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

Abstract

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)

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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References 43

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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%

Prediction of Water Content of Winter Wheat Plant Based on Comprehensive Index Synergetic Optimization

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