Nitrogen Fertilization Decision-Making Model During Sugar Growth Period of Sugar Beet Roots Based on Multispectral Technology

doi:10.11924/j.issn.1000-6850.casb2023-0343

Abstract

Abstract:

To obtain fast and accurate information of the growth of sugar beet and to achieve high quality and high yield by precise fertilization of sugar beet in the cold region of Northeast China, the multi-spectral images and field measurements of sugar beet ‘KWS7748’ were obtained at the experimental site of Hulan Campus of Heilongjiang University in Harbin, Heilongjiang Province in 2022. The optimum vegetation index reflecting the physiology and biochemistry of sugar beet during this period was selected, and a decision-making model for sugar beet N application was constructed. The results showed that the sugar beet yield increased and gradually decreased, while the sugar content significantly reduced with the increase of N-fertilizer application. Based on the optimum N application rate of the plot, it generated a fertilizer map of N fertilizer variables used for sugar beet in the experimental area, which was consistent with the actual situation. UAV remote sensing technology is of theoretical importance in diagnosing nitrogen nutrition in sugar beet and guiding the precise application of nitrogen fertilizer to achieve high quality and yield.

Key words: sugar beet, multispectral, nitrogen fertilizer recommendation, vegetation index, UAV remote sensing

WANG Jingyun, HU Xiaohang, DONG Xinjiu, MA Yahuai, LI Yanli. Nitrogen Fertilization Decision-Making Model During Sugar Growth Period of Sugar Beet Roots Based on Multispectral Technology[J]. Chinese Agricultural Science Bulletin, 2024, 40(10): 132-139.

Figures/Tables 11

编号	植被指数	计算公式	参考文献
1	GNDVI	$R N I R - R g r e e n R N I R + R g r e e n$	[21]
2	LCI	$R N I R - R r e d - e d g e R N I R + R r e d$	[22]
3	MCARI2	$3.75 (R N I R - R r e d) - 1.95 (R N I R - R g r e e n) (2 R N I R + 1) 2 - (6 R N I R - 5 R r e d) - 0.5$	[23]
4	NDVI	$R N I R - R r e d R N I R + R r e d$	[24]
5	NDRE	$R N I R - R r e d - e d g e R N I R + R r e d - e d g e$	[25]
6	OSAVI	$1.16 (R N I R - R r e d) R N I R - R r e d + 0.16$	[26]
7	SAVI	$R N I R - R r e d (R N I R + R r e d + L) (1 + L)$ L=0.5	[27]

编号	植被指数	计算公式	参考文献
1	GNDVI	$R N I R - R g r e e n R N I R + R g r e e n$	[21]
2	LCI	$R N I R - R r e d - e d g e R N I R + R r e d$	[22]
3	MCARI2	$3.75 (R N I R - R r e d) - 1.95 (R N I R - R g r e e n) (2 R N I R + 1) 2 - (6 R N I R - 5 R r e d) - 0.5$	[23]
4	NDVI	$R N I R - R r e d R N I R + R r e d$	[24]
5	NDRE	$R N I R - R r e d - e d g e R N I R + R r e d - e d g e$	[25]
6	OSAVI	$1.16 (R N I R - R r e d) R N I R - R r e d + 0.16$	[26]
7	SAVI	$R N I R - R r e d (R N I R + R r e d + L) (1 + L)$ L=0.5	[27]

模型类型	植被指数	a	b	c	R²	RMSE
线性模型 Y=a+bx	GNDVI	-64.89	126.29		0.6524	0.8383
	LCI	-2.69	73.03		0.6981	0.7812
	MCARI2	83.75	-49.50		0.0260	1.4033
	NDVI	-131.79	192.53		0.3023	1.1878
	NDRE	9.42	68.00		0.6679	0.8195
	OSAVI	-131.26	165.47		0.3034	1.1868
	SAVI	-131.40	256.17		0.3046	1.1858
二项式 Y=a+bx+cx²	GNDVI	1335.82	-2088.89	853.36	0.6603	0.8736
	LCI	-530.87	693.75	-184.02	0.7086	0.8091
	MCARI2	-49198.33	86723.84	-38176.87	0.4346	1.1271
	NDVI	-21759.85	39018.49	-17450.71	0.3775	1.1826
	NDRE	-869.73	852.28	-167.17	0.6977	0.8241
	OSAVI	-15554.28	32356.44	-16786.48	0.3755	1.1845
	SAVI	-37083.85	49873.37	-16727.74	0.3764	1.1836
对数 Y=a+blnx	GNDVI	59.43	104.63		0.6515	0.8394
	LCI	62.96	42.74		0.7005	0.7782
	MCARI2	34.66	-43.26		0.0255	1.4037
	NDVI	59.61	171.97		0.3030	1.1871
	NDRE	64.60	30.75		0.6729	0.8132
	OSAVI	34.11	171.43		0.3041	1.1862
	SAVI	108.95	171.58		0.3053	1.1852
指数 Y=ae^bx	GNDVI	2.85	3.18		0.6547	0.8356
	LCI	13.81	1.82		0.6954	0.7848
	MCARI2	90.49	-0.92		0.0239	1.4049
	NDVI	0.57	4.76		0.2990	1.1905
	NDRE	18.74	1.69		0.6638	0.8244
	OSAVI	0.58	4.10		0.3002	1.1895
	SAVI	0.57	6.34		0.3014	1.1885
幂函数 Y=ax^b	GNDVI	65.25	2.64		0.6538	0.8366
	LCI	70.86	1.07		0.6980	0.7815
	MCARI2	35.10	-1.06		0.0250	1.4040
	NDVI	0.57	4.76		0.2990	1.1905
	NDRE	73.63	0.76		0.6691	0.8180
	OSAVI	34.57	4.24		0.3010	1.1889
	SAVI	220.43	4.25		0.3021	1.1879

References 30

[1]	刘洪, 董元华, 隋跃宇, 等. 甜菜抗病品种产生抗性的土壤微生物机理[J]. 中国农学通报, 2021, 37(15):78-86. doi: 10.11924/j.issn.1000-6850.casb2020-0441
[2]	白如霄, 范守杰, 和海秀, 等. 不同氮磷肥施用量对甜菜生长、生理及产量品质的影响[J]. 江苏农业科学, 2022, 50(10):117-124.
[3]	倪洪涛, 孙元, 黄雅曦. 甜菜氮代谢的研究进展[J]. 中国甜菜糖业, 2008(4):35-38.
[4]	FU Y, YANG G, PU R, et al. An overview of crop nitrogen status assessment using hyperspectral remote sensing: Current status and perspectives[J]. European journal of agronomy, 2021, 124:126241. doi: 10.1016/j.eja.2021.126241 URL
[5]	尹彦鑫, 孟志军, 赵春江, 等. 大田无人农场关键技术研究现状与展望[J]. 智慧农业(中英文), 2022, 4(4):1-25.
[6]	付婷婷, 邓永卓, 韩志江. 无人机在现代农业生产中的应用[J]. 天津农林科技, 2017(4):14-15.
[7]	于丰华, 曹英丽, 许童羽, 等. 基于高光谱遥感处方图的寒地分蘖期水稻无人机精准施肥[J]. 农业工程学报, 2020, 36(15):103-110.
[8]	许童羽, 白驹驰, 郭忠辉, 等. 基于无人机高光谱遥感的水稻氮营养诊断方法[J]. 农业机械学报, 2023, 54(2):189-197,222.
[9]	郑启帅. 基于多旋翼无人机的油菜变量追肥技术研究[D]. 杭州: 浙江大学, 2019.
[10]	孙志伟, 王晓琳, 张启明, 等. 基于无人机可见光谱平台的烤烟氮素营养诊断[J]. 光谱学与光谱分析, 2021, 41(2):586-591.
[11]	魏全全, 李飞, 张萌, 等. 基于数字图像技术的马铃薯氮素营养估测及验证[J]. 生态学杂志, 2021, 40(9):3017-3024.
[12]	宋成阳. 无人机表型技术在冬小麦产量性状鉴定中的应用[D]. 乌鲁木齐: 新疆农业大学, 2022.
[13]	QIU Z, MA F, LI Z, et al. Estimation of nitrogen nutrition index in rice from UAV RGB images coupled with machine learning algorithms[J]. Computers and electronics in agriculture, 2021, 189:106421. doi: 10.1016/j.compag.2021.106421 URL
[14]	张晶. 基于高光谱成像技术的甜菜生理生态参数检测方法研究[D]. 呼和浩特: 内蒙古农业大学, 2021.
[15]	CAO Y, LI G L, LUO Y K, et al. Monitoring of sugar beet growth indicators using wide-dynamic-range vegetation index (WDRVI) derived from UAV multispectral images[J]. Computers and electronics in agriculture, 2020, 171:105331. doi: 10.1016/j.compag.2020.105331 URL
[16]	CHANCIA R, VAN AARDT J, PETHYBRIDGE S, et al. Predicting table beet root yield with multispectral UAS imagery[J]. Remote sensing, 2021, 13(11):2180. doi: 10.3390/rs13112180 URL
[17]	SAIF M S, CHANCIA R, PETHYBRIDGE S, et al. Forecasting table beet root yield using spectral and textural features from hyperspectral UAS imagery[J]. Remote sensing, 2023, 15(3):794. doi: 10.3390/rs15030794 URL
[18]	WALSH O S, NAMBI E, SHAFIAN S, et al. UAV-based NDVI estimation of sugarbeet yield and quality under varied nitrogen and water rates[J]. Agrosystems, geosciences & environment, 2023, 6(1):e20337.
[19]	ZENG Y, HAO D, HUETE A, et al. Optical vegetation indices for monitoring terrestrial ecosystems globally[J]. Nature reviews earth & environment, 2022, 3(7):477-493.
[20]	FERCHICHI A, ABBES A B, BARRA V, et al. Forecasting vegetation indices from spatio-temporal remotely sensed data using deep learning-based approaches: A systematic literature review[J]. Ecological informatics, 2022:101552.
[21]	樊鸿叶, 李姚姚, 卢宪菊, 等. 基于无人机多光谱遥感的春玉米叶面积指数和地上部生物量估算模型比较研究[J]. 中国农业科技导报, 2021, 23(9):112-120.
[22]	边立丽. 基于地面与低空影像的烤烟氮素营养诊断[D]. 北京: 中国农业科学院, 2021.
[23]	姜海玲, 杨杭, 陈小平, 等. 利用光谱指数反演植被叶绿素含量的精度及稳定性研究[J]. 光谱学与光谱分析, 2015, 35(4):975-981.
[24]	ROUSE J J W, HAAS R H, DEERING D W, et al. Monitoring the vernal advancement and retrogradation (green wave effect) of natural vegetation[R]. 1974.
[25]	BARNES E M, CLARKE T R, RICHARDS S E, et al. Coincident detection of crop water stress, nitrogen status and canopy density using ground based multispectral data[A].//Proceedings of the Fifth International Conference on Precision Agriculture[C]. Bloomington, MN, USA, 2000:6.
[26]	RONDEAUX G, STEVEN M, BARET F. Optimization of soil-adjusted vegetation indices[J]. Remote sensing of environment, 1996, 55(2):95-107. doi: 10.1016/0034-4257(95)00186-7 URL
[27]	LIAQAT M U, CHEEMA M J M, HUANG W, et al. Evaluation of MODIS and Landsat multiband vegetation indices used for wheat yield estimation in irrigated Indus Basin[J]. Computers and electronics in agriculture, 2017, 138:39-47. doi: 10.1016/j.compag.2017.04.006 URL
[28]	林明, 王荣华, 曹禹, 等. 基于光合性状和农艺性状评价不同甜菜品种的生态稳定性[J]. 中国农业大学学报, 2023, 28(2):43-58.
[29]	VERMA B, PRASAD R, SRIVASTAVA P K, et al. Investigation of optimal vegetation indices for retrieval of leaf chlorophyll and leaf area index using enhanced learning algorithms[J]. Computers and electronics in agriculture, 2022, 192:106581. doi: 10.1016/j.compag.2021.106581 URL
[30]	CILIA C, PANIGADA C, ROSSINI M, et al. Nitrogen status assessment for variable rate fertilization in maize through hyperspectral imagery[J]. Remote sensing, 2014, 6(7):6549-6565. doi: 10.3390/rs6076549 URL