基于作物生长监测诊断仪的双季稻叶干重监测模型

doi:10.3724/SP.J.1006.2021.02077

Abstract

Abstract:

The quantitative, convenient and non-destructive monitoring of leaf dry weight (LDW) is critical for precise management in double cropping rice production. The objective of this study is to verify the accuracy of crop growth monitoring and diagnosis apparatus (CGMD, a passive multi-spectral sensor containing 810 nm and 720 nm wavelengths) in monitoring growth index of double cropping rice, and establish the model for monitoring LDW of double cropping rice based on CGMD. Plot experiments were conducted in Jiangxi province in 2016 and 2017, including eight early and late rice cultivars and four nitrogen application rates. The normalized difference vegetation index (NDVI), differential vegetation index (DVI), and ratio vegetation index (RVI) were measured at tillering, jointing, booting, heading and filling stages with two spectrometers, CGMD and analytical spectral devices field-spec handheld 2 (ASD FH2, a passive hyper-spectral sensor containing 325 nm to 1075 nm wavelengths). In order to verify the measurement precision of CGMD, the correlation relationship of vegetation indices between CGMD and ASD FH2 was analyzed. The LDW monitoring models of double cropping rice were established based on CGMD from an experimental dataset and then validated using an independent dataset involving different early and late rice cultivars and nitrogen application rates. The results indicated that the LDW of early and late rice were increased with the increase of nitrogen application rate at different growth stages, and exhibited “low-high-low” dynamic variation trend with early and late rice development progress. The NDVI, DVI, and RVI from CGMD and ASD FH2 were significantly correlation. The correlation coefficient (r) of NDVI, DVI, and RVI from CGMD and ASD FH2 were 0.9535-0.9972, 0.9099-0.9948, and 0.9298-0.9926, respectively. This result indicated that there was highly consistent of vegetation indices from CGMD and ASD FH2, and the CGMD could replace expensive ASD FH2 to measure NDVI, DVI and RVI. Compared with the three vegetation indices based on CGMD, the correlation between RVI_CGMD and LDW was the highest. The power function model based on RVI_CGMD could accurate monitoring LDW with a determination coefficient (R²) in the range of 0.8604-0.9216, the root mean square error (RMSE), relative root mean square error (RRMSE), and r of model validation in the range of 12.97-17.87 g m^-2, 4.88%-16.79%, and 0.9951-0.9992, respectively. Compared with the manual sampling measure LDW, CGMD method can timely and accurately measure the LDW dynamic variation of double cropping rice, which had a potential to be widely applied for growth precision diagnosis and high yield and high efficiency cultivation in double cropping rice production.

Key words: double cropping rice, leaf dry weight, crop growth monitoring and diagnosis apparatus, vegetation index, monitoring model

LI Yan-Da, CAO Zhong-Sheng, SHU Shi-Fu, SUN Bin-Feng, YE Chun, HUANG Jun-Bao, ZHU Yan, TIAN Yong-Chao. Model for monitoring leaf dry weight of double cropping rice based on crop growth monitoring and diagnosis apparatus[J].Acta Agronomica Sinica, 2021, 47(10): 2028-2035.

Figures/Tables 4

Fig. 1

Table 1

Table 2

Monitoring models of leaf dry weight for early and late rice under different growth stages based on CGMD vegetation indices"

植被指数 Vegetation index	生育期 Growth stages	早稻 Early rice		晚稻 Late rice
植被指数 Vegetation index	生育期 Growth stages	监测模型 Monitoring model	决定系数 R²	监测模型 Monitoring model	决定系数 R²
NDVI_CGMD	分蘖期 Tillering stage	$\text{LDW}=\text{32}\text{.173}\times {{\text{e}}^{\text{5}\text{.8687}\times \text{NDV}{{\text{I}}_{\text{CGMD}}}}}$	0.8304	$\text{LDW}=\text{54}\text{.595}\times {{\text{e}}^{\text{4}\text{.3443}\times \text{NDV}{{\text{I}}_{\text{CGMD}}}}}$	0.8932
	拔节期 Jointing stage	$\text{LDW}=\text{46}\text{.779}\times {{\text{e}}^{\text{4}\text{.9717}\times \text{NDV}{{\text{I}}_{\text{CGMD}}}}}$	0.8413	$\text{LDW}=\text{71}\text{.407}\times {{\text{e}}^{\text{4}\text{.4888}\times \text{NDV}{{\text{I}}_{\text{CGMD}}}}}$	0.8451
	孕穗期 Booting stage	$\text{LDW}=\text{68}\text{.835}\times {{\text{e}}^{\text{3}\text{.9738}\times \text{NDV}{{\text{I}}_{\text{CGMD}}}}}$	0.8291	$\text{LDW}=\text{69}\text{.912}\times {{\text{e}}^{\text{5}\text{.0304}\times \text{NDV}{{\text{I}}_{\text{CGMD}}}}}$	0.8782
	抽穗期 Heading stage	$\text{LDW}=\text{65}\text{.956}\times {{\text{e}}^{\text{3}\text{.0992}\times \text{NDV}{{\text{I}}_{\text{CGMD}}}}}$	0.8235	$\text{LDW}=\text{56}\text{.463}\times {{\text{e}}^{\text{5}\text{.7332}\times \text{NDV}{{\text{I}}_{\text{CGMD}}}}}$	0.8775
	灌浆期 Filling stage	$\text{LDW}=\text{47}\text{.316}\times {{\text{e}}^{\text{4}\text{.5720}\times \text{NDV}{{\text{I}}_{\text{CGMD}}}}}$	0.8465	$\text{LDW}=\text{61}\text{.509}\times {{\text{e}}^{\text{5}\text{.0302}\times \text{NDV}{{\text{I}}_{\text{CGMD}}}}}$	0.8303
DVI_CGMD	分蘖期 Tillering stage	LDW=1512.80×DVI_CGMD +38.511	0.8312	LDW=785.22×DVI_CGMD +61.521	0.8197
	拔节期 Jointing stage	LDW=867.63×DVI_CGMD +54.730	0.8178	LDW=912.36×DVI_CGMD +91.027	0.8162
	孕穗期 Booting stage	LDW=712.08×DVI_CGMD +94.664	0.8267	LDW=717.89×DVI_CGMD +114.750	0.8379
	抽穗期 Heading stage	LDW=546.71×DVI_CGMD +74.901	0.8190	LDW=755.20×DVI_CGMD +92.275	0.8230
	灌浆期 Filling stage	LDW=956.46×DVI_CGMD +37.730	0.8445	LDW=770.92×DVI_CGMD +77.178	0.8244
RVI_CGMD	分蘖期 Tillering stage	LDW =39.217×RVI_CGMD^1.9585	0.8697	LDW =50.006×RVI_CGMD^2.1792	0.9216
	拔节期 Jointing stage	LDW =52.865×RVI_CGMD^1.8850	0.8892	LDW =64.510×RVI_CGMD^2.3807	0.9045
	孕穗期 Booting stage	LDW =70.446×RVI_CGMD^1.4743	0.8758	LDW =66.149×RVI_CGMD^2.2845	0.9118
	抽穗期 Heading stage	LDW =51.792×RVI_CGMD^1.6805	0.8604	LDW =59.700×RVI_CGMD^2.3213	0.8851
	灌浆期 Filling stage	LDW =41.993×RVI_CGMD^2.4764	0.8793	LDW =56.741×RVI_CGMD^2.6011	0.8854

Table 2

Fig. 2

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作物 Crop	生育期 Growth stages	归一化植被指数 NDVI		差值植被指数 DVI		比值植被指数 RVI
作物 Crop	生育期 Growth stages	差异显著性概率P	相关系数 r	差异显著性概率P	相关系数 r	差异显著性概率P	相关系数 r
早稻	分蘖期 Tillering stage	0.60	0.9675^**	0.49	0.9099^**	0.34	0.9298^**
Early rice	拔节期 Jointing stage	0.55	0.9960^**	0.61	0.9870^**	0.42	0.9320^**
	孕穗期 Booting stage	0.46	0.9613^**	0.87	0.9948^**	0.54	0.9894^**
	抽穗期 Heading stage	0.54	0.9820^**	0.69	0.9799^**	0.38	0.9926^**
	灌浆期 Filling stage	0.48	0.9654^**	0.50	0.9779^**	0.58	0.9543^**
晚稻	分蘖期 Tillering stage	0.91	0.9550^**	0.30	0.9387^**	0.93	0.9807^**
Late rice	拔节期 Jointing stage	0.82	0.9972^**	0.22	0.9750^**	0.89	0.9703^**
	孕穗期 Booting stage	0.83	0.9535^**	0.81	0.9772^**	0.94	0.9899^**
	抽穗期 Heading stage	0.94	0.9711^**	0.78	0.9751^**	0.86	0.9438^**
	灌浆期 Filling stage	0.79	0.9880^**	0.84	0.9732^**	0.61	0.9823^**

Model for monitoring leaf dry weight of double cropping rice based on crop growth monitoring and diagnosis apparatus

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