基于无人机图像的水稻地上部生物量估算

舒时富; 李艳大; 曹中盛; 孙滨峰; 叶春; 吴罗发; 朱艳; 丁艳锋; 何勇

doi:10.19303/j.issn.1008-0384.2022.007.002

基于无人机图像的水稻地上部生物量估算

doi: 10.19303/j.issn.1008-0384.2022.007.002

1.
江西省农业科学院农业工程研究所/江西省智能农机装备工程研究中心/江西省农业信息化工程技术研究中心，江西南昌　330200
2.
南京农业大学，江苏南京　210095
3.
浙江大学生物系统工程与食品科学学院，浙江杭州　310029

基金项目: 江西省重点研发计划项目（20192BBF60052、20212BBF61013、20212BBF63040、20202BBFL63046、20202BBFL63044）；国家自然科学基金项目（31460320）；国家“万人计划”青年拔尖人才项目（2020-2023）；江西省“双千计划”项目（2020-2022）

详细信息

作者简介:
舒时富（1986−），男，硕士，副研究员，研究方向：作物精确管理和农业机械化（E-mail：shusf@foxmail.com）

通讯作者:
李艳大（1980−），男，博士，研究员，研究方向：信息农学与农机化技术（E-mail：liyanda2008@126.com）

中图分类号: S 511
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出版历程
- 收稿日期: 2022-01-08
- 修回日期: 2022-06-28
- 网络出版日期: 2022-08-07
- 刊出日期: 2022-07-28

Estimation of Aboveground Rice Biomass by Unmanned Aerial Vehicle Imaging

1.
Institute of Agricultural Engineering, Jiangxi Academy of Agricultural Sciences/Jiangxi Province Engineering Research Center of Intelligent Agricultural Machinery Equipment/Jiangxi Province Engineering Research Center of Information Technology in Agriculture, Nanchang, Jiangxi　330200, China
2.
Nanjing Agricultural University, Nanjing, Jiangsu　210095, China
3.
College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, Zhejiang　310029, China

摘要

摘要: 目的为探究无人机图像估算水稻地上部生物量（Aboveground biomass，AGB）的可行性，明确各图像特征与水稻AGB的定量关系，构建基于图像特征的水稻AGB估算模型。方法通过实施2个品种和4个施氮水平的小区试验，于分蘖期、孕穗期和齐穗期测定水稻AGB，同步采用无人机搭载数码相机获取水稻图像并提取颜色指数和纹理特征，分析其在不同生育期与水稻AGB之间的相关性，构建定量估算模型，并对模型进行检验。结果颜色指数中红蓝差值（r-b）与水稻AGB之间的相关性最好，纹理特征参数（G-mean）与水稻AGB之间的相关性最高；基于红蓝差值（r-b）和G-mean构建的水稻AGB双指数模型优于单一指数模型，全生育期估算模型y=2544.507+5054.243x₁−145.543x₂−556.553x₁x₂+27379.41x₁²+3.927x₂²，建模决定系数（R²）为0.9202，模型检验的决定系数（R²）为0.9112。结论基于颜色指数（r-b）和纹理特征参数（G-mean）融合构建的AGB估算模型可准确的估算水稻AGB，在水稻长势快速无损监测和精确管理中具有应用价值。
- 水稻 /
- 地上部生物量 /
- 无人机图像 /
- 估算模型.
Abstract: Objective Feasibility of using images generated by unmanned aerial vehicle (UAV) to estimate the aboveground biomass (AGB) on a rice field was evaluated for crop production prediction. Methods On fields of two different varieties of rice fertilized with 4 varied nitrogen applications, AGB of rice plants at tillering, booting, and full heading stages were recorded by using the UAV imaging technology. Data on color and texture measurements were extracted from the images to correlate with corresponding AGB. A mathematic model was constructed, tested, and validated for prediction accuracy. Result On color, the red and blue differentiation (r-b) of the images highly correlated with the AGB; on texture, it was the G-mean. A prediction model was thus obtained for the entire growth period as y=2 544.507+5 054.243x₁−145.543x₂−556.553x₁x₂+27 379.41x₁²+3.927x₂² , which had a correlation coefficient (R²) of 0.920 2 and a test determination coefficient of 0.911 2. Conclusion The prediction model based on r-b and G-mean derived from the UAV images performed satisfactorily in monitoring the AGB for the entire growth period of rice in the field. It was conceivably applicable for the farming operation and crop management.
- Rice /
- aboveground biomass /
- UAV image /
- estimation model

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图 1 小区试验布置

Figure 1. Arrangement for plot experiment

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图 2 不同施氮水平下水稻地上部生物量的动态变化

Figure 2. Dynamic changes on AGB of rice plants grown with varied nitrogen fertilizations

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图 3 水稻孕穗期无人机RGB图像

Figure 3. UAV RGB images of rice plants at booting stage

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图 4 基于Co-occurrence measures处理的水稻孕穗期图像

Figure 4. Co-occurrence measures-based UAV image of rice plants at booting stage

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表 1 地上部生物量与颜色指数之间的相关性

Table 1. Correlation between AGB and color indices

序号 Number	颜色指数 Color index	相关系数r Correlation coefficient
序号 Number	颜色指数 Color index	分蘖期 Tillering	孕穗期 Booting	齐穗期 Heading	全生育期 Whole growth period
1	r-b	−0.899^**	−0.914^**	−0.887^**	−0.890^**
2	VARI	0.809^**	0.844^**	0.833^**	0.841^**
3	G	−0.818^**	−0.834^**	−0.843^**	−0.841^**
4	NRI	−0.821^**	−0.833^**	−0.822^**	−0.823^**
5	r/b	−0.817^**	−0.825^**	−0.821^**	−0.822^**
6	R	−0.802^**	−0.821^**	−0.812^**	−0.811^**
7	WI	0.792^**	0.803^**	0.793^**	0.795^**
8	NBI	0.764^*	0.798^**	0.792^**	0.793^**
9	MGRVI	0.748^*	0.778^*	0.772^*	0.769^*
10	GRVI	0.750^*	0.757^*	0.764^*	0.766^*
11	ExR	−0.738^*	−0.745^*	−0.741^*	−0.746^*
12	g/b	−0.63^*	−0.73^*	−0.731^*	−0.728^*
13	ExGR	0.532	0.614^*	0.609^*	0.611^*
14	B	−0.569	−0.583	−0.578	−0.572
15	g-b	−0.478	−0.532	−0.523	−0.516
16	ExG	0.245	0.321	0.328	0.331
17	NGI	0.245	0.312	0.321	0.315
18	r+b	−0.245	−0.311	−0.301	−0.312
19	RGBVI	0.042	0.052	0.049	0.051
代表P=0.05显著水平，代表P=0.01极显著水平。表5同。 Significance level: represents P=0.05. Extremely significant level：** represents P=0.01.The same as Table 5.

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表 2 基于红蓝差值（r-b）的AGB估算模型构建和验证

Table 2. Construction and validation of AGB estimation model based on （r-b）

生育期 Growth stage	建模 Calibration			检验 Validation
生育期 Growth stage	类型 Type	估算模型 Estimation model	决定系数R ² Coefficient of determination R²	RMSE/ (g·m⁻²)	RRMSE/ %	R²
分蘖期 Tillering	二次多项式 Quadratic	y = 2690.9x² − 2806.5x + 942.59	0.8778	22.07	6.09	0.8682
	指数 Exponential	y = 972.12e^−3.47x	0.8774	24.15	6.64	0.8639
	对数 Logarithmic	y = −355.2ln(x) − 83.638	0.8772	22.94	6.28	0.8568
	乘幂 Power	y = 100.57x^−1.011	0.8748	24.83	6.78	0.8487
	线性 Linear	y = −1213.7x + 711.68	0.8719	24.25	6.65	0.8543
孕穗期 Booting	二次多项式 Quadratic	y = −1491.4x² − 836.69x + 892.93	0.8820	20.22	5.58	0.8713
	线性 Linear	y = −1431.4x + 948.53	0.8801	20.54	5.66	0.8654
	指数 Exponential	y = 1017.8e^−2.182x	0.8741	21.95	6.09	0.8501
	对数 Logarithmic	y = −268.9ln(x) + 220.54	0.8574	27.76	7.58	0.8435
	乘幂 Power	y = 336.6x^−0.408	0.8409	29.73	8.09	0.8212
齐穗期 Heading	二次多项式 Quadratic	y = 3480.4x² − 2549.8x + 1311.2	0.8728	23.99	6.62	0.8643
	指数 Exponential	y = 1292.7e^−1.637x	0.8694	25.67	7.08	0.8566
	线性 Linear	y = −1778.5x + 1281.7	0.8630	26.46	7.25	0.8521
	对数 Logarithmic	y = −135ln(x) + 754.89	0.8159	27.9	7.68	0.8072
	乘幂 Power	y = 799.19x^−0.123	0.7956	33.17	9.04	0.7013
全生育期 Whole growth period	二次多项式 Quadratic	y = 705.61x² − 3450.2x + 1364.8	0.8693	24.65	6.74	0.8614
	指数 Exponential	y = 1688.3e^−4.88x	0.8237	29.91	8.15	0.8122
	线性 Linear	y = −3177.4x + 1344.4	0.8689	26.59	7.30	0.8461
	对数 Logarithmic	y = −381.9ln(x) + 25.399	0.7251	35.06	9.62	0.6531
	乘幂 Power	y = 238.62x^−0.547	0.5263	51.25	14.09	0.4101

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表 3 地上部生物量与纹理特征参数之间的相关性

Table 3. Correlations between AGB and texture features

序号 Number	纹理特征参数 Texture feature	相关系数 r correlation coefficient r
序号 Number	纹理特征参数 Texture feature	分蘖期 Tillering	孕穗期 Booting	齐穗期 Heading	全生育期 Whole growth period
1	G-均值	−0.907^**	−0.932^**	−0.917^**	−0.901^**
1	G-mean	−0.907^**	−0.932^**	−0.917^**	−0.901^**
2	R-均值	−0.887^**	−0.891^**	−0.890^**	−0.889^**
2	R-mean	−0.887^**	−0.891^**	−0.890^**	−0.889^**
3	R-二阶矩	0.583^*	0.782^*	0.727^*	0.801^**
3	R-second moment	0.583^*	0.782^*	0.727^*	0.801^**
4	B-均值	−0.768^*	−0.749^*	−0.762^*	−0.787^*
4	B-mean	−0.768^*	−0.749^*	−0.762^*	−0.787^*
5	R-方差	0.652^*	0.742^*	0.772^*	0.713^*
5	R-variance	0.652^*	0.742^*	0.772^*	0.713^*
6	B-二阶矩	0.543	0.652^*	0.544	0.522
6	B-second moment	0.543	0.652^*	0.544	0.522
7	G-方差	0.568	0.563	0.612^*	0.555
7	G-variance	0.568	0.563	0.612^*	0.555
8	B-方差	0.464	0.494	0.432	0.564
8	B-variance	0.464	0.494	0.432	0.564
9	G-对比度	0.333	0.452	0.411	0.562
9	G-contrast	0.333	0.452	0.411	0.562
10	R-相异性	0.488	0.521	0.517	0.553
10	R-dissimilarity	0.488	0.521	0.517	0.553
11	B-信息熵	−0.327	−0.431	−0.417	−0.476
11	B-entropy	−0.327	−0.431	−0.417	−0.476
12	G-协同性	−0.415	−0.463	−0.434	−0.408
12	G-homogeneity	−0.415	−0.463	−0.434	−0.408
13	R-信息熵	−0.457	−0.325	−0.402	−0.376
13	R-entropy	−0.457	−0.325	−0.402	−0.376
14	G-相异性	0.404	0.441	0.454	0.412
14	G-dissimilarity	0.404	0.441	0.454	0.412
15	B-协同性	0.212	0.221	0.322	0.435
15	B-homogeneity	0.212	0.221	0.322	0.435
16	B-对比度	0.126	0.321	0.311	0.411
16	B-contrast	0.126	0.321	0.311	0.411
17	G-信息熵	−0.371	−0.362	−0.322	−0.377
17	G-entropy	−0.371	−0.362	−0.322	−0.377
18	R-协同性	0.195	0.211	0.322	0.164
18	R-homogeneity	0.195	0.211	0.322	0.164
19	B-相关性	−0.204	−0.264	−0.228	−0.224
19	B-correlation	−0.204	−0.264	−0.228	−0.224
20	G-二阶矩	0.04	0.211	0.142	0.226
20	G-second moment	0.04	0.211	0.142	0.226
21	R-相关性	−0.107	−0.124	−0.214	−0.135
21	R-correlation	−0.107	−0.124	−0.214	−0.135
22	R-对比度	0.118	0.123	0.135	0.074
22	R-contrast	0.118	0.123	0.135	0.074
23	B-相异性	0.093	0.132	0.127	0.211
23	B-dissimilarity	0.093	0.132	0.127	0.211
24	G-相关性	−0.006	−0.013	−0.021	−0.089
24	G-correlation	−0.006	−0.013	−0.021	−0.089

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表 4 基于纹理特征参数（G-mean）的水稻各生育期AGB模型的构建和检验

Table 4. Construction and validation of AGB estimation model for each growth stage of rice plants based on texture G-mean

生育期 Growth stage	建模 Calibration			检验 Validation
生育期 Growth stage	类型 Type	估算模型 Estimation model	决定系数R ² Coefficient of determination R²	均方根误差 RMSE/ (g·m⁻²)	相对均方根误差 RRMSE/ %	决定系数 R²
分蘖期 Tillering	二次多项式 Quadratic	y = 0.0736x² − 14.46x + 780.83	0.8875	22.06	6.08	0.8797
	对数 Logarithmic	y = −327ln(x) + 1525.7	0.8873	21.67	6.01	0.8635
	指数 Exponential	y = 902.39e^−0.026x	0.8871	23.89	6.56	0.8717
	线性 Linear	y = −9.0632x + 684.31	0.8859	23.65	6.48	0.8790
	乘幂 Power	y = 9934.4x^−0.934	0.8742	24.45	6.70	0.8311
孕穗期 Booting	二次多项式 Quadratic	y = 0.1879x² − 25.845x + 1201.6	0.9128	17.14	4.75	0.9034
	指数 Exponential	y = 1237.7e^−0.024x	0.9126	16.63	4.63	0.9017
	对数 Logarithmic	y = −411.4ln(x) + 1994.6	0.9118	15.70	4.38	0.9004
	线性 Linear	y = −15.965x + 1075.6	0.9106	17.97	4.98	0.9094
	乘幂 Power	y = 5007.2x^−0.627	0.9071	19.69	5.42	0.8903
齐穗期 Heading	二次多项式 Quadratic	y = 2.2052x² − 114.72x + 2398.5	0.9014	17.26	4.81	0.8945
	乘幂 Power	y = 5702.9x^−0.584	0.9001	17.52	4.95	0.8809
	对数 Logarithmic	y = −634.1ln(x) + 2892.2	0.8948	17.66	4.91	0.8843
	指数 Exponential	y = 1930.3e^−0.033x	0.8888	20.01	5.57	0.8671
	线性 Linear	y = −35.866x + 1714.6	0.8778	22.64	6.22	0.8121
全生育期 Whole growth period	二次多项式 Quadratic	y = 0.9499x² − 86.442x + 2254	0.9005	11.10	3.11	0.8993
	乘幂 Power	y = 45897x^−1.33	0.8925	16.09	4.53	0.8713
	对数 Logarithmic	y = −865.4ln(x) + 3492.7	0.8913	13.95	3.89	0.8671
	指数 Exponential	y = 2447.4e^−0.051x	0.8883	11.69	3.29	0.9098
	线性 Linear	y = −32.075x + 1557.3	0.8692	26.59	7.31	0.8463

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表 5 基于r-b和G-mean的AGB双指数线性回归模型的构建和检验

Table 5. Construction and validation of AGB estimation model based on r-b and G-mean

生育期 Growth stage	建模 Calibration		检验 Validation
生育期 Growth stage	估算模型 Model	决定系数R ² Coefficient of determination R²	均方根误差 RMSE/ (g·m⁻²)	相对均方根误差 RRMSE/ %	决定系数 R²
分蘖期 Tillering	y=879.0997−3088.61x₁+5.9053x₂+364.8907x₁x₂−18580.4 x₁²−1.607 x₂²	0.9099	16.46	4.68	0.8903
孕穗期 Booting	y=1218.506−1364.43x₁−16.1009x₂+380.0562x₁x₂−21120.2947x₁²−1.4778 x₂²	0.9155	22.01	3.32	0.9006
齐穗期 Heading	y=2422.079+3430.592x₁−143.099 x₂−381.908x₁x₂+13707.691x₁²+4.369x₂²	0.9043	36.46	3.37	0.8901
全生育期 Whole growth period	y=2544.507+5054.243x₁−145.543x₂−556.553x₁x₂+27379.41x₁²+3.927x₂²	0.9202	76.20	10.91	0.9112

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参考文献(24)

[1]	ZHU W, SUN Z, PENG J, et al. Estimating maize above-ground biomass using 3d point clouds of multi-source unmanned aerial vehicle data at multi-spatial scales [J]. Remote Sens, 2019, 11: 2678. doi: 10.3390/rs11222678
[2]	田婷, 张青, 张海东, 等. 基于无人机遥感的水稻产量估测 [J]. 中国稻米, 2022, 28(1):67−71, 77. TIAN T, ZHANG Q, ZHANG H D. Estimating yield of rice based on remote sensing by unmanned aerial vehicle [J]. China Rice, 2022, 28(1): 67−71, 77.(in Chinese)
[3]	周蓉, 赵天忠, 吴发云. 基于Landsat 8遥感影像的地上生物量模型反演研究 [J]. 西北林学院学报, 2022, 37(2):186−192. doi: 10.3969/j.issn.1001-7461.2022.02.26 ZHOU R, ZHAO T Z, WU F Y. Aboveground biomass model based on Landsat 8 remote sensing images [J]. Journal of Northwest Forestry University, 2022, 37(2): 186−192.(in Chinese) doi: 10.3969/j.issn.1001-7461.2022.02.26
[4]	姜妍, 王琳, 杨月, 等. 无人机高光谱成像技术在作物生长信息监测中的应用 [J]. 东北农业大学学报, 2022, 53(3):88−96. JIANG Y, WANG L, YANG Y, et al. Application of UAV hyperspectral imaging technology in crop growth information monitoring [J]. Journal of Northeast Agricultural University, 2022, 53(3): 88−96.(in Chinese)
[5]	杨福芹, 李天驰, 冯海宽, 等. 基于无人机数码影像的冬小麦氮素营养诊断研究 [J]. 福建农业学报, 2021, 36(3):369−378. YANG F Q, LI T C, FENG H K, et al. UAV digital image-assisted monitoring on nitrogen nutrition of winter wheat in the field [J]. Fujian Journalof Agricultural Sciences, 2021, 36(3): 369−378.(in Chinese)
[6]	梁胤豪, 陈全, 董彩霞, 等. 基于深度学习和无人机遥感技术的玉米雄穗检测研究 [J]. 福建农业学报, 2020, 35(4):456−464. LIANG Y H, CHEN Q, DONG C X, et al. Application of deep-learning and UAV for field surveying corn tassel [J]. Fujian Journal of Agricultural Sciences, 2020, 35(4): 456−464.(in Chinese)
[7]	YANG G J, LIU J G, ZHAO C J, et al. Unmanned aerial vehicle remote sensing for field-based crop phenotyping: Current status and perspectives [J]. Frontiers in Plant Science, 2017, 8: 1111. doi: 10.3389/fpls.2017.01111
[8]	CASANOVA D, EPEMA G F, GOUDRIAAN J. Monitoring rice reflectance at field level for estimating biomass and LAI [J]. Field Crops Research, 1998, 55(1/2): 83−92.
[9]	TAKAHASHI W, NGUYEN-CONG V, KAWAGUCHI S, et al. Statistical models for prediction of dry weight and nitrogen accumulation based on visible and near-infrared hyper-spectral reflectance of rice canopies [J]. Plant Production Science, 2000, 3(4): 377−386. doi: 10.1626/pps.3.377
[10]	GNYP M L, MIAO Y X, YUAN F, et al. Hyperspectral canopy sensing of paddy rice aboveground biomass at different growth stages [J]. Field Crops Research, 2014, 155: 42−55. doi: 10.1016/j.fcr.2013.09.023
[11]	王秀珍, 黄敬峰, 李云梅, 等. 水稻地上鲜生物量的高光谱遥感估算模型研究 [J]. 作物学报, 2003, 29(6):815−821. doi: 10.3321/j.issn:0496-3490.2003.06.004 WANG X Z, HUANG J F, LI Y M, et al. Study on hyperspectral remote sensing estimation models for the ground fresh biomass of rice [J]. Acta Agronomica Sinica, 2003, 29(6): 815−821.(in Chinese) doi: 10.3321/j.issn:0496-3490.2003.06.004
[12]	DAI M, YANG T L, YAO Z S, et al. Wheat biomass estimation in different growth stages based on color and texture features of UAV images [J/OL]. Smart Agriculture, Published online: 2022-02-22 14: 21: 52. URL: https://kns.cnki.net/kcms/detail/10.1681.S.20220221.1425.002.html.
[13]	杨俊, 丁峰, 陈晨, 等. 小麦生物量及产量与无人机图像特征参数的相关性 [J]. 农业工程学报, 2019, 35(23):104−110. doi: 10.11975/j.issn.1002-6819.2019.23.013 YANG J, DING F, CHEN C, et al. Correlation of wheat biomass and yield with UAV image characteristic parameters [J]. Transactions of the Chinese Society of Agricultural Engineering, 2019, 35(23): 104−110.(in Chinese) doi: 10.11975/j.issn.1002-6819.2019.23.013
[14]	刘畅, 杨贵军, 李振海, 等. 融合无人机光谱信息与纹理信息的冬小麦生物量估测 [J]. 中国农业科学, 2018, 51(16):3060−3073. doi: 10.3864/j.issn.0578-1752.2018.16.003 LIU C, YANG G J, LI Z H, et al. Biomass estimation in winter wheat by UAV spectral information and texture information fusion [J]. Scientia Agricultura Sinica, 2018, 51(16): 3060−3073.(in Chinese) doi: 10.3864/j.issn.0578-1752.2018.16.003
[15]	陈晨. 基于无人机图像的小麦生物量与产量的估测研究[D]. 扬州: 扬州大学, 2019 CHEN C. Estimation of wheat biomass and yield based on UAV images[D]. Yangzhou: Yangzhou University, 2019. (in Chinese)
[16]	张建, 谢田晋, 尉晓楠, 等. 无人机多角度成像方式的饲料油菜生物量估算研究 [J]. 作物学报, 2021, 47(9):1816−1823. ZHANG J, XIE T J, WEI X N, et al. Estimation of feed rapeseed biomass based on multi-angle oblique imaging technique of unmanned aerial vehicle [J]. Acta Agronomica Sinica, 2021, 47(9): 1816−1823.(in Chinese)
[17]	刘杨, 冯海宽, 孙乾, 等. 不同分辨率无人机数码影像的马铃薯地上生物量估算研究 [J]. 光谱学与光谱分析, 2021, 41(5):1470−1476. LIU Y, FENG H K, SUN Q, et al. Estimation study of above ground biomass in potato based on UAV digital images with different resolutions [J]. Spectroscopy And Spectral Analysis, 2021, 41(5): 1470−1476.(in Chinese)
[18]	陶惠林, 徐良骥, 冯海宽, 等. 基于无人机数码影像的冬小麦株高和生物量估算 [J]. 农业工程学报, 2019, 35(19):107−116. doi: 10.11975/j.issn.1002-6819.2019.19.013 TAO H L, XU L J, FENG H K, et al. Estimation of plant height and biomass of winter wheat based on UAV digital image [J]. Transactions of the Chinese Society of Agricultural Engineering, 2019, 35(19): 107−116.(in Chinese) doi: 10.11975/j.issn.1002-6819.2019.19.013
[19]	李艳大, 叶春, 曹中盛, 等. 基于作物生长监测诊断仪的双季稻叶片氮含量和氮积累量监测 [J]. 应用生态学报, 2020, 31(9):3040−3050. doi: 10.13287/j.1001-9332.202009.012 LI Y D, YE C, CAO Z S, et al. Monitoring leaf nitrogen concentration and nitrogen accumulation of double cropping rice based on crop growth monitoring and diagnosis apparatus [J]. Chinese Journal of Applied Ecology, 2020, 31(9): 3040−3050.(in Chinese) doi: 10.13287/j.1001-9332.202009.012
[20]	叶春, 刘莹, 李艳大, 等. 基于RGB颜色空间的早稻氮素营养监测研究 [J]. 中国农业大学学报, 2020, 25(8):25−34. doi: 10.11841/j.issn.1007-4333.2020.08.03 YE C, LIU Y, LI Y D, et al. Monitoring the nitrogen nutrition of early rice based on RGB color space [J]. Journal of China Agricultural University, 2020, 25(8): 25−34.(in Chinese) doi: 10.11841/j.issn.1007-4333.2020.08.03
[21]	石媛媛. 基于数字图像的水稻氮磷钾营养诊断与建模研究[D]. 杭州: 浙江大学, 2011 SHI Y Y. Rice nutrition diagnosis and modeling based on digital image[D]. Hangzhou: Zhejiang University, 2011. (in Chinese)
[22]	吴华兵, 朱艳, 田永超, 等. 棉花冠层高光谱参数与叶片氮含量的定量关系 [J]. 植物生态学报, 2007, 31(5):903−909. doi: 10.17521/cjpe.2007.0114 WU H B, ZHU Y, TIAN Y C, et al. Relationship between canopy hyperspectra parameter and leaf nitrogen concentration in cotton [J]. Journal of Plant Ecology, 2007, 31(5): 903−909.(in Chinese) doi: 10.17521/cjpe.2007.0114
[23]	LEE K J, LEE B W. Estimation of rice growth and nitrogen nutrition status using color digital camera image analysis [J]. European Journal of Agronomy, 2013, 48: 57−65. doi: 10.1016/j.eja.2013.02.011
[24]	班松涛. 水稻长势无人机遥感监测研究[D]. 杨凌: 西北农林科技大学, 2020 BAN S T. Monitoring rice growth conditions using UAV remote sensing system[D]. Yangling: Northwest A&F University, 2020. (in Chinese)