基于影像分割的覆膜玉米叶绿素含量反演

doi:10.3864/j.issn.0578-1752.2024.06.004

Abstract

Abstract:

【Objective】 In order to quickly and accurately monitor chlorophyll content of film-mulched maize, explore whether the removal of film and shadow background pixels can improve the accuracy of chlorophyll content inversion with spectral and texture features.【Method】 This study was based on multi-spectral remote sensing image data of unmanned aerial vehicle (UAV) and took chlorophyll content of film-mulched maize at seedling stage, jointing stage, tasseling stage and filling stage as objects. The support vector machine supervised classification was used to segment image background pixels and maize pixels, analyze the influence of background pixels on the spectra of maize canopy, the vegetation index and texture features of all pixels and maize pixels images were calculated and the better variable input was screened, and the inversion model of leaf chlorophyll content was established by using three machine learning algorithms, partial least squares, support vector machine and BP neural network.【Result】 (1) Background pixels in the multispectral images at seedling stage, jointing stage, tasseling stage and filling stage had significant effects on the spectra of maize canopy. (2) The inversion accuracy of vegetation index, texture feature and vegetation index + texture feature as variable input based on maize pixels image extraction was better than that of all pixels image (R² for optimal model was increased by 0.078, RMSE and MAE were decreased by 0.060 and 0.055 mg·g^-1, respectively, and R² for verification was increased by 0.109, RMSE and MAE were reduced by 0.075 and 0.047 mg·g^-1, respectively. (3) The modeling accuracy based on maize pixels image with spectral features + texture features as variable inputs was significantly improved over the modeling accuracy using only spectral features or texture features as variable inputs; The BP neural network model with spectral features + texture features as variable inputs had the highest accuracy (R², RMSE and MAE were 0.690, 0.468 mg·g^-1 and 0.375 mg·g^-1, respectively).【Conclusion】 The multispectral image spectral and texture feature data of UAV with removing background pixels and combined with BP neural network can better realize the inversion of chlorophyll content of film-mulched maize. The results can provide theoretical reference for quick and accurate retrieval of leaf chlorophyll content of film-mulched maize by UAV remote sensing.

Key words: UAV multispectral, image segmentation, chlorophyll content, film-mulching, texture feature

ZHOU ZhiHui, GU XiaoBo, CHENG ZhiKai, CHANG Tian, ZHAO TongTong, WANG YuMing, DU YaDan. Inversion of Chlorophyll Content of Film-Mulched Maize Based on Image Segmentation[J].Scientia Agricultura Sinica, 2024, 57(6): 1066-1079.

0
/ / Recommend

Add to citation manager EndNote|Reference Manager|ProCite|BibTeX|RefWorks

URL: https://www.chinaagrisci.com/EN/10.3864/j.issn.0578-1752.2024.06.004

https://www.chinaagrisci.com/EN/Y2024/V57/I6/1066

Figures/Tables 14

Fig. 1

Table 1

Table 2

Fig. 2

Fig. 3

Table 3

Table 4

Fig. 4

Fig. 5

Table 5

Table 6

Table 7

Fig. 6

Fig. 7

References 46

[1]	薛颖昊, 周涛, 靳拓, 徐志宇. 农用地膜污染合作治理模式及其绩效损失研究. 中国农业资源与区划, 2022, 43(11): 39-50.
	XUE Y H, ZHOU T, JIN T, XU Z Y. Research on the cooperative governance model and performance loss of agricultural plastic film recovery. Chinese Journal of Agricultural Resources and Regional Planning, 2022, 43(11): 39-50. (in Chinese)
[2]	张金瑞, 任思洋, 戴吉照, 丁凡, 肖谋良, 刘学军, 严昌荣, 葛体达, 汪景宽, 刘勤, 王锴, 张福锁. 地膜对农业生产的影响及其污染控制. 中国农业科学, 2022, 55(20): 3983-3996. doi: 10.3864/j.issn.0578-1752.2022.20.010.
	ZHANG J R, REN S Y, DAI J Z, DING F, XIAO M L, LIU X J, YAN C R, GE T D, WANG J K, LIU Q, WANG K, ZHANG F S. Influence of plastic film on agricultural production and its pollution control. Scientia Agricultura Sinica, 2022, 55(20): 3983-3996. doi: 10.3864/j.issn.0578-1752.2022.20.010. (in Chinese)
[3]	方恒, 李援农, 谷晓博, 银敏华, 杨金宇. 覆膜与施氮组合下夏玉米籽粒灌浆过程拟合分析. 农业机械学报, 2018, 49(8): 245-252.
	FANG H, LI Y N, GU X B, YIN M H, YANG J Y. Fitting and analysis of grain filling of summer maize under combination of film mulching and nitrogen fertilizer. Transactions of the Chinese Society for Agricultural Machinery, 2018, 49(8): 245-252. (in Chinese)
[4]	周昌明, 李援农, 银敏华, 谷晓博, 赵玺. 连垄全覆盖降解膜集雨种植促进玉米根系生长提高产量. 农业工程学报, 2015, 31(7): 109-117.
	ZHOU C M, LI Y N, YIN M H, GU X B, ZHAO X. Ridge-furrow planting with biodegradable film mulching over ridges for rain harvesting improving root growth and yield of maize. Transactions of the Chinese Society of Agricultural Engineering, 2015, 31(7): 109-117. (in Chinese)
[5]	QIAO L, ZHAO R, TANG W, AN L, SUN H, LI M, WANG N, LIU Y, LIU G. Estimating maize LAI by exploring deep features of vegetation index map from UAV multispectral images. Field Crops Research, 2022, 289: 108739. doi: 10.1016/j.fcr.2022.108739
[6]	苏伟, 王伟, 刘哲, 张明政, 边大红, 崔彦宏, 黄健熙. 无人机影像反演玉米冠层LAI和叶绿素含量的参数确定. 农业工程学报, 2020, 36(19): 58-65.
	SU W, WANG W, LIU Z, ZHANG M Z, BIAN D H, CUI Y H, HUANG J X. Determining the retrieving parameters of corn canopy LAI and chlorophyll content computed using UAV image. Transactions of the Chinese Society of Agricultural Engineering, 2020, 36(19): 58-65. (in Chinese)
[7]	牛庆林, 冯海宽, 周新国, 朱建强, 雍蓓蓓, 李会贞. 冬小麦SPAD值无人机可见光和多光谱植被指数结合估算. 农业机械学报, 2021, 52(8): 183-194.
	NIU Q L, FENG H K, ZHOU X G, ZHU J Q, YONG B B, LI H Z. Combining UAV visible light and multispectral vegetation indices for estimating SPAD value of winter wheat. Transactions of the Chinese Society for Agricultural Machinery, 2021, 52(8): 183-194. (in Chinese)
[8]	HASITUYA, CHEN Z, WANG L, WU W B, JIANG Z W, LI H. Monitoring plastic-mulched farmland by Landsat-8 OLI imagery using spectral and textural features. Remote Sensing, 2016, 8(4): 353. doi: 10.3390/rs8040353
[9]	孟庆兰, 高军凯, 卢筱茜, 常学礼. 覆膜种植对玉米生长初期NDVI识别的影响. 生态学杂志, 2016, 35(7): 1761-1766.
	MENG Q L, GAO J K, LU X Q, CHANG X L. Impacts of film mulch planting on NDVI identification in corn early growth. Chinese Journal of Ecology, 2016, 35(7): 1761-1766. (in Chinese)
[10]	姚志华, 陈俊英, 张智韬, 谭丞轩, 魏广飞, 王新涛. 覆膜对无人机多光谱遥感反演土壤含盐量精度的影响. 农业工程学报, 2019, 35(19): 89-97.
	YAO Z H, CHEN J Y, ZHANG Z T, TAN C X, WEI G F, WANG X T. Effect of plastic film mulching on soil salinity inversion by using UAV multispectral remote sensing. Transactions of the Chinese Society of Agricultural Engineering, 2019, 35(19): 89-97. (in Chinese)
[11]	黄茜, 杨伟才, 魏夏永, 毛晓敏. 不同覆膜处理下春玉米叶面积指数高光谱估算. 农业机械学报, 2021, 52(7): 184-194.
	HUANG X, YANG W C, WEI X Y, MAO X M. Hyperspectral estimation of leaf area index of spring maize under different film mulching treatments. Transactions of the Chinese Society for Agricultural Machinery, 2021, 52(7): 184-194. (in Chinese)
[12]	HUANG X, GUAN H, BO L, XU Z, MAO X. Hyperspectral proximal sensing of leaf chlorophyll content of spring maize based on a hybrid of physically based modelling and ensemble stacking. Computers and Electronics in Agriculture, 2023, 208: 107745. doi: 10.1016/j.compag.2023.107745
[13]	魏夏永, 黄茜, 薄丽媛, 毛晓敏. 西北旱区不同覆膜和灌溉水平下的玉米冠层氮含量垂直分布及高光谱反演. 中国农业大学学报, 2022, 27(11): 13-21.
	WEI X Y, HUANG X, BO L Y, MAO X M. Vertical distribution of nitrogen content in spring maize leaves and its hyperspectral inversion under different film mulching and irrigation levels in northwest arid region. Journal of China Agricultural University, 2022, 27(11): 13-21. (in Chinese)
[14]	JAY S, BARET F, DUTARTRE D, MALATESTA G, HÉNO S, COMAR A, WEISS M, MAUPAS F. Exploiting the centimeter resolution of UAV multispectral imagery to improve remote-sensing estimates of canopy structure and biochemistry in sugar beet crops. Remote Sensing of Environment, 2019, 231: 110898. doi: 10.1016/j.rse.2018.09.011
[15]	邓尚奇, 赵钰, 白雪源, 李旭, 孙振东, 梁健, 李振海, 成枢. 基于无人机图像分割的冬小麦叶绿素与叶面积指数反演. 农业工程学报, 2022, 38(3): 136-145.
	DENG S Q, ZHAO Y, BAI X Y, LI X, SUN Z D, LIANG J, LI Z H, CHENG S. Inversion of chlorophyll and leaf area index for winter wheat based on UAV image segmentation. Transactions of the Chinese Society of Agricultural Engineering, 2022, 38(3): 136-145. (in Chinese)
[16]	CHEN P, WANG F. Effect of crop spectra purification on plant nitrogen concentration estimations performed using high-spatial- resolution images obtained with unmanned aerial vehicles. Field Crops Research, 2022, 288: 108708. doi: 10.1016/j.fcr.2022.108708
[17]	刘畅, 杨贵军, 李振海, 汤伏全, 王建雯, 张春兰, 张丽妍. 融合无人机光谱信息与纹理信息的冬小麦生物量估测. 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, TANG F Q, WANG J W, ZHANG C L, ZHANG L Y. Biomass estimation in winter wheat by UAV spectral information and texture information fusion. Scientia Agricultura Sinica, 2018, 51(16): 3060-3073. doi: 10.3864/j.issn.0578-1752.2018.16.003. (in Chinese)
[18]	ZHENG H, CHENG T, ZHOU M, LI D, YAO X, TIAN Y, CAO W, ZHU Y. Improved estimation of rice aboveground biomass combining textural and spectral analysis of UAV imagery. Precision Agriculture, 2019, 20: 611-629. doi: 10.1007/s11119-018-9600-7
[19]	陈鹏, 冯海宽, 李长春, 杨贵军, 杨钧森, 杨文攀, 刘帅兵. 无人机影像光谱和纹理融合信息估算马铃薯叶片叶绿素含量. 农业工程学报, 2019, 35(11): 63-74.
	CHEN P, FENG H K, LI C C, YANG G J, YANG J S, YANG W P, LIU S B. Estimation of chlorophyll content in potato using fusion of texture and spectral features derived from UAV multispectral image. Transactions of the Chinese Society of Agricultural Engineering, 2019, 35(11): 63-74. (in Chinese)
[20]	王爱玉, 张春庆, 吴承来, 高明伟. 玉米叶绿素含量快速测定方法研究. 玉米科学, 2008(2): 97-100.
	WANG A Y, ZHANG C Q, WU C L, GAO M W. Study on a fast method of testing chlorophyll content in maize. Journal of Maize Sciences, 2008(2): 97-100. (in Chinese)
[21]	WANG Y, TAN S, JIA X, QI L, LIU S, LU H, WANG C, LIU W, ZHAO X, HE L, CHEN J, YANG C, WANG X, CHEN J, QIN Y, YU J, MA X. Estimating relative chlorophyll content in rice leaves using unmanned aerial vehicle multi-spectral images and spectral-textural analysis. Agronomy, 2023, 13(6): 1541. doi: 10.3390/agronomy13061541
[22]	ROUSE J W, HAAS R H, SCHELL J A, DEERING D W, HARLAN J C. Monitoring the Vernal Advancement of Retrogradation (Green Wave Effect) of Natural Vegetation. Texas A&M University: Remote Sensing Center, 1974.
[23]	GITELSON A A, KEYDAN G P, MERZLYAK M N. Three-band model for noninvasive estimation of chlorophyll, carotenoids, and anthocyanin contents in higher plant leaves. Geophysical Research Letters, 2006, 33(11): 431-433.
[24]	GITELSON A A, KAUFMAN Y J, MERZLYAK M N. Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sensing of Environment, 1996, 58(3): 289-298. doi: 10.1016/S0034-4257(96)00072-7
[25]	HUETE A R, HUA G, QI J, CHEHBOUNI A G, LEEUWEN W J. Normalization of multidirectional red and NIR reflectances with the SAVI. Remote Sensing of Environment, 1992, 41(2/3): 143-154. doi: 10.1016/0034-4257(92)90074-T
[26]	RONDEAUX G, STEVEN M, BARET F. Optimization of soil- adjusted vegetation indices. Remote Sensing of Environment, 1996, 55(2): 95-107. doi: 10.1016/0034-4257(95)00186-7
[27]	WU C Y, NIU Z, TANG Q, HUANG W J. Estimating chlorophyll content from hyperspectral vegetation indices: Modeling and validation. Agricultural and Forest Meteorology, 2008, 148(8/9): 1230-1241. doi: 10.1016/j.agrformet.2008.03.005
[28]	DASH J, CURRAN P J. The MERIS terrestrial chlorophyll index. International Journal of Remote Sensing, 2004, 25(23): 5403-5413. doi: 10.1080/0143116042000274015
[29]	FITZGERALD G J, RODRIGUEZ D, CHRISTENSEN L K, BELFORD R, SADRAS V O, CLARKE T R. Spectral and thermal sensing for nitrogen and water status in rainfed and irrigated wheat environments. Precision Agriculture, 2006, 7(4): 233-248. doi: 10.1007/s11119-006-9011-z
[30]	CHEN J M. Evaluation of vegetation indices and a modified simple ratio for boreal applications. Canadian Journal of Remote Sensing, 1996, 22(3): 229-242. doi: 10.1080/07038992.1996.10855178
[31]	谭丞轩, 张智韬, 许崇豪, 马宇, 姚志华, 魏广飞, 李宇. 无人机多光谱遥感反演各生育期玉米根域土壤含水率. 农业工程学报, 2020, 36(10): 63-74.
	TAN C X, ZHANG Z T, XU C H, MA Y, YAO Z H, WEI G F, LI Y. Soil water content inversion model in field maize root zone based on UAV multispectral remote sensing. Transactions of the Chinese Society of Agricultural Engineering, 2020, 36(10): 63-74. (in Chinese)
[32]	李美炫, 朱西存, 白雪源, 彭玉凤, 田中宇, 姜远茂. 基于无人机影像阴影去除的苹果树冠层氮素含量遥感反演. 中国农业科学, 2021, 54(10): 2084-2094. doi: 10.3864/j.issn.0578-1752.2021.10.005.
	LI M X, ZHU X C, BAI X Y, PENG Y F, TIAN Z Y, JIANG Y M. Remote sensing inversion of nitrogen content in apple canopy based on shadow removal in UAV multi-spectral remote sensing images. Scientia Agricultura Sinica, 2021, 54(10): 2084-2094. doi: 10.3864/j.issn.0578-1752.2021.10.005. (in Chinese)
[33]	YUE J, YANG G, TIAN Q, FENG H, XU K, ZHOU C. Estimate of winter-wheat above-ground biomass based on UAV ultrahigh-ground- resolution image textures and vegetation indices. ISPRS Journal of Photogrammetry and Remote Sensing, 2019, 150: 226-244. doi: 10.1016/j.isprsjprs.2019.02.022
[34]	QIAO L, GAO D, ZHANG J, LI M, SUN H, MA J. Dynamic influence elimination and chlorophyll content diagnosis of maize using UAV spectral imagery. Remote Sensing, 2020, 12(16): 2650. doi: 10.3390/rs12162650
[35]	谷晓博, 程智楷, 周智辉, 常甜, 李汶龙, 杜娅丹. 基于特征降维和机器学习的覆膜冬小麦LAI遥感反演. 农业机械学报, 2023, 54(6): 148-157, 167.
	GU X B, CHENG Z K, ZHOU Z H, CHANG T, LI W L, DU Y D. Remote sensing inversion of leaf area index of mulched winter wheat based on feature downscaling and machine learning. Transactions of the Chinese Society for Agricultural Machinery, 2023, 54(6): 148-157, 167. (in Chinese)
[36]	FU H, LU J, CHEN J, WANG W, CUI G, SHE W. Influence of structure and texture feature on retrieval of ramie leaf area index. Agronomy, 2023, 13(7): 1690. doi: 10.3390/agronomy13071690
[37]	DUBE T, MUTANGA O. Investigating the robustness of the new Landsat-8 operational land imager derived texture metrics in estimating plantation forest aboveground biomass in resource constrained areas. ISPRS Journal of Photogrammetry and Remote Sensing, 2015, 108: 12-32. doi: 10.1016/j.isprsjprs.2015.06.002
[38]	罗小波, 谢天授, 董圣贤. 基于无人机多光谱影像的柑橘冠层叶绿素含量反演. 农业机械学报, 2023, 54(4): 198-205.
	LUO X B, XIE T S, DONG S X. Estimation of citrus canopy chlorophyll based on UAV multispectral images. Transactions of the Chinese Society for Agricultural Machinery, 2023, 54(4): 198-205. (in Chinese)
[39]	张东彦, 韩宣宣, 林芬芳, 杜世州, 张淦, 洪琪. 基于多源无人机影像特征融合的冬小麦LAI估算. 农业工程学报, 2022, 38(9): 171-179.
	ZHANG D Y, HAN X X, LIN F F, DU S Z, ZHANG G, HONG Q. Estimation of winter wheat leaf area index using multi-source UAV image feature fusion. Transactions of the Chinese Society of Agricultural Engineering, 2022, 38(9): 171-179. (in Chinese)
[40]	田军仓, 杨振峰, 冯克鹏, 丁新军. 基于无人机多光谱影像的番茄冠层SPAD预测研究. 农业机械学报, 2020, 51(8): 178-188.
	TIAN J C, YANG Z F, FENG K P, DING X J. Prediction of tomato canopy SPAD based on UAV multispectral image. Transactions of the Chinese Society for Agricultural Machinery, 2020, 51(8): 178-188. (in Chinese)
[41]	张春兰, 杨贵军, 李贺丽, 汤伏全, 刘畅, 张丽妍. 基于随机森林算法的冬小麦叶面积指数遥感反演研究. 中国农业科学, 2018, 51(5): 855-867. doi: 10.3864/j.issn.0578-1752.2018.05.005.
	ZHANG C L, YANG G J, LI H L, TANG F Q, LIU C, ZHANG L Y. Remote sensing inversion of leaf area index of winter wheat based on random forest algorithm. Scientia Agricultura Sinica, 2018, 51(5): 855-867. doi: 10.3864/j.issn.0578-1752.2018.05.005. (in Chinese)
[42]	李媛媛, 常庆瑞, 刘秀英, 严林, 罗丹, 王烁. 基于高光谱和BP神经网络的玉米叶片SPAD值遥感估算. 农业工程学报, 2016, 32(16): 135-142.
	LI Y Y, CHANG Q R, LIU X Y, YAN L, LUO D, WANG S. Estimation of maize leaf SPAD value based on hyperspectrum and BP neural network. Transactions of the Chinese Society of Agricultural Engineering, 2016, 32(16): 135-142. (in Chinese)
[43]	QI H, WU Z, ZHANG L, LI J, ZHOU J, JUN Z, ZHU B. Monitoring of peanut leaves chlorophyll content based on drone-based multispectral image feature extraction. Computers and Electronics in Agriculture, 2021, 187: 106292. doi: 10.1016/j.compag.2021.106292
[44]	BAI Y, ZHANG S, BHATTARAI N, MALLICK K, LIU Q, TANG L, IM J, GUO L, ZHANG J. On the use of machine learning based ensemble approaches to improve evapotranspiration estimates from croplands across a wide environmental gradient. Agricultural and Forest Meteorology, 2021, 298/299: 108308. doi: 10.1016/j.agrformet.2020.108308
[45]	VON BLOH M, NÓIA JÚNIOR R, WANGERPOHL X, SALTIK A O, HALLER V, KAISER L, ASSENG S. Machine learning for soybean yield forecasting in Brazil. Agricultural and Forest Meteorology, 2023, 341: 109670. doi: 10.1016/j.agrformet.2023.109670
[46]	费帅鹏, 禹小龙, 兰铭, 李雷, 夏先春, 何中虎, 肖永贵. 基于高光谱遥感和集成学习方法的冬小麦产量估测研究. 中国农业科学, 2021, 54(16): 3417-3427. doi: 10.3864/j.issn.0578-1752.2021.16.005.
	FEI S P, YU X L, LAN M, LI L, XIA X C, HE Z H, XIAO Y G. Research on winter wheat yield estimation based on hyperspectral remote sensing and ensemble learning method. Scientia Agricultura Sinica, 2021, 54(16): 3417-3427. doi: 10.3864/j.issn.0578-1752.2021.16.005. (in Chinese)

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed

Comments

Recommended 0

No Suggested Reading articles found!

光谱特征 Spectral feature	公式 Formula	文献 Reference	光谱特征 Spectral feature	公式 Formula	文献 Reference
B	B=R450		GNDVI	GNDVI=(R840-R560)/(R840+R560)	[24]
G	G=R560		SAVI	SAVI=(1+0.5)×(R840-R650)/(R840+R650+0.5)	[25]
R	R=R650		OSAVI	OSAVI=(R840-R650)/(R840+R650+0.16)	[26]
RE	RE=R730		MCARI	MCARI=[(R730-R650) -0.2×(R730-R560)]×(R730/R650)	[27]
NIR	NIR (near infrared)=R840		MTCI	MTCI=(R840-R730)/(R730-R650)	[28]
NDVI	NDVI=(R840-R650)/(R840+R650)	[22]	NDRE	NDRE=(R840-R730)/(R840+R730)	[29]
CIgreen	CIgreen=R840/R560-1	[23]	MSR	MSR=(R840/R650-1)/(R840/R650+1)^0.5	[30]
CIre	CIre=R840/R730-1	[23]

生育期 Growth stage	总体精度 Overall accuracy (%)	Kappa系数 Kappa coefficient
苗期Seedling stage	99.60	0.994
拔节期Jointing stage	97.99	0.967
抽雄期Tasseling stage	98.78	0.981
灌浆期Filling stage	98.15	0.971

数据集 Dataset	样本量 Sample size	最小值 Minimum (mg·g^-1)	最大值 Maximum (mg·g^-1)	平均值 Average (mg·g^-1)	标准差 Standard deviation (mg·g^-1)	方差 Variance	变异系数Coefficient of variation (%)
样本集 Sample set	144	2.07	5.50	3.69	0.84	0.71	22.78
建模集 Modeling set	108	2.07	5.48	3.68	0.84	0.71	22.92
验证集 Validation set	36	2.25	5.50	3.73	0.84	0.71	22.66

生育期 Growth stage	B	G	R	RE	NIR
苗期Seedling stage	**	**	**	**	**
拔节期Jointing stage	**	**	**	*	**
抽雄期Tasseling stage	*	**	ns	**	**
灌浆期Filling stage	*	**	ns	**	**
全生育期Whole growth stages	**	**	*	**	**

纹理特征 Texture feature	相关系数Correlation coefficient
	全像元All pixels					玉米像元Maize pixels
	B	G	R	RE	NIR	B	G	R	RE	NIR
Mean	0.08ns	0.06ns	0.10ns	0.28**	0.54**	0.52**	0.43**	0.63**	0.25**	0.71**
Var	0.45**	0.62**	0.35**	0.47**	0.58**	0.40**	0.19*	0.38**	0.63**	0.67**
Hom	0.27**	0.28**	0.24**	0.37**	0.30**	0.34**	0.20*	0.52**	0.44**	0.37**
Con	0.46**	0.62**	0.36**	0.50**	0.55**	0.36**	0.15ns	0.36**	0.62**	0.67**
Dis	0.19*	0.28**	0.19*	0.19*	0.51**	0.37**	0.16*	0.45**	0.59**	0.59**
Ent	0.29**	0.29**	0.28**	0.34**	0.32**	0.30**	0.23**	0.52**	0.27**	0.27**
Sec	0.32**	0.32**	0.31**	0.34**	0.33**	0.32**	0.28**	0.53**	0.15ns	0.23**
Corr	0.15ns	0.09ns	0.15ns	0.11ns	0.16*	0.52**	0.37**	0.64**	0.27**	0.26**

Inversion of Chlorophyll Content of Film-Mulched Maize Based on Image Segmentation

RichHTML

PDF

Abstract

Cite this article

share this article

Figures/Tables 14

References 46

Related Articles 15

Metrics

Comments

Recommended 0

影像类型 Image type	变量输入 Variable input	筛选结果 <BOLD>F</BOLD>iltering result	R²_adj	BIC
全像元 All pixels	SF	B NIR NDRE MSR MCARI	0.59	-105
	TF	Mean_B Con_G Diss_G Sec_R Mean_RE Var_RE Sec_NIR	0.63	-100
	SF+TF	Mean_B Con_G Diss_G Sec_R Mean_RE Var_RE Sec_NIR	0.63	-100
玉米像元 Maize pixels	SF	B RE GNDVI NDRE MCARI	0.59	-103
	TF	Hom_B Con_B Diss_B Corr_B Diss_R Sec_R Entropy_RE Sec_RE	0.66	-120
	SF+TF	NDRE Hom_B Con_B Diss_B Var_G Var_R Diss_R Diss_NIR	0.69	-132

模型Model	影像类型 Image type	变量输入 Variable input	建模Modeling			验证<BOLD>V</BOLD>alidation
模型Model	影像类型 Image type	变量输入 Variable input	R²	RMSE (mg·g^-1)	MAE (mg·g^-1)	R²	RMSE (mg·g^-1)	MAE (mg·g^-1)
PLS	玉米像元 Maize pixels	SF	0.606	0.527	0.431	0.538	0.568	0.463
		TF	0.663	0.488	0.405	0.582	0.547	0.442
		SF+TF	0.660	0.490	0.408	0.611	0.523	0.411
	全像元 All pixels	SF	0.589	0.539	0.441	0.515	0.582	0.491
	全像元 All pixels	TF（SF+TF）	0.609	0.526	0.440	0.526	0.574	0.483
SVM	玉米像元 Maize pixels	SF	0.622	0.517	0.422	0.563	0.554	0.455
		TF	0.635	0.508	0.422	0.577	0.542	0.432
		SF+TF	0.662	0.488	0.403	0.634	0.506	0.376
	全像元 All pixels	SF	0.621	0.517	0.423	0.532	0.573	0.462
	全像元 All pixels	TF（SF+TF）	0.630	0.518	0.409	0.540	0.568	0.478
BPNN	玉米像元 Maize pixels	SF	0.664	0.487	0.388	0.613	0.519	0.436
		TF	0.723	0.442	0.363	0.666	0.482	0.375
		SF+TF	0.750	0.421	0.341	0.690	0.468	0.375
	全像元 All pixels	SF	0.618	0.520	0.425	0.572	0.550	0.454
	全像元 All pixels	TF（SF+TF）	0.672	0.481	0.396	0.581	0.543	0.422

[1]	LI FaJi, CHENG DunGong, YU XiaoCong, WEN WeiE, LIU JinDong, ZHAI ShengNan, LIU AiFeng, GUO Jun, CAO XinYou, LIU Cheng, SONG JianMin, LIU JianJun, LI HaoSheng. Genome-Wide Association Studies for Canopy Activity Related Traits and Its Genetic Effects on Yield-Related Traits [J]. Scientia Agricultura Sinica, 2024, 57(4): 627-637.
[2]	WANG WeiKang, ZHANG JiaYi, WANG Hui, CAO Qiang, TIAN YongChao, ZHU Yan, CAO WeiXing, LIU XiaoJun. Non-Destructive Monitoring of Rice Growth Key Indicators Based on Fixed-Wing UAV Multispectral Images [J]. Scientia Agricultura Sinica, 2023, 56(21): 4175-4191.
[3]	CAI WeiDi,ZHANG Yu,LIU HaiYan,ZHENG HengBiao,CHENG Tao,TIAN YongChao,ZHU Yan,CAO WeiXing,YAO Xia. Early Detection on Wheat Canopy Powdery Mildew with Hyperspectral Imaging [J]. Scientia Agricultura Sinica, 2022, 55(6): 1110-1126.
[4]	ZHAO Ling, ZHANG Yong, WEI XiaoDong, LIANG WenHua, ZHAO ChunFang, ZHOU LiHui, YAO Shu, WANG CaiLin, ZHANG YaDong. Mapping of QTLs for Chlorophyll Content in Flag Leaves of Rice on High-Density Bin Map [J]. Scientia Agricultura Sinica, 2022, 55(5): 825-836.
[5]	HaiYu TAO,AiWu ZHANG,HaiYang PANG,XiaoYan KANG. Smart-Phone Application in Situ Grassland Biomass Estimation [J]. Scientia Agricultura Sinica, 2021, 54(5): 933-944.
[6]	YinHua MA,KaiQin MO,Lu LIU,PingFang LI,ChenZhong JIN,Fang YANG. Effect of Overexpression of OsRRK1 Gene on Rice Leaf Development [J]. Scientia Agricultura Sinica, 2021, 54(5): 877-886.
[7]	ZHANG Wen,MENG ShuJun,WANG QiYue,WAN Jiong,MA ShuanHong,LIN Yuan,DING Dong,TANG JiHua. Transcriptome Analysis of Maize pTAC2 Effects on Chlorophyll Synthesis in Seedling Leaves [J]. Scientia Agricultura Sinica, 2020, 53(5): 874-889.
[8]	SHI YaJiao,CHEN PengFei. A Method for the Automatic Determination of Scale Parameter During Segmenting Agricultural Drone Images [J]. Scientia Agricultura Sinica, 2020, 53(17): 3496-3508.
[9]	CHEN PengFei, LIANG Fei. Cotton Nitrogen Nutrition Diagnosis Based on Spectrum and Texture Feature of Images from Low Altitude Unmanned Aerial Vehicle [J]. Scientia Agricultura Sinica, 2019, 52(13): 2220-2229.
[10]	SHI DaKun, YAO TianLong, LIU NanNan, DENG Min, DUAN HaiYang, WANG LuLin, WAN Jiong, GAO JiongHao, XIE HuiLing, TANG JiHua, ZHANG XueHai. Genome-Wide Association Study of Chlorophyll Content in Maize [J]. Scientia Agricultura Sinica, 2019, 52(11): 1839-1857.
[11]	LI WenTao, YANG JiangBo, ZHANG Ji, WANG KeJian, DENG Lie, Lü Qiang, HE ShaoLan, XIE RangJin, ZHENG YongQiang, MA YanYan, YI ShiLai. The Evaluation of Chlorophyll Content Detection in the Leaves of Newhall Navel Orange Based on Different Sensors [J]. Scientia Agricultura Sinica, 2018, 51(6): 1057-1066.
[12]	LIU Chang, YANG GuiJun, LI ZhenHai, TANG FuQuan, WANG JianWen, ZHANG ChunLan, ZHANG LiYan. Biomass Estimation in Winter Wheat by UAV Spectral Information and Texture Information Fusion [J]. Scientia Agricultura Sinica, 2018, 51(16): 3060-3073.
[13]	FANG Xian-Yi, ZHU Xi-Cun, WANG Ling, ZHAO Geng-Xing. Hyperspectral Monitoring of the Canopy Chlorophyll Content at Apple Tree Prosperous Fruit Stage [J]. Scientia Agricultura Sinica, 2013, 46(16): 3504-3513.
[14]	ZHAO Ming-hui,SUN Jian,WANG Jia-yu,XU Hai,TANG Liang,CHEN Wen-fu . Global Genome Expression Analysis of Photosynthesis-Related Genes Under Low Nitrogen Stress in Rice Flag Leaf [J]. Scientia Agricultura Sinica, 2011, 44(1): 1-8 .
[15]	. Maize Disease Identifying System Based Image Recognition [J]. Scientia Agricultura Sinica, 2007, 40(4): 698-703 .