基于无人机影像多时相的小麦品种氮效率分类识别

doi:10.3864/j.issn.0578-1752.2024.09.006

Abstract

Abstract:

【Objective】To explore the potential of UAV remote sensing in nitrogen efficiency classification and recognition, a nitrogen efficiency classification method for wheat varieties was constructed, so as to provide the theoretical basis and technical support for nitrogen efficient variety screening.【Method】Six agronomic indicators related to nitrogen efficiency at maturity stage (yield, plant nitrogen accumulation, nitrogen physiological use efficiency, plant dry biomass, total nitrogen uptake of grains, and N harvest index) were used to construct the principal component synthesis value, and K-Means cluster analysis was performed on them. The 121 wheat varieties were divided into three types: high, medium, and low nitrogen efficiency types. A UAV remote sensing platform equipped with a multi-spectral camera was used to obtain remote sensing images of wheat at the jointing, booting and flowering stages, and 34 vegetation indices were extracted to analyze the correlation between vegetation index and nitrogen efficiency comprehensive value. The accuracy of nitrogen efficiency classification models of support vector machine (SVM), random forest (RF), and K-nearest neighbor (KNN) classification methods were compared, and the overall classification accuracy (OA) and Kappa coefficient were used to compare the classification and recognition ability of wheat varieties in different growth periods. Three different feature set screening methods(ReliefF algorithm, Boruta algorithm and RF-RFE algorithm) were used to comprehensively evaluate the optimized feature subsets, and an appropriate classification and recognition method for wheat varieties nitrogen efficiency was established.【Result】With the progress of wheat growth stage, the correlation between vegetation index and the comprehensive value of nitrogen efficiency gradually increased, which reached the highest correlation coefficient at flowering stage (r=0.502). The full feature set of vegetation indices was used to classify the nitrogen efficiency of wheat varieties. For the data of single growth stage, SVM model had the best classification accuracy at flowering stage (OA=77.1%, Kappa=0.591), and the worst classification accuracy at jointing stage (OA=65.6%, Kappa=0.406). In general, the classification accuracy of nitrogen efficiency of varieties with multi-growth stage data fusion was higher than that of single growth stage, among which SVM model with jointing stage + booting stage + flowering stage had the best classification accuracy (OA=80.6%, Kappa=0.669). In order to reduce the number of feature set variables in multi-growth period data fusion, the feature optimization effects of RF-RFE, Boruta and ReliefF algorithms were compared and analyzed. The optimal feature subset based on RF-RFE algorithm had the highest classification accuracy, and its OA and Kappa coefficients were 4.0% and 10.1% higher than those of the full feature set classification model, respectively. Among them, the data fusion of three growth stages had the best classification accuracy (OA=85.4%, Kappa=0.749).【Conclusion】The nitrogen efficiency evaluation method with six nitrogen efficiency indexes - principal component analysis -K-Means were established in this study. The RF-RFE algorithm effectively optimized the number of characteristic subsets of the multi-growth period combination, and obtained high classification accuracy. A nitrogen efficiency classification model of wheat varieties based on the fusion of multi-growth period combination and RF-RFE-SVM technology was established, which provided the theoretical basis and technical support for the rapid and accurate classification and identification of wheat varieties with nitrogen efficiency.

Key words: winter wheat, UAV, vegetation index, multiple growth periods, feature selection, nitrogen efficiency classification

ZANG ShaoLong, LIU LinRu, GAO YueZhi, WU Ke, HE Li, DUAN JianZhao, SONG Xiao, FENG Wei. Classification and Identification of Nitrogen Efficiency of Wheat Varieties Based on UAV Multi-Temporal Images[J].Scientia Agricultura Sinica, 2024, 57(9): 1687-1708.

Figures/Tables 17

Fig. 1

Table 1

Numbers and names of the studied raw cultivars"

编号 Number	品种名称 Variety name	编号 Number	品种名称 Variety name	编号 Number	品种名称 Variety name	编号 Number	品种名称 Variety name
C1	中麦30 Zhongmai 30	C32	泛育麦20 Fanyumai 20	C62	丰德存5号 Fengdecun 5	C92	西农926 Xinong 926
C2	新麦30 Xinmai 30	C33	西农99 Xinong 99	C63	郑麦1926 Zhengmai 1926	C93	科大116 Keda 116
C3	秋乐168 Qiule 168	C34	泛麦8号 Fanmai 8	C64	矮抗58 Aikang 58	C94	天民304 Tianmin 304
C4	大平原1号 Dapingyuan 1	C35	中育1686 Zhongyu 1686	C65	鑫华818 Xinhua 818	C95	郑366 Zheng 366
C5	新麦26 Xinmai 26	C36	开麦1502 Kaimai 1502	C66	西农633 Xinong 633	C96	豫农416 Yunong 416
C6	周麦27 Zhoumai 27	C37	中麦578 Zhongmai 578	C67	西农579 Xinong 579	C97	郑麦113 Zhengmai 113
C7	郑麦369 Zhengmai 369	C38	漯麦18 Luomai 18	C68	豫农907 Yunong 907	C98	豫农186 Yunong 186
C8	洛麦41 Luomai 41	C39	兰考198 Lankao 198	C69	浚麦8105 Xunmai 8105	C99	西农161 Xinong 161
C9	西农20 Xinong 20	C40	平安0602 Pingan 0602	C70	西农586 Xinong 586	C100	郑麦33 Zhengmai 33
C10	平安658 Pingan 658	C41	泰农18 Tainong 18	C71	豫农908 Yunong 908	C101	郑育11 Zhengyu 11
C11	怀川709 Huaichuan 709	C42	阜麦936 Fumai 936	C72	艾麦24 Aimai 24	C102	豫农804 Yunong 804
C12	周麦30 Zhoumai 30	C43	平安518 Pingan 518	C73	漯丰7011 Luomai 7011	C103	泉麦39 Quanmai 39
C13	洛麦37 Luomai 37	C44	新麦45 Xinmai 45	C74	稷麦337 Jimai 337	C104	连麦1901 Lianmai 1901
C14	怀川758 Huaichuan 758	C45	云台301 Yuntai 301	C75	富麦916 Fumai 916	C105	周麦28 Zhoumai 28
C15	漯麦163 Luomai 163	C46	优麦3号 Youmai 3	C76	春晓158 Chunxiao 158	C106	百农889 Bainong 889
C16	偃高58 Yangao 58	C47	豫麦49198 Yumai 49198	C77	滑育麦1号 Huayumai 1	C107	轮选369 Lunxuan 369
C17	洛麦31 Luomai 31	C48	扬麦22 Yangmai 22	C78	轮选136 Lunxuan 136	C108	洛麦27 Luomai 27
C18	新麦60 Xinmai 60	C49	郑麦163 Zhengmai 163	C79	许麦1706 Xumai 1706	C109	许科168 Xuke 168
C19	天宁38号 Tianning 38	C50	轮选989 Lunxuan 989	C80	昌麦20 Changmai 20	C110	洛麦29 Luomai 29
C20	百麦1811 Baimai 1811	C51	轮选125 Lunxuan 125	C81	徐麦17106 Xumai 17106	C111	天麦119 Tianmai 119
C21	郑麦136 Zhengmai 136	C52	百农207 Bainong 207	C82	新麦65 Xinmai 65	C112	保丰1903 Baofeng 1903
C22	西农511 Xinong 511	C53	豫麦18 Yumai 18	C83	周麦33号 Zhoumai 33	C113	漯丰1901 Luofeng 1901
C23	郑麦158 Zhengmai 158	C54	轮选69 Lunxuan 69	C84	怀川101 Huaichuan 101	C114	许麦1636 Xumai 1636
C24	中育1428 Zhongyu 1428	C55	豫麦61 Yumai 61	C85	吉兴653 Jixing 653	C115	阜麦16 Fumai 16
C25	囤麦259 Tunmai 259	C56	小偃6号 Xiaoyan 6	C86	鹤麦601 Hemai 601	C116	中育1220 Zhongyu 1220
C26	洛麦26 Luomai 26	C57	济麦229 Jimai 229	C87	憨丰3468 Hanfeng 3468	C117	新麦29 Xinmai 29
C27	洛麦34 Luomai 34	C58	存麦29 Cunmai 29	C88	职院171 Zhiyuan 171	C118	周麦32 Zhoumai 32
C28	囤麦257 Tunmai 257	C59	西农979 Xinong 979	C89	郑研麦182 Zhengyanmai 182	C119	囤麦127 Tunmai 127
C29	郑品麦8号 Zhengpinmai 8	C60	太麦198 Taimai 198	C90	百农4199 Bainong 4199	C120	郑麦618 Zhengmai 618
C30	郑麦1860 Zhengmai 1860	C61	涡麦44 Womai 44	C91	郑大181 Zhengda 181	C121	泛育麦18 Fanyumai 18
C31	存麦16 Cunmai 16

Table 1

Fig. 2

Fig. 3

Table 2

Spectral indices and calculation formula in this study"

植被指数 Vegetation indices	公式 Formulas
归一化植被指数NDVI	(Rnir-Rred)/(Rnir+Rred)
重归一化植被指数RDVI	0.5(Rnir-Rred)/(Rnir+Rred)
优化土壤调节植被指数OSAVI	1.16(Rnir-Rred)/(Rnir+Rred+0.16)
归一化绿度植被指数GNDVI	(Rnir-Rgreen)/(Rnir+Rgreen)
红波段比值植被指数RVI_red	Rnir/Rred
红边归一化植被指数NDRE	(Rnir-Rre)/(Rnir+Rre)
双波段增强植被指数EVI2	2.5(Rnir-Rred)/(Rnir+2.4Rred+1)
改良叶绿素吸收率指数MCARI	[Rre-Rred-0.2(Rre-Rgreen)]/(Rre/Rred)
转化叶绿素吸收反射指数TCARI	3[Rnir-Rred-0.2(Rnir-Rgreen)(Rnir/Rred)]
改善简单比率植被指数MSR	(Rnir/Rred-1)/[(Rnir/Rred)^0.5+1]
结构不敏感色素指数SIPI	(Rnir-Rblue)/(Rnir-Rred)
植被衰减指数PSRI	(Rnir-Rgreen)/Rnir
宽动态植被指数WDRVI	(0.1Rnir-Rred)/(0.1Rnir+Rred)
差值植被指数DVI	Rnir-Rred
非线性指数NLI	(Rnir²-Rred)/(Rnir²+Rred)
三角植被指数TVI	60(Rnir-Rgreen)-100(Rred-Rgreen)
改善三角植被指数MTVI2	1.5[1.2(Rnir-Rgreen)-2.5(Rred-Rgreen)]/[(2Rnir+1)²-(6Rnir-5Rred^0.5)-0.5]^0.5
红边土壤调整植被指数RESAVI	1.5(Rnir-Rre)/(Rnir+Rred+0.5)
改进土壤调节植被指数MSAVI	0.5{2Rnir+1-[(2Rnir+1)²-8(Rnir-Rred)]^0.5}
红边比值植被指数RERVI	Rnir/Rre
垂直植被指数PVI	2.5(Rnir-10.489Rred-6.604)/(1+10.489²)^0.5
红边差异植被指数REDVI	Rnir-Rre
绿波段比值植被指数RVIgreen	Rnir/Rgreen
红边宽动态植被指数REWDRVI	(0.12Rnir-Rre)/(0.12Rnir+Rre)
最佳植被指数VIopt1	100(lnRnir-lnRre)
红边反射植被指数RRE	(Rnir+Rred)/2
红边重归一化植被指数RERDVI	(Rnir-Rre)/(Rnir+Rre)^0.5
归一化绿蓝植被指数GBNDVI	(Rnir-Rgreen-Rblue)/(Rnir+Rgreen+Rblue)
修正红边变换植被指数MRETVI	1.2[1.2(Rnir-Rgreen)-2.5(Rre-Rgreen)]
绿色宽动态植被指数GWDRVI	(0.12Rnir-Rgreen)/(0.12Rnir+Rgreen)
改进归一化差异指数MNDI	(Rnir-Rre)/(Rnir-Rgreen)
DATT	(Rnir-Rre)/(Rnir-Rred)
归一化红边指数NREI	Rre/(Rnir+Rre+Rgreen)
MERIS陆地叶绿素指数MTCI	(Rnir-Rre)/(Rre-Rred)

Table 2

Table 3

Table 4

Table 5

Principal component comprehensive evaluation of the studied wheat cultivars"

品种编号 Breed number	F1	F2	F3	F	品种编号 Breed number	F1	F2	F3	F
C1	-0.317	0.641	0.818	0.142	C62	-0.629	-0.245	-2.503	-0.770
C2	-2.011	1.418	0.208	-0.586	C63	0.888	0.475	-0.904	0.424
C3	-1.072	2.763	-0.114	0.187	C64	-1.093	-0.445	-1.344	-0.871
C4	1.558	-0.820	-0.464	0.480	C65	-0.927	-0.314	-0.024	-0.546
C5	1.171	-0.146	1.237	0.735	C66	-0.711	2.003	-0.301	0.134
C6	-2.373	1.041	0.733	-0.783	C67	2.748	-1.147	-0.506	0.976
C7	-0.432	0.977	0.119	0.065	C68	1.629	-1.093	0.327	0.567
C8	-0.155	-0.042	0.362	-0.031	C69	-0.765	0.460	-0.511	-0.337
C9	-0.006	0.885	0.963	0.384	C70	-1.388	2.118	0.259	-0.083
C10	0.681	-1.102	-0.443	-0.026	C71	-2.323	1.811	-1.120	-0.844
C11	0.582	-0.452	0.013	0.170	C72	-0.038	-0.899	-0.289	-0.303
C12	-0.981	0.873	-0.662	-0.357	C73	1.612	-3.448	-0.519	-0.201
C13	0.483	1.710	-0.767	0.574	C74	-0.942	1.702	-0.580	-0.105
C14	1.751	-0.988	-0.135	0.583	C75	-1.488	-2.072	-1.125	-1.465
C15	2.400	0.508	-0.070	1.313	C76	-0.146	-2.357	0.762	-0.580
C16	-0.458	0.391	-2.511	-0.517	C77	-3.360	0.514	1.877	-1.233
C17	-1.466	1.810	-1.177	-0.429	C78	0.611	1.384	0.871	0.808
C18	-1.272	2.205	-0.894	-0.183	C79	-2.138	0.572	1.842	-0.618
C19	-0.711	0.174	0.728	-0.191	C80	-1.821	-0.316	1.690	-0.721
C20	-0.111	-0.329	0.171	-0.116	C81	0.568	-0.359	0.577	0.277
C21	-0.197	0.598	0.289	0.107	C82	-0.993	0.503	0.883	-0.220
C22	-1.496	0.626	-0.916	-0.718	C83	1.539	1.277	1.063	1.269
C23	2.322	-0.657	0.150	0.998	C84	-3.303	-0.793	0.820	-1.718
C24	-0.843	1.528	0.105	0.006	C85	-2.781	-0.974	0.833	-1.506
C25	1.371	-0.396	-1.048	0.409	C86	-1.936	-1.588	-0.315	-1.431
C26	-1.296	0.084	0.360	-0.563	C87	0.423	0.861	0.680	0.546
C27	-0.666	-0.680	0.398	-0.449	C88	0.865	-0.012	-0.223	0.390
C28	0.492	-0.398	0.740	0.254	C89	0.954	0.778	0.852	0.813
C29	-1.476	-1.032	0.832	-0.875	C90	4.364	0.249	-0.764	2.107
C30	2.381	-1.044	1.470	1.132	C91	0.372	-1.094	-0.488	-0.183
C31	2.202	-1.051	0.719	0.924	C92	0.039	0.394	0.995	0.281
C32	1.328	-0.610	0.730	0.610	C93	-1.180	0.379	1.978	-0.173
C33	0.202	-0.103	0.702	0.183	C94	-2.722	-0.602	1.461	-1.279
C34	0.126	0.049	-0.126	0.056	C95	-1.469	-0.462	0.316	-0.801
C35	-0.181	1.094	0.192	0.231	C96	0.018	-1.265	-0.429	-0.395
C36	1.531	0.562	-0.051	0.900	C97	5.070	2.615	-0.155	3.183
C37	2.610	-1.414	0.361	0.973	C98	-1.095	-0.871	0.252	-0.735
C38	0.981	-2.864	-1.846	-0.566	C99	0.665	-0.939	-0.135	0.058
C39	-2.627	0.864	-2.907	-1.527	C100	0.654	-0.626	-0.461	0.085
C40	0.902	-0.348	0.359	0.411	C101	1.691	0.077	0.114	0.876
C41	-2.718	0.103	-1.911	-1.619	C102	-4.342	-0.949	2.120	-2.070
C42	-0.107	-1.098	-0.821	-0.474	C103	-0.220	0.625	1.729	0.329
C43	-1.018	1.756	-1.385	-0.254	C104	0.945	0.360	0.120	0.583
C44	-0.485	1.925	-0.780	0.149	C105	2.353	1.331	0.326	1.571
C45	0.864	-0.213	-1.090	0.200	C106	-0.243	-2.186	-0.464	-0.775
C46	-1.828	-0.370	0.557	-0.916	C107	0.958	-0.269	0.418	0.469
C47	-0.065	0.660	-1.906	-0.156	C108	-0.102	-2.081	-0.771	-0.726
C48	-0.708	0.061	-0.690	-0.443	C109	3.877	1.811	0.524	2.485
C49	-0.284	2.189	-1.453	0.214	C110	2.431	-0.157	-0.175	1.135
C50	-0.835	0.143	-0.640	-0.476	C111	-2.170	-1.724	-0.457	-1.605
C51	2.213	-1.481	-1.143	0.522	C112	0.232	0.647	0.690	0.395
C52	2.023	0.189	0.169	1.078	C113	-0.654	0.268	0.714	-0.141
C53	-0.903	-0.914	-0.044	-0.697	C114	-0.670	-0.885	0.353	-0.512
C54	-0.106	0.867	0.908	0.321	C115	0.774	-0.621	-0.040	0.212
C55	-0.587	-0.615	-0.016	-0.457	C116	1.676	1.766	1.622	1.555
C56	-1.346	1.678	-0.780	-0.343	C117	5.175	1.677	0.290	3.055
C57	-2.038	1.776	-1.254	-0.734	C118	2.738	2.314	0.319	2.022
C58	0.668	0.214	-0.468	0.314	C119	-0.605	-0.974	0.267	-0.517
C59	3.495	-2.360	0.963	1.254	C120	-2.297	-1.011	0.991	-1.251
C60	-0.806	-2.776	-2.516	-1.533	C121	-2.325	-1.947	0.824	-1.540
C61	0.654	-2.304	0.522	-0.207

Table 5

Table 6

Fig. 4

Fig. 5

Table 7

Table 8

Fig. 6

Fig. 7

Fig. 8

Fig. 9

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项目 Item	产量 Yield （kg·hm^-2）	植株氮积累 Plant nitrogen accumulation（kg·hm^-2）	氮素生理利用效率 Nitrogen physiological utilization efficiency（kg/kg）	植株干生物量 Dry plant weight （kg·hm^-2）	籽粒总吸氮量 Total nitrogen uptake amount of grain （kg·hm^-2）	N收获指数 N harvest index（kg/kg）
最小值 Minimum	3747.263	92.987	22.697	4690.196	79.300	0.549
最大值 Maximum	10214.400	281.304	56.771	19581.582	217.876	0.905
标准差 SD	947.395	35.408	6.998	2643.563	23.819	0.067
平均值 Mean	6477.528	170.242	39.075	12368.062	136.316	0.763
变异系数 CV (%)	14.626	20.799	17.910	21.374	17.474	8.754

主成分 Principal component	载荷值 Load value						特征值 Eigen value	贡献率 Contribution rate (%)	累计贡献率 Cumulative contribution rate (%)
主成分 Principal component	产量 Yield	植株氮积累量 Plant nitrogen accumulation	氮素生理利用效率 Nitrogen physiological utilization efficiency	植株干生物量 Dry plant weight	籽粒总吸氮量 Total nitrogen uptake amount of grain	N收获指数 N harvest index	特征值 Eigen value	贡献率 Contribution rate (%)	累计贡献率 Cumulative contribution rate (%)
F1	0.063	0.860	-0.937	0.780	0.179	-0.001	2.262	37.7	37.7
F2	0.975	0.465	0.263	0.409	0.911	0.104	2.244	37.4	75.1
F3	0.049	0.020	-0.012	-0.022	0.124	0.993	1.006	16.8	91.9

类别 Classes	品种编号 Breed number	聚类中心 Clustering center
氮低效型 Low nitrogen efficiency type	C2、C6、C12、C16、C17、C22、C26、C27、C29、C38、C39、C41、C42、C46、C48、C50、C53、C55、C56、C57、C60、C62、C64、C65、C69、C71、C72、C75、C76、C77、C79、C80、C84、C85、C86、C94、C95、C96、C98、C102、C106、C108、C111、C114、C119、C120、C121	-0.856
氮中效型 Medium nitrogen efficiency type	C1、C3、C4、C5、C7、C8、C9、C10、C11、C13、C14、C18、C19、C20、C21、C23、C24、C25、C28、C31、C32、C33、C34、C35、C36、C37、C40、C43、C44、C45、C47、C49、C51、C54、C58、C61、C63、C66、C67、C68、C70、C73、C74、C78、C81、C82、C87、C88、C89、C91、C92、C93、C99、C100、C101、C103、C104、C107、C112、C113、C115	0.280
氮高效型 High nitrogen efficiency type	C15、C30、C52、C59、C83、C90、C97、C105、C109、C110、C116、C117、C118	1.782

生育时期 Growth stage	KNN		RF		SVM		平均值 Mean
生育时期 Growth stage	OA（%）	Kappa	OA（%）	Kappa	OA（%）	Kappa	OA（%）	Kappa
S1	55.8	0.154	62.4	0.358	65.6	0.406	61.3	0.306
S2	65.2	0.354	68.3	0.454	72.1	0.511	68.6	0.440
S3	66.3	0.380	72.2	0.510	77.1	0.591	71.9	0.493
平均值 Mean	62.4	0.296	67.6	0.441	71.6	0.503

生育时期 Growth stage	KNN		RF		SVM		平均值 Mean
生育时期 Growth stage	OA（%）	Kappa	OA（%）	Kappa	OA（%）	Kappa	OA（%）	Kappa
S1+S2	65.2	0.352	72.3	0.523	74.4	0.557	70.6	0.477
S1+S3	66.7	0.387	74.7	0.565	77.1	0.609	72.8	0.521
S2+S3	68.1	0.414	76.8	0.592	78.0	0.620	74.3	0.542
S1+S2+S3	70.3	0.451	79.3	0.632	80.6	0.669	76.7	0.584
平均值 Mean	67.6	0.401	75.8	0.578	77.5	0.614

Classification and Identification of Nitrogen Efficiency of Wheat Varieties Based on UAV Multi-Temporal Images

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