基于综合指标协同优化的冬小麦植株水分含量预测

doi:10.3864/j.issn.0578-1752.2023.22.004

摘要/Abstract

摘要：

【目的】基于冬小麦冠层温度参数、形态指标和生理指标3种综合指标构建不同生育时期冬小麦植株含水量（PWC）反演模型，探寻更全面、精准的水分亏缺监测方法，为冬小麦抗旱提供理论依据。【方法】以冬小麦为研究对象，设置3个水分梯度，分别为水分亏缺处理（W1）：35 mm和（W2）：48 mm，对照处理（W3）：68 mm；2个小麦品种洛麦22（一般耐旱品种）和周麦27（弱耐旱品种）。分别获取拔节期、孕穗期和灌浆期冬小麦的冠层温度参数（冠层温度标准差（CTSD）和作物水分胁迫指数（CWSI））、形态指标（株高、茎粗、地上部生物量和叶面积指数）和生理指标（气孔导度、蒸腾速率和光合速率），按照平均权重原则分别构建综合温度参数指标（comprehensive temperature parameter indicator，CTPI）、综合生长指标（comprehensive growth indicator，CGI）和综合生理指标（comprehensive physiological indicator，CPI）。分析植株含水量与综合指标之间的相关关系，并采用多元线性回归（MLR）、偏最小二乘回归（PLSR）和支持向量机（SVM）方法分生育时期构建基于综合指标的PWC反演模型。【结果】不同生育时期内冬小麦的冠层温度参数、形态指标及生理指标水分亏缺处理（W1、W2）较对照处理（W3）均表现出显著性差异（P<0.05）。孕穗期和灌浆期的综合指标（CTPI、CGI和CPI）与PWC具有显著相关关系，相关系数（r）分别为-0.70（-0.78）、0.84（0.80）和0.83（0.76）。采用MLR、PLSR和SVM方法，基于综合指标（CTPI、CGI和CPI）构建PWC反演预测模型均具有较高的预测精度，其中以SVM构建的孕穗期PWC模型最优，R²_cal（R²_val）、RMSE_cal（RMSE_val）和nRMSE_cal（nRMSE_val）分别为0.878（0.815）、2.06%（2.37%）和3.10%（3.33%）。【结论】基于综合指标（CTPI、CGI和CPI）构建的SVM-PWC模型能够很好地预测冬小麦各生育时期水分亏缺状况，可为黄淮海平原冬小麦防旱抗旱提供理论依据。

关键词: 冬小麦, 水分亏缺, 综合指标, 植株含水量, 支持向量机

Abstract:

【Objective】To find a more comprehensive and accurate method to monitor the water deficit and to provide a theoretical basis for drought relief of winter wheat, the present study was conducted to construct an inversion model of plant water content (PWC) at different growth stages based on three comprehensive indexes, namely, canopy temperature, morphology and physiology indexes of winter wheat.【Method】The winter wheat was studied by setting up three water treatments (water deficit treatment W1: 35 mm, water deficit treatment W2: 48 mm, and control treatment W3: 68 mm) and two wheat varieties (general drought resistant variety Luomai 22 and weak drought resistant variety Zhoumai 27). Canopy temperature parameters (canopy temperature standard deviation (CTSD) and crop water stress index (CWSI)), morphological indicators (plant height, stem diameter, aboveground biomass, and leaf aera index (LAI)) and physiological indicators (stomatal conductance, transpiration rate, and photosynthetic rate) of winter wheat were obtained at jointing, booting, and filling stages, respectively. Comprehensive temperature parameter indicators (CTPI), comprehensive growth indicators (CGI) and comprehensive physiological indicators (CPI) based on the average weight principle were constructed. The correlation between PWC and comprehensive indicators was analyzed, and multiple linear regression (MLR), partial least squares recurrence (PLSR) and support vector machine (SVM) methods were used to construct the PWC inversion model based on comprehensive indicators according to the growth period.【Result】The canopy temperature parameters, morphology and physiological indexes of winter wheat at different growth stages showed significant differences between water deficit treatments (W1, W2) and control treatment (W3) (P<0.05). Comprehensive indicators (CTPI, CGI and CPI) at booting and filling stages have a significant correlation with PWC, with correlation coefficients (r) of -0.70 (-0.78), 0.84 (0.80) and 0.83 (0.76), respectively. Using MLR, PLSR and SVM methods, the PWC inversion prediction model based on comprehensive indicators (CTPI, CGI and CPI) has high prediction accuracy, among which the PWC model built by SVM is the best, R²_cal (R²_val), RMSE_cal (RMSE_val), and nRMSE_cal (nRMSE_val) were 0.878 (0.815), 2.06% (2.37%), and 3.10% (3.33%), respectively.【Conclusion】The SVM-PWC model based on the comprehensive indicators CTPI, CGI and CPI can well predict the water deficit of winter wheat at different growth stages, and provide theoretical basis for drought prevention and drought resistance of winter wheat in the Huang-Huai-Hai Plain.

Key words: winter wheat, water deficit, comprehensive index, plant water content (PWC), support vector machine (SVM)

高晨凯, 刘水苗, 李煜铭, 吴鹏年, 王艳丽, 刘长硕, 乔毅博, 关小康, 王同朝, 温鹏飞. 基于综合指标协同优化的冬小麦植株水分含量预测[J]. 中国农业科学, 2023, 56(22): 4403-4416.

GAO ChenKai, LIU ShuiMiao, LI YuMing, WU PengNian, WANG YanLi, LIU ChangShuo, QIAO YiBo, GUAN XiaoKang, WANG TongChao, WEN PengFei. Prediction of Water Content of Winter Wheat Plant Based on Comprehensive Index Synergetic Optimization[J]. Scientia Agricultura Sinica, 2023, 56(22): 4403-4416.

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生育时期 Growing stage	处理 Treatment		CETR	MTD	CTSD	CTCV	CRTD	CWSI
拔节期 Jointing stage	洛麦22 Luomai 22	W1	19.41-29.61	10.85-14.14	1.21-1.46	0.052-0.059	0.163-0.184	0.41-0.45
		W2	16.94-25.17	8.03-8.63	0.92-1.00	0.047-0.049	0.136-0.144	0.34-0.36
		W3	15.85-22.01	5.52-7.03	0.83-0.88	0.042-0.044	0.113-0.124	0.30-0.32
	周麦27 Zhoumai 27	W1	20.37-29.56	14.29-15.26	1.09-1.64	0.052-0.069	0.174-0.191	0.42-0.49
		W2	17.21-25.26	8.26-9.52	0.96-1.08	0.048-0.051	0.169-0.178	0.40-0.43
		W3	16.38-23.18	6.24-6.68	0.83-0.87	0.041-0.045	0.134-0.162	0.34-0.37
孕穗期 Booting stage	洛麦22 Luomai 22	W1	23.74-33.91	9.43-13.40	1.55-1.94	0.066-0.081	0.179-0.191	0.42-0.48
		W2	21.60-28.26	5.99-6.36	0.73-0.87	0.031-0.036	0.117-0.134	0.37-0.39
		W3	20.24-26.04	4.43-5.71	0.55-0.69	0.024-0.029	0.088-0.116	0.29-0.33
	周麦27 Zhoumai 27	W1	24.34-37.74	9.59-13.67	1.66-2.29	0.074-0.093	0.216-0.244	0.41-0.49
		W2	22.80-29.69	7.15-8.06	0.85-0.93	0.032-0.036	0.131-0.143	0.42-0.48
		W3	20.48-26.44	5.38-6.12	0.64-0.79	0.027-0.033	0.101-0.127	0.29-0.39
灌浆期 Filling stage	洛麦22 Luomai 22	W1	31.32-42.48	11.37-12.21	1.72-1.95	0.051-0.056	0.167-0.185	0.45-0.51
		W2	29.31-40.07	7.80-8.38	1.35-1.61	0.041-0.046	0.115-0.146	0.42-0.45
		W3	27.86-34.51	5.99-7.40	0.61-0.89	0.021-0.028	0.085-0.106	0.31-0.38
	周麦27 Zhoumai 27	W1	31.68-43.53	10.70-13.35	1.98-2.53	0.058-0.070	0.173-0.208	0.50-0.55
		W2	30.74-41.23	8.41-9.63	1.29-1.63	0.041-0.048	0.126-0.144	0.43-0.46
		W3	28.40-36.35	7.65-8.52	0.73-1.15	0.024-0.036	0.119-0.123	0.37-0.41

生育时期 Growing stage	建模Modeling									验证Validation
	MLR			PLSR			SVM			MLR			PLSR			SVM
	R²	RMSE	nRMSE	R²	RMSE	nRMSE	R²	RMSE	nRMSE	R²	RMSE	nRMSE	R²	RMSE	nRMSE	R²	RMSE	nRMSE
拔节期 Jointing stage	0.707	2.62%	3.30%	0.735	3.91%	5.48%	0.728	2.97%	4.48%	0.622	4.67%	6.21%	0.658	7.50%	10.04%	0.702	3.71%	5.07%
孕穗期 Booting stage	0.771	2.70%	3.86%	0.827	2.23%	3.20%	0.878	2.06%	3.10%	0.676	4.11%	5.87%	0.766	3.98%	5.65%	0.815	2.37%	3.33%
灌浆期 Filling stage	0.721	3.30%	5.53%	0.744	3.30%	4.98%	0.770	3.16%	5.54%	0.668	4.04%	6.57%	0.704	3.72%	5.67%	0.718	3.70%	5.31%
全生育期 Whole reproductive period	0.494	6.02%	8.23%	0.572	5.10%	6.38%	0.711	5.06%	6.32%	0.387	10.71%	17.83%	0.508	9.26%	16.21%	0.665	7.62%	13.34%