under a greenhouse condition. Sugar Tech, 2022: 1-9.
[18]	CHAERLE L, CAENEGHEM W V, MESSENS E, LAMBERS H, MONTAGU M V, STRAETEN D V D. Presymptomatic visualization of plant-virus interactions by thermography. Nature Biotechnology, 1999, 17(8): 813-816. doi: 10.1038/11765
[19]	PEARCE R S, FULLER M P. Freezing of barley studied by infrared video thermography. Plant physiology, 2001, 125(1): 227-240. doi: 10.1104/pp.125.1.227
[20]	O’SHAUGHNESSY S A, EVETT S R, COLAIZZI P D, HOWELL T A. Using radiation thermography and thermometry to evaluate crop water stress in soybean and cotton. Agricultural Water Management, 2011, 98(10): 1523-1535. doi: 10.1016/j.agwat.2011.05.005
[21]	MANGUS D L, SHARDA A, ZHANG N. Development and evaluation of thermal infrared imaging system for high spatial and temporal resolution crop water stress monitoring of corn within a greenhouse. Computers and Electronics in Agriculture, 2016, 121(4): 149-159. doi: 10.1016/j.compag.2015.12.007
[22]	张智韬, 边江, 韩文霆, 付秋萍, 陈硕博, 崔婷. 无人机热红外图像计算冠层温度特征数诊断棉花水分胁迫. 农业工程学报, 2018, 34(15): 77-84.
	ZHANG Z T, BIAN J, HAN W T, FU Q P, CHEN S B, CUI T. Cotton moisture stress diagnosis based on canopy temperature characteristics calculated from UAV thermal infrared image. Transactions of the Chinese Society of Agricultural Engineering, 2018, 34(15): 77-84. (in Chinese)
[23]	PURUSHOTHAMAN R, THUDI M, KRISHNAMURTHY L, UPADHYAYA H D, KASHIWAGI J, GOWDA C L L, VARSHNEY A K. Association of mid-reproductive stage canopy temperature depression with the molecular markers and grain yields of chickpea (Cicer arietinum L.) germplasm under terminal drought. Field Crops Research, 2015, 174: 1-11. doi: 10.1016/j.fcr.2015.01.007
[24]	张智韬, 许崇豪, 谭丞轩, 李宇, 宁纪锋. 基于无人机热红外遥感的玉米地土壤含水率诊断方法. 农业机械学报, 2020, 51(3): 180-190.
	ZHANG Z T, XU C H, TAN C X, LI Y, NING J F. Diagnosing method of soil moisture content in corn field based on thermal infrared remote sensing of UAV. Transactions of the Chinese Society for Agricultural Machinery, 2020, 51(3): 180-190. (in Chinese)
[25]	JACKSON R D, IDSO S B, REGINATO R J, PINTER, JR P J. Canopy temperature as a crop water stress indicator. Water Resources Research, 1981, 17(4): 1133-1138. doi: 10.1029/WR017i004p01133
[26]	OLIVO N, GIRONA J, MARSAL J. Seasonal sensitivity of stem water potential to vapour pressure deficit in grapevine. Irrigation Science, 2009, 27(2): 175-182. doi: 10.1007/s00271-008-0134-z
[27]	BERGER B, PARENT B, TESTER M. High-throughput shoot imaging to study drought responses. Journal of Experimental Botany, 2010, 61(13): 3519-3528. doi: 10.1093/jxb/erq201
[28]	ZIA S, ROMANO G, SPREER W, SANCHEZ C, CAIRNS J, ARAUS J L, MULLER J. Infrared thermal imaging as a rapid tool for identifying water-stress tolerant maize genotypes of different phenology. Journal of Agronomy and Crop Science, 2012, 13: 931-2250.
[29]	GRANT O M, TRONINA L, JONES H G, CHAVES M M. Exploring thermal imaging variables for the detection of stress responses in grapevine under different irrigation regimes. Journal of Experimental Botany, 2007, 58(4): 815-825. doi: 10.1093/jxb/erl153
[30]	李磊, 贾志清, 朱雅娟, 綦艳林. 我国干旱区植物抗旱机理研究进展. 中国沙漠, 2010, 30(5): 1053-1059.
	LI L, JIA Z Q, ZHU Y J, QI Y L. Research advances on drought resistance mechanism of plant species in arid area of China. Journal of Desert Research, 2010, 30(5): 1053-1059. (in Chinese)
[31]	MING H, ZHANG H, DEJONGE K C, COMAS L H, TROUT T J. Estimating maize water stress by standard deviation of canopy temperature in thermal imagery. Agricultural Water Management, 2016, 177: 400-409. doi: 10.1016/j.agwat.2016.08.031
[32]	吴金芝, 王志敏, 李友军, 张英华. 干旱胁迫下不同抗旱性小麦品种产量形成与水分利用特征. 中国农业大学学报, 2015, 20(6): 25-35.
	WU J Z, WANG Z M, LI Y J, ZHANG Y H. Characteristics of yield formation and water use in different drought tolerance cultivars of winter wheat under drought stress. Journal of China Agricultural University, 2015, 20(6): 25-35. (in Chinese)
[33]	IDSO S B, JACKSON R D, PINTER, JR P J, REGINATO R J, HATFIELD J L. Normalizing the stress-degree-day parameter for environmental variability. Agricultural Meteorology, 1981, 24(1): 45-55. doi: 10.1016/0002-1571(81)90032-7
[34]	XU J, LV Y P, LIU X Y, DALSON T, YANG S H, WU J. Diagnosing crop water stress of rice using infrared thermal imager under water deficit condition. International Journal of Agriculture & Biology, 2015. DOI: 10.17957/IJAB/15.0125. doi: 10.17957/IJAB/15.0125
[35]	BIJU S, FUENTES S, GUPTA D. The use of infrared thermal imaging as a non-destructive screening tool for identifying drought- tolerant lentil genotypes. Plant Physiology and Biochemistry, 2018, 127: 11-24. doi: 10.1016/j.plaphy.2018.03.005
[36]	BIAN J, ZHANG Z T, CHEN J Y, CHEN H Y, CUI C F, LI X W, CHEN S B, FU Q Q. Simplified evaluation of cotton water stress using high resolution Unmanned Aerial Vehicle thermal imagery. Remote Sensing, 2019, 11(3): 267. doi: 10.3390/rs11030267
[37]	张仁华. 定量热红外遥感模型及地面实验基础. 北京: 科学出版社, 2009.
	ZHANG R H. Quantitative Thermal Infrared Remote Sensing Model and Ground Experiment Basis. Beijing: Science Press, 2009. (in Chinese)
[38]	KARIMIZADEH R, MOHAMMADI M. Association of canopy temperature depression with yield of durum wheat genotypes under supplementary irrigated and rainfed conditions. Australian Journal of Crop Science, 2011, 5(2): 138-146.
[39]	KUMAR M, GOVINDASAMY, RANE J, SINGH A K, CHOUDHARY R L, RAINA S K, GEORGE P, AHER L K, SINGH N P. Canopy temperature depression (CTD) and canopy greenness associated with variation in seed yield of soybean genotypes grown in semi-arid environment. South African Journal of Botany, 2017, 113: 230-238. doi: 10.1016/j.sajb.2017.08.016
[40]	REBETZKE G J, RATTEY A R, FARQHAR G D, RICHARDS R A, CONDON A G. Genomic regions for canopy temperature and their genetic association with stomatal conductance and grain yield in wheat. Functional Plant Biology, 2013, 40(1): 14-33. doi: 10.1071/FP12184
[41]	KRISHNAMURTHY L, KASHIWAGI J, GAUR P M, UPADHYAYA H D, VADAZ V. Sources of tolerance to terminal drought in the chickpea (Cicer arietinum L.) minicore germplasm. Field Crops Research, 2010, 119(2/3): 322-330. doi: 10.1016/j.fcr.2010.08.002
[42]	JOAQUIM B, JORDI M, JOAN G, VICTORIA G D, ELIAS F, SUSAN L U, PABLO J Z T. Airborne thermal imagery to detect the seasonal evolution of crop water status in peach, nectarine and saturn peach orchards. Remote Sensing, 2016, 8(1): 39. doi: 10.3390/rs8010039
[43]	王士强, 胡银岗, 佘奎军, 周琳璘, 孟凡磊. 小麦抗旱相关农艺性状和生理生化性状的灰色关联度分析. 中国农业科学, 2007, 40(11): 2452-2459.
	WANG S Q, HU Y G, SHE K J, ZHOU L L, MENG F L. Gray relational grade analysis of agronomical and physi-biochemical traits related to drought tolerance in wheat. Scientia Agricultura Sinica, 2007, 40(11): 2452-2459. (in Chinese)
[44]	徐蕊, 王启柏, 张春庆, 吴承来. 玉米自交系抗旱性评价指标体系的建立. 中国农业科学, 2009, 42(1): 72-84.
	XU R, WANG Q B, ZHANG C Q, WU C L. Drought-resistance evaluation system of maize inbred. Scientia Agricultura Sinica, 2009, 42(1): 72-84. (in Chinese)
[45]	谢小玉, 张霞, 张兵. 油菜苗期抗旱性评价及抗旱相关指标变化分析. 中国农业科学, 2013, 46(3): 476-485.
	XIE X Y, ZHANG X, ZHANG B. Evaluation of drought resistance and analysis of variation of relevant parameters at seedling stage of rapeseed (Brassica napus L.). Scientia Agricultura Sinica, 2013, 46(3): 476-485. (in Chinese)
[46]	李龙, 毛新国, 王景一, 昌小平, 柳玉平, 景蕊莲. 小麦种质资源抗旱性鉴定评价. 作物学报, 2018, 44(7): 988-999. doi: 10.3724/SP.J.1006.2018.00988
	LI L, MAO X G, WANG J Y, CHANG X P, LIU Y P, JING R L. Drought tolerance evaluation of wheat germplasm resources. Acta Agronomica Sinica, 2018, 44(7): 988-999. (in Chinese) doi: 10.3724/SP.J.1006.2018.00988
[47]	唐延林, 黄敬峰, 王人潮, 王福民. 水稻遥感估产模拟模式比较. 农业工程学报, 2004, 20(1): 166-171.
	TANG Y L, HUANG J F, WANG R C, WANG F M. Comparsion of yield estimation simulated models of rice by remote sensing. Transactions of the Chinese Society of Agricultural Engineering, 2004, 20(1): 166-171. (in Chinese)
[48]	张龙龙, 杨明明, 董剑, 赵万春, 高翔, 陈冬阳. 三个小麦新品种不同生育阶段抗旱性的综合评价. 麦类作物学报, 2016, 36(4): 426-434.
	ZHANG L L, YANG M M, DONG J, ZHAO W C, GAO X, CHEN D Y. Comprehensive analysis of drought resistance of three new wheat cultivars at different growth stages. Journal of Triticeae Crops, 2016, 36(4): 426-434. (in Chinese)

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抗旱性级别 Drought resistance rank	抗旱指数 Drought resistance index	抗旱性评价标准 Drought resistance evaluation standard
1	≥1.30	极强 HR
2	1.10—1.29	强 R
3	0.90—1.09	中等 MR
4	0.70—0.89	弱 S
5	≤0.69	极弱 HS

代号 Number	品种名称 Cultivar	产量Grain yield（kg·hm^-2）		抗旱指数 Drought index	抗旱评价 Drought evaluation
代号 Number	品种名称 Cultivar	棚内 Output in dry land	棚外 Output in wet land	抗旱指数 Drought index	抗旱评价 Drought evaluation
1	洛旱19 Luohan 19	4816.98b	8610.52c	1.06	中等MR
2	洛旱22 Luohan 22	5026.80a	8713.45bc	1.14	强R
3	洛麦26 Luomai 26	4584.04c	9378.24a	0.88	弱S
4	中麦175 Zhongmai 175	4350.88d	9050.06b	0.82	弱S
5	百农207 Bainong 207	4299.84d	8144.90d	0.89	弱S
6	豫农516 Yunong516	4551.87c	8838.05b	0.92	中等MR
7	中麦895 Zhongmai 895	3873.35e	8831.18b	0.67	极弱HS
8	安农0711 Annong 0711	4390.08d	8953.79b	0.85	弱S
9	华成3366 Huacheng 3366	3915.60e	8679.40bc	0.69	极弱HS
10	晋麦47 Jinmai 47	4587.77c	8269.14d	1.00	中等MR

时期 Time	抗旱性评价 Drought evaluation	CETR	CTEP	CTSD	CTCV	CTD	CWSI	CRTD
拔节期 Jointing stage	强R	14.90-19.17	6.58	0.480	0.03	5.14	0.4	0.138
	中等MR	14.71-23.42	7.99-9.28	0.934-1.102	0.048-0.054	3.42-4.87	0.41-0.44	0.158-0.174
	弱S	14.98-23.51	9.62-10.00	1.036-1.273	0.053-0.065	2.22-3.00	0.45-0.47	0.164-0.210
	极弱HS	15.15-23.60	11.37-11.62	1.105-1.227	0.068-0.071	1.81-2.69	0.46-0.50	0.198-0.214
孕穗期 Booting stage	强R	21.93-26.28	7.63	0.660	0.028	-0.32	0.44	0.076
	中等MR	21.72-28.73	8.43-9.33	0.728-0.850	0.031-0.034	-0.66--1.13	0.46-0.48	0.086-0.096
	弱S	22.14-28.36	9.91-10.52	0.807-0.880	0.033-0.040	-1.42--1.88	0.48-0.50	0.101-0.112
	极弱HS	21.45-29.77	11.06-11.79	0.952-1.164	0.039-0.045	-1.65--2.38	0.51-0.54	0.119-0.153
开花期 Anthesis stage	强R	21.01-27.67	11.58	1.108	0.041	-4.17	0.47	0.105
	中等MR	24.92-35.29	12.21-14.29	1.298-1.601	0.043-0.051	-4.30--5.16	0.49-0.51	0.108-0.116
	弱S	25.42-36.19	13.52-14.95	1.270-1.812	0.047-0.056	-5.56--6.47	0.53-0.54	0.120-0.136
	极弱HS	25.90-38.48	15.14-18.82	1.824-1.963	0.054-0.060	-6.87--7.78	0.59-0.60	0.149-0.159

响应变异 Response variable	步骤 Step	拔节期 Jointing stage		孕穗期 Booting stage		开花期 Anthesis stage
响应变异 Response variable	步骤 Step	入选变量 Variable entered	精度 R²	入选变量 Variable entered	精度 R²	入选变量 Variable entered	精度 R²
抗旱指数DRI	S₁	CTD	0.706	CTD	0.624	CTD	0.672
	S₂	CTCV	0.837	CTCV	0.734	CWSI	0.768
	S₃	CWSI	0.872	—	—	—	—

响应变异 Response variable	步骤 Step	生育期 Growth stage	入选变量 Variable entered	估算精度 Estimation R²
抗旱指数 DRI	S₁	拔节 Jointing stage	CTD	0.706
	S₂	孕穗 Booting stage	CTCV	0.836
	S₃	开花 Anthesis stage	CWSI	0.902
	S₄	拔节 Jointing stage	CTSD	0.948

Identification and Evaluation of Drought Resistance of Wheat Varieties Based on Thermal Infrared Image

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