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水稻冠层叶瘟病与缺氮的高光谱识别
利用水稻大田近地冠层高光谱数据，实现在冠层尺度上对叶瘟病与缺氮水稻的早期识别。
基于氮胁迫梯度试验和大田自然发病，采用室外高光谱成像系统采集缺氮水稻和自然发病水稻的大田近地冠层图像，提取并分析中度感病、轻度感病、缺氮和健康水稻冠层的反射率光谱特征。针对预处理后的高光谱数据，采用主成分分析(principal component analysis，PCA)、植被指数(vegetation index，VI)和竞争性自适应重加权法(competitive adaptive reweighted sampling，CARS) 3种方法提取特征变量，并结合线性判别分析(linear discriminant analysis，LDA)与支持向量机(support vector machine，SVM)分类算法，构建水稻冠层叶瘟病与缺氮的早期识别模型。
单独使用PCA降维方法提取特征构建的模型无显著的分类效果；基于VI的模型相比全谱模型在区分效果上有所提升，但提升幅度不大；采用CARS提取特征波长所构建的模型显示出最佳的区分效果。进一步对提取的CARS特征进行PCA降维后，获得5个主成分特征用于建模，其SVM和LDA模型的总体分类精度分别为95.51%和96.15%，Kappa系数分别为0.91和0.92，表现出较高的分类一致性。
本研究通过选用较少的特征变量，成功实现了水稻冠层叶瘟病与缺氮的有效识别，为水稻病害与养分胁迫的遥感监测提供了新的思路和理论依据。研究成果可为大规模水稻病虫害监测与精准施药提供支持，在精准农业领域具有重要的应用价值。
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关键词:
- 高光谱成像 /
- 水稻叶瘟病 /
- 竞争性自适应重加权法 (CARS) /
- 支持向量机 (SVM) /
- 线性判别分析 (LDA)
Hyperspectral Identification of Rice Canopy Leaf Blast and Nitrogen Deficiency
To achieve early detection of rice leaf blast and nitrogen deficiency at the canopy scale using hyperspectral data from rice field plots.
Based on nitrogen stress gradient experiments and natural field infection, hyperspectral images of nitrogen-deficient and naturally infected rice canopies were captured using an outdoor hyperspectral imaging system. Spectral reflectance features of moderate and mild infection, nitrogen-deficient, and healthy rice canopies were extracted and analyzed. For the preprocessed hyperspectral data, three feature extraction methods were applied, including principal component analysis (PCA), vegetation index (VI), and competitive adaptive reweighted sampling (CARS), and linear discriminant analysis (LDA) and support vector machine (SVM) classifiers were used to construct early detection models for rice leaf blast and nitrogen deficiency.
The model constructed using PCA alone did not show significant classification performance. The models based on VI showed slight improvement over the full-spectrum models, but the enhancement was not substantial. The model constructed using features selected by the CARS method demonstrated the best discriminatory ability. Further dimensionality reduction of the extracted CARS features by PCA, five principal components were got and then used to construct the SVM and LDA models, which had overall classification accuracies of 95.51% and 96.15%, with Kappa coefficients of 0.91 and 0.92, respectively, demonstrating high classification consistency.
This study successfully achieves effective identification of rice leaf blast and nitrogen deficiency at the canopy scale using a limited number of feature variables. The results provide new insights and theoretical foundations for remote sensing monitoring of rice diseases and nutrient stress. The findings can support large-scale rice pest and disease monitoring and precision spraying, offering significant applications in the field of precision agriculture.
参考文献
| [1] | 杨燕, 何勇. 基于高光谱图像的稻瘟病抗氧化酶值早期预测[J]. 农业工程学报, 2013, 29(20): 135. DOI: 10.3969/j.issn.1002-6819.2013.20.019. |
| [2] | 马文强, 张漫, 李源, 等. 基于高光谱成像的核桃仁品质检测与分类方法[J]. 分析化学, 2020, 48(12): 1737. DOI: 10.19756/j.issn.0253-3820.191289. |
| [3] |
XIE C Q, CHU B Q, HE Y. Prediction of banana color and firmness using a novel wavelengths selection method of hyperspectral imaging[J]. Food Chemistry, 2018, 245(4): 132. DOI: 10.1016/j.foodchem.2017.10.079. |
| [4] | 孙俊, 靳海涛, 芦兵, 等. 基于高光谱图像及深度特征的大米蛋白质含量预测模型[J]. 农业工程学报, 2019, 35(15): 295. DOI: 10.11975/j.issn.1002-6819.2019.15.036. |
| [5] |
TIAN Y C, GU K J, CHU X, et al. Comparison of different hyperspectral vegetation indices for canopy leaf nitrogen concentration estimation in rice[J]. Plant and Soil, 2014, 376(1/2): 193. DOI: 10.1007/s11104-013-1937-0. |
| [6] |
WANG J Q, WU W M, WANG T W, et al. Estimation of leaf chlorophyll content and density in Populus euphratica based on hyperspectral characteristic variables[J]. Spectroscopy Letters, 2018, 51(9): 485. DOI: 10.1080/00387010.2018.1523194. |
| [7] | 秦占飞, 常庆瑞, 谢宝妮, 等. 基于无人机高光谱影像的引黄灌区水稻叶片全氮含量估测[J]. 农业工程学报, 2016, 32(23): 77. DOI: 10.11975/j.issn.1002-6819.2016.23.011. |
| [8] |
WANG Y, JIANG F, GUPTA B B, et al. Variable selection and optimization in rapid detection of soybean straw biomass based on CARS[J]. IEEE Access, 2017, 99: 1. DOI: 10.1109/ACCESS.2017.2763596. |
| [9] |
LU J Z , ZHOU M C , GAO Y W , et al. Using hyperspectral imaging to discriminate yellow leaf curl disease in tomato leaves[J]. Precision Agriculture, 2018, 19(1): 1. DOI: 10.1007/s11119-017-9524-7. |
| [10] |
JIN X, LU J, WANG S, et al. Classifying wheat hyperspectral pixels of healthy heads and fusarium head blight disease using a deep neural network in the wild field[J]. Remote Sensing, 2018, 10(3): 395. DOI: 10.3390/rs10030395. |
| [11] |
ASHOURLOO D, AGHIGHI H, MATKAN A A, et al. An investigation into machine learning regression techniques for the leaf rust disease detection using hyperspectral measurement[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2016, 9(9): 4344. DOI: 10.1109/jstars.2016.2575360. |
| [12] | 秦立峰, 张熹, 张晓茜. 基于高光谱病害特征提取的温室黄瓜霜霉病早期检测[J]. 农业机械学报, 2020, 51(11): 212. DOI: 10.6041/j.issn.1000-1298.2020.11.023. |
| [13] |
LIU Z Y, WU H F, HUANG J F. Application of neural networks to discriminate fungal infection levels in rice panicles using hyperspectral reflectance and principal components analysis[J]. Computers and Electronics in Agriculture, 2010, 72(2): 99. DOI: 10.1016/j.compag.2010.03.003. |
| [14] | 袁培森, 曹益飞, 马千里, 等. 基于Random Forest 的水稻细菌性条斑病识别方法研究[J]. 农业机械学报, 2021, 52(1): 139. DOI: 10.6041/j.issn.1000-1298.2021.01.015. |
| [15] | 康丽, 袁建清, 高睿, 等. 高光谱成像的水稻稻瘟病早期分级检测[J]. 光谱学与光谱分析, 2021, 41(3): 898. DOI: 10.3964/j.issn.1000-0593(2021)03-0898-05. |
| [16] | 黄双萍, 孙超, 齐龙, 等. 基于深度卷积神经网络的水稻穗颈瘟病检测方法[J]. 农业工程学报, 2017, 33(20): 169. DOI: 10.11975/j.issn.1002-6819.2017.20.021. |
| [17] |
KOBAYASHI T, SASAHARA M, KANDA E, et al. Assessment of rice panicle blast disease using airborne hyperspectral imagery[J]. Open Agriculture Journal, 2016, 10(1): 28. DOI: 10.2174/1874331501610010028. |
| [18] | 孔繁昌, 刘焕军, 于滋洋, 等. 高寒地区粳稻穗颈瘟的无人机高光谱遥感识别[J]. 农业工程学报, 2020, 36(22): 68. DOI: 10.11975/j.issn.1002-6819.2020.22.008. |
| [19] | 袁建清, 苏中滨, 贾银江, 等. 基于高光谱成像的寒地水稻叶瘟病与缺氮识别[J]. 农业工程学报, 2016, 32(13): 155. DOI: 10.11975/j.issn.1002-6819.2016.13.022. |
| [20] | 张竞成. 多源遥感数据小麦病害信息提取方法研究[D]. 杭州: 浙江大学, 2012. |
| [21] |
GITELSON A, MERZLYAK M N. Spectral reflectance changes associated with autumn senescence of Aesculus hippocastanum L. and Acer platanoides L. leaves. Spectral feature sand relation to chlorophyll estimation[J]. Journal of Plant Physiology, 1994, 143(3): 286. DOI: 10.1016/S0176-1617(11)81633-0. |
| [22] |
SIMS D A, GAMON J A. Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages[J]. Remote Sensing of Environment, 2002, 81(2/3): 337. DOI: 10.1016/S0034-4257(02)00010-X. |
| [23] |
GITELSON A A, ZUR Y, CHIVKUNOVA O B, et al. Assessing carotenoid content in plant leaves with reflectance spectroscopy[J]. Photochemistry and Photobiology, 2002, 75(3): 272. DOI: 10.1562/0031-8655(2002)075<0272:accipl>2.0.co;2. |
| [24] |
HUETE A R, LIU H Q, BATCHILY K, et al. A comparison of vegetation indices over a global set of TM images for EOS-MODIS[J]. Remote Sensing of Environment, 1997, 59(3): 440. DOI: 10.1016/S0034-4257(96)00112-5. |
| [25] | 黄敬峰, 王福民, 王秀珍. 水稻高光谱遥感实验研究[M]. 杭州: 浙江大学出版社, 2010. |
| [26] | 郑志雄, 齐龙, 马旭, 等. 基于高光谱成像技术的水稻叶瘟病病害程度分级方法[J]. 农业工程学报, 2013, 29(19): 138. DOI: 10.3969/j.issn.1002-6819.2013.19.017. |
| [27] |
ZHOU Q F, WANG J H. Comparison of adaxial and abaxial surface reflectance under different nitrogen level[J]. Transactions of the Chinese Society of Agricultural Engineering, 2002, 18(5): 34. DOI: 10.1007/s11769-002-0038-4. |
| [28] | 苏宝峰, 刘昱麟, 黄彦川, 等. 群体小麦条锈病发病动态无人机遥感监测方法[J]. 农业工程学报, 2021, 37(23): 127. DOI: 10.11975/j.issn.1002-6819.2021.23.015. |
| [29] | 郭铭淇, 包云轩, 黄璐, 等. 无人机多光谱影像在稻纵卷叶螟危害监测中的应用[J]. 江苏农业学报, 2023, 39(7): 1530. DOI: 10.969/j.issn.1000-4440.2023.07.010. |
| [30] | 李媛媛, 常庆瑞, 刘秀英, 等. 基于高光谱和BP神经网络的玉米叶片SPAD值遥感估算[J]. 农业工程学报, 2016, 32(16): 135. DOI: 10.11975/j.issn.1002-6819.2016.16.019. |
| [31] | 杨迈, 李晓琳, 郑毅, 等. 基于高光谱技术的滇池流域湿地植物氮含量估算模型构建[J]. 西南林业大学学报(自然科学), 2024, 44(3): 80. DOI: 10.11929/j.swfu.202303048. |
| [32] | 韩雨, 刘焕军, 张新乐, 等. 基于冠层光谱的水稻穗颈瘟病害程度预测模型[J]. 光谱学与光谱分析, 2022, 41(4): 1220. DOI: 10.3964/j.issn.1000-0593(2021)04-1220-07. |
| [33] | 姜荣昌, 顾鸣声, 赵庆贺, 等. 高光谱和卷积神经网络的大白菜农残检测[J]. 光谱学与光谱分析, 2022, 42(5): 1385. DOI: 10.3964/j.issn.1000-0593(2022)05-1385-08. |
| [34] | 王海龙, 杨国国, 张瑜, 等. 竞争性自适应重加权算法和相关系数法提取特征波长检测番茄叶片真菌病害[J]. 光谱学与光谱分析, 2017, 37(7): 2115. DOI: 10.3964/j.issn.1000-0593(2017)07-2115-05. |
| [35] | 刘潭, 李子默, 冯帅, 等. 基于LMPSO-SVM的高光谱水稻稻瘟病害分级检测[J]. 农业机械学报, 2023, 54(11): 208. DOI: 10.6041/j.issn.1000-1298.2023.11.020. |
| [36] | 吴迪, 曹芳, 张浩, 等. 基于可见—近红外光谱技术的水稻穗颈瘟染病程度分级方法研究[J]. 光谱学与光谱分析, 2009, 29(12): 3295. DOI: 10.3964/j.issn.1000-0593(2009)12-3295-05. |
| [37] | 刘翠翠, 杨涛, 马京晶, 等. 基于PCA-SVM的麦冬叶部病害识别系统[J]. 中国农机化学报, 2019, 40(8): 132. DOI: 10.13733/j.jcam.issn.2095-5553.2019.08.23. |
| [38] | 袁建清, 仇逊超, 贾银江, 等. 高光谱成像的水稻冠层穗颈瘟早期识别[J]. 西南大学学报(自然科学版), 2023, 45(6): 57. DOI: 10.13718/j.cnki.xdzk.2023.06.006. |
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