基于红外传感器的棉花叶片温度变化特征及其影响因子分析

doi:10.11963/cs20210064

摘要/Abstract

摘要：

【目的】叶片是对环境变化较敏感的植物器官,叶片温度是植物重要的生理指标。探究棉花叶片温度的昼夜变化特性、明确环境因子对叶片温度的影响。【方法】基于红外温度传感器对棉花叶片温度进行全自动实时监测,进而探究不同生育时期和不同天气条件下棉花叶片温度的昼夜变化特征,并通过相关性分析、逐步回归分析及通径分析方法探究叶片温度、叶气温差与环境因子的关系。【结果】不同天气条件和不同生育时期叶片温度的昼夜变化存在差异,叶片温度的变化幅度小于气温。环境因子（降水量除外）与棉花叶片温度、环境因子（水汽压除外）和叶气温差均显著相关（P<0.05）,气温与叶片温度的相关性最高（r=0.890）,空气相对湿度与叶气温差的相关性最高（r=0.825）。通径分析结果表明,对叶片温度的影响因子按决策系数排序依次为：气温>光合有效辐射>水汽压;光合有效辐射和水汽压均主要通过气温间接影响叶片温度的变化。对叶气温差的影响因子按决策系数排序依次为空气相对湿度>日照时间>水汽压;日照时间、水汽压都主要通过空气相对湿度间接影响叶气温差的变化。【结论】探究了棉花叶片温度的昼夜动态变化,初步分析了环境因素对叶片温度和叶气温差的综合影响,研究结果可以为棉花生产和智能化管理提供参考。

关键词: 红外传感器; 叶片温度; 实时监测; 环境因子; 棉花

Abstract:

[Objective] Leaf blade is a plant organ that is sensitive to environmental changes. Leaf temperature is an important physiological index of plants. This study aims to explore the day-night variation characteristics of leaf temperature and to clarify the influence of meteorological factors on leaf temperature. [Method] In this study, the temperature of cotton leaves was automatically monitored in real-time based on infrared temperature sensors, and then the characteristics of day-night variation of cotton leaves temperature in different growth periods and under different weather conditions were explored, and the relationship of meteorological factors with leaf temperature and leaf-air temperature difference were explored by correlation analysis, stepwise regression analysis and path statistical analysis. [Result] The day-night variation of leaf temperature was different in various weather conditions and growth stages, and the variation range of leaf temperature was less than that of air temperature. Environmental factors were significantly correlated with cotton leaf temperature (except precipitation) and leaf-air temperature difference (except water vapor pressure) (P<0.05). The correlation between air temperature and leaf temperature was the highest (r = 0.890). The correlation between air relative humidity and leaf-air temperature difference was the highest (r = 0.825). The results of path analysis showed that the order of factors’ effect on leaf temperature according to decision coefficient was air temperature > photosynthetic active radiation > water vapor pressure. Photosynthetic active radiation and water vapor pressure mainly indirectly affected the change of leaf temperature through air temperature. The order of the effects of analyzed environmental factors on the leaf-air temperature difference according to the decision coefficient is air relative humidity > sunshine time > water vapor pressure. Sunshine time and water vapor pressure indirectly affect the change of leaf-air temperature difference through air relative humidity. [Conclusion] This study explored the day-night dynamic changes of cotton leaf temperature, and preliminarily explored the comprehensive effects of environmental factors on leaf temperature and leaf-air temperature difference. The results provide reference for cotton production and intelligent management.

Key words: infrared sensor; leaf temperature; real-time monitoring; environmental factors; cotton

王亚茹,杨北方,雷亚平,熊世武,韩迎春,王占彪,冯璐,李小飞,邢芳芳,辛明华,吴沣槭,陈家乐,李亚兵. 基于红外传感器的棉花叶片温度变化特征及其影响因子分析[J]. 棉花学报, 2022, 34(3): 235-246.

Wang Yaru,Yang Beifang,Lei Yaping,Xiong Shiwu,Han Yingchun,Wang Zhanbiao,Feng Lu,Li Xiaofei,Xing Fangfang,Xin Minghua,Wu Fengqi,Chen Jiale,Li Yabing. Analysis of variation of cotton leaf temperature and its influencing factors based on infrared sensors[J]. Cotton Science, 2022, 34(3): 235-246.

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链接本文: https://journal.cricaas.com.cn/Jweb_mhxb/CN/10.11963/cs20210064

https://journal.cricaas.com.cn/Jweb_mhxb/CN/Y2022/V34/I3/235

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参考文献 36

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月份 Month	最高温度 Maximum temperature/℃	最低温度 Minimum temperature/℃	平均温度 Average temperature/℃	降水量 Precipitation/ mm	日照时间 Sunshine time/h	风速 Wind speed/ （m·s^-1）
4月April	20.36	8.74	14.65	23	208.62	3.45
5月May	27.97	13.57	20.96	31	286.61	2.58
6月June	33.86	20.94	27.15	61	231.70	1.99
7月July	32.07	22.90	27.26	653	201.32	2.20
8月August	29.62	21.34	25.33	166	213.74	2.20
9月September	25.85	18.27	22.20	331	186.56	2.32

因子 Factor	Y₁	Y₂	X₁	X₂	X₃	X₄	X₅	X₆	X₇	X₈	X₉	X₁₀	X₁₁
Y₂	-0.628^*	1.000
X₁	0.890^**	-0.763^**	1.000
X₂	-0.630^**	0.825^**	-0.696^**	1.000
X₃	0.721^**	-0.730^**	0.682^**	-0.670^**	1.000
X₄	-0.253	0.412^**	-0.294	0.349^*	-0.319^*	1.000
X₅	-0.351^*	0.395^**	-0.340^*	0.332^*	-0.257	0.270^*	1.000
X₆	0.735^**	-0.704^**	0.689^**	-0.694^**	0.968^**	-0.272^*	-0.256	1.000
X₇	0.726^**	-0.717^**	0.689^**	-0.703^**	0.970^**	-0.273^*	-0.250	0.998^**	1.000
X₈	0.372^*	0.038	0.24	0.434^*	-0.044	0.037	0.004	-0.080	-0.099	1.000
X₉	0.717^**	-0.576^**	0.723^**	-0.893^**	0.511^**	-0.313^*	-0.303^*	0.536^**	0.542^**	-0.179	1.000
X₁₀	0.741^**	-0.557^**	0.743	-0.848^**	0.478^*	-0.320^*	-0.280^*	0.503^*	0.507^**	-0.074	0.984^**	1.000
X₁₁	0.642^**	-0.420^**	0.647	-0.730^**	0.307^*	-0.282^*	-0.204	0.336^*	0.339^*	-0.018	0.913^**	0.964^**	1.000
X₁₂	-0.460^*	0.426^*	-0.514	0.776^**	-0.282^*	0.247	0.224	-0.282^*	-0.300^*	0.296^*	-0.893^**	-0.899^**	-0.902^**

因变量Dependent variable	自变量Independent variable	直接通径系数 Direct path coefficient	相关系数Correlation coefficient	间接通径系数Indirect path coefficient						决策系数Decision coefficient (R²)
因变量Dependent variable	自变量Independent variable	直接通径系数 Direct path coefficient	相关系数Correlation coefficient	X₁	X₂	X₃	X₆	X₈	Total	决策系数Decision coefficient (R²)
Y₁	X₁	0.584	0.890	-	-	-	0.402	0.140	0.542	0.890
	X₆	0.354	0.735	0.243	-	-	-	-0.030	0.240	0.736
	X₈	0.260	0.372	0.060	-	-	-0.020	-	0.040	0.372
Y₂	X₂	0.603	0.825	-	-	0.226	-	-0.103	0.123	0.631
	X₃	-0.337	-0.730	-	-0.404	-	-	0.010	-0.394	0.378
	X₈	-0.238	0.038	-	0.262	0.015	-	-	0.277	-0.075