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Acta Agronomica Sinica ›› 2020, Vol. 46 ›› Issue (8): 1248-1257.doi: 10.3724/SP.J.1006.2020.01004

• TILLAGE & CULTIVATION·PHYSIOLOGY & BIOCHEMISTRY • Previous Articles     Next Articles

Canopy SIF synergize with total spectral reflectance optimized by the MDBPSO algorithm to monitor wheat stripe rust

BAI Zong-Fan1,JING Xia1,*(),ZHANG Teng1,DONG Ying-Ying2   

  1. 1College of Geometrics, Xi’an University of Science and Technology, Xi’an 710054, Shaanxi, China
    2Institute of Remote Sensing and Digital Earth, Chinese Academy of Science, Beijing 100094, China
  • Received:2020-01-09 Accepted:2020-03-24 Online:2020-08-12 Published:2020-04-17
  • Contact: Xia JING E-mail:jingxiaxust@163.com
  • Supported by:
    National Natural Science Foundation of China (41601467)(41601467)

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Abstract:

In order to extract the characteristic parameters sensitive to wheat stripe rust from total spectral reflectance and improve the operation efficiency and estimation accuracy of the wheat stripe rust remote sensing detection model, this paper improved the traditional discrete binary particle swarm optimization (DBPSO) algorithm from two aspects: inertia weight and particle update method. The modified discrete binary particle swarm optimization (MDBPSO) algorithm was used to select the characteristic parameters for the severity of wheat stripe rust from the total spectral reflectance. The selected characteristic variables were combined with canopy solar-induced chlorophyll fluorescence (SIF) data as independent variables to construct wheat stripe rust estimation model. Random forest (RF) algorithm and back propagation (BP) neural network algorithm were used as model construction method to compare and analyze the accuracy of the feature parameter construction model extracted by the correlation coefficient (CC) analysis method and the DBPSO algorithm. The MDBPSO algorithm had faster convergence speed and higher optimization accuracy than the DBPSO algorithm. The number of iterations before and after the improvement was reduced from 395 to 156. The optimal fitness function (OFV) value decreased from 0.145 to 0.127. When the MDBPSO algorithm was used to select feature variables, the accuracy of the models constructed by the two methods of RF and BP neural networks was higher than that by CC analysis and DBPSO. The validation set determination coefficient (R2V) between the predicted disease index (DI) value and the measured DI value of the RF algorithm was 9% and 3% higher than that of the CC analysis method and the DBPSO algorithm, the validation set root mean square error (RMSEV) was reduced by 28% and 11%, respectively. The R2V between the predicted disease index (DI) value and the measured DI value of the BP neural network algorithm was 13% and 6% higher than that of the CC analysis method and DBPSO algorithm, respectively, and the RMSEV was reduced by 21% and 10% respectively. The MDBPSO algorithm can improve the remote sensing detection accuracy of wheat stripe rust. Among the three feature selection algorithms of MDBPSO, DBPSO, and CC analysis, the accuracy of the model constructed by the RF algorithm was higher than that by the BP neural network algorithm. The R2V between the predicted DI value and the measured DI value of the RF model was at least 7% higher than that of BP neural network algorithm, with an average increase of 9%; RMSEV had reduced by at least 15%, with an average reduction of 20%. The MDBPSO-RF model for wheat stripe rust remote sensing detection constructed by RF method using the characteristic parameters selected by the MDBPSO algorithm as independent variables is an appropriate model for wheat stripe rust remote sensing detection. The research results provide new ideas for further realizing large-area high-precision remote sensing monitoring of crop health.

Key words: total spectral reflectance, modified particle swarm optimization, solar-induced chlorophyll fluorescence, stripe rust of wheat, feature band

Fig. 1

Curve of correlation coefficient between reflectance and disease index after SNV transformation (n = 52) The dotted line in the figure indicates the significant at the 0.1% propability level, and R0.001[52] = 0.443."

Fig. 2

Convergence curves of fitness functions for DBPSO and MDBPSO algorithms"

Fig. 3

Feature bands selected by the DBPSO and MDBPSO algorithms"

Fig. 4

Distribution of measured and predicted values of wheat stripe rust disease index — 1:1 relationship line; ■ training set; △ test set."

[1] 董锦绘, 杨小冬, 杨贵军, 王宝山. 基于近地高光谱信息的小麦条锈病病情指数反演. 麦类作物学报, 2016,36:1674-1680.
Dong J H, Yang X D, Yang G J, Wang B S. Inversion of wheat stripe rust disease index based on near ground hyperspectral data. J Triticeae Crops, 2016,36:1674-1680 (in Chinese with English abstract).
[2] 赵叶, 竞霞, 黄文江, 董莹莹, 李存军. 日光诱导叶绿素荧光与反射率光谱数据监测小麦条锈病严重度的对比分析. 光谱学与光谱分析, 2019,39:2739-2745.
Zhao Y, Jing X, Huang W J, Dong Y Y, Li C J. Comparison of sun-induced chlorophyll fluorescence and reflectance data on estimating severity of wheat stripe rust. Spectrosc Spect Anal, 2019,39:2739-2745 (in Chinese with English abstract).
[3] Liang D, Liu N, Zhang D Y, Zhao J L, Ding Y W. Discrimination of powdery mildew and yellow rust of winter wheat using high-resolution hyperspectra and imageries. Infrared Laser Eng, 2017,46:50-58.
[4] Zheng Q, Huang W J, Cui X M, Shi Y, Liu L Y. New spectral index for detecting wheat yellow rust using sentinel-2 multispectral imagery. Sensor(Basel), 2018,18:868-886.
[5] 梁琨, 张夏夏, 丁静, 徐剑宏, 韩东燊, 沈明霞. 傅里叶中红外光谱结合稀疏表示分类方法鉴别小麦赤霉病感染等级. 光谱学与光谱分析, 2019,39:3251-3255.
Liang K, Zhang X X, Ding J, Xu J H, Han D S, Shen M X. Discrimination of wheat scab infection level by Fourier mid-infrared technology combined with sparse representation based classification method. Spectrosc Spect Anal, 2019,39:3251-3255 (in Chinese with English abstract).
[6] Shi Y, Huang W J, González-Moreno P, Luke B, Dong Y Y, Zheng Q, Ma H Q, Liu L Y. Wavelet-based rust spectral feature set (WRSFs): a novel spectral feature set based on continuous wavelet transformation for tracking progressive host-pathogen interaction of yellow rust on wheat. Remote Sens, 2018,252:1-19.
[7] Shi Y, Huang W J, Zhou X F. Evaluation of wavelet spectral features in pathological detection and discrimination of yellow rust and powdery mildew in winter with hyperspectral reflectance data. J Appl Remote Sens, 2017,11:1-18.
[8] 蒋金豹, 陈云浩, 黄文江, 李京. 冬小麦条锈病严重度高光谱遥感反演模型研究. 南京农业大学学报, 2007,30(3):63-67.
Jiang J B, Chen Y H, Huang W J, Li J. Study on hyperspectral remote sensing retrieval models about winter wheat stripe rust severity. J Nanjing Agric Univ, 2007,30(3):63-67 (in Chinese with English abstract).
[9] Wang L, Qu J J. Satellite remote sensing applications for surface soil moisture monitoring: a review. Front Earth Sci China, 2009,3:237-247.
[10] 刘琦, 谷医林, 王翠翠, 王睿, 李薇, 马占鸿. 基于偏最小二乘法的小麦条锈病潜育期冠层高光谱分析. 植物保护学报, 2018,45:138-145.
Liu Q, Gu Y L, Wang C C, Wang R, Li W, Ma Z H. Canopy hyperspectral features analysis of latent period wheat stripe rust based on discriminant partial least squares. J Plant Prot, 2018,45:138-145 (in Chinese with English abstract).
[11] 黄木易, 黄文江, 刘良云, 黄义德, 王纪华, 赵春江, 万安民. 冬小麦条锈病单叶光谱特性及严重度反演. 农业工程学报, 2004,20(1):176-180.
Huang M Y, Huang W J, Liu L Y, Huang Y D, Wang J H, Zhao C J, Wan A M. Spectral reflectance feature of winter wheat single leaf infected with stripe rust and severity level inversion. Trans CSAE, 2004,20(1):176-180 (in Chinese with English abstract).
[12] 刘琦, 王翠翠, 王睿, 谷医林, 李薇, 马占鸿. 潜育期小麦条锈菌的高光谱定性识别. 植物保护学报, 2018,45:153-160.
Liu Q, Wang C C, Wang R, Gu Y L, Li W, Ma Z H. Hyperspectral qualitative identification on latent period of wheat stripe rust. J Plant Prot, 2018,45:153-160 (in Chinese with English abstract).
[13] Yang C, Tan Y L, Bruzzone L, Lu L J, Guan R C. Discriminative feature metric learning in the affinity propagation model for band selection in hyperspectral images. Remote Sens, 2017,9:782-798.
[14] Kennedy J, Eberhart R. A discrete binary version of the particle swarm algorithm. In: IEEE, eds. International Conference on Systems, Man, and Cybernetics. Florida, USA: Computational Cybernetics and Simulation, 1997. pp 4104-4108.
[15] 沈林成, 霍霄华, 牛轶峰. 离散粒子群优化算法研究现状综述. 系统工程与电子技术, 2008,30:1986-1990.
Shen L C, Huo X H, Niu Y F. Survey of discrete particle swarm optimization algorithm. J Syst Eng Electron, 2008,30:1986-1990 (in Chinese with English abstract).
[16] Yin C, Ye Y, Zhao H, Jiang Y Z, Wang H, Shang Y Z, Wang J F. Remote sensing of water quality based on HJ-1A HSI imagery with modified discrete binary particle swarm optimization-partial least squares (MDBPSO-PLS) in inland waters: a case in Weishan Lake. Ecol Inf, 2018,44:21-32.
[17] 张珏, 田海清, 赵志宇, 张丽娜, 张晶, 李斐. 基于改进离散粒子群算法的青贮玉米原料含水率高光谱检测. 农业工程学报, 2019,35(1):285-293.
Zhang J, Tian H Q, Zhao Z Y, Zhang L N, Zhang J, Li F. Moisture content detection in silage maize raw material based on hyperspectrum and improved discrete particle swarm. Trans CSAE, 2019,35(1):285-293 (in Chinese with English abstract).
[18] Yang J, Zhang H S, Ling Y, Cheng P. Task allocation for wireless sensor network using modified binary particle swarm optimization. IEEE Sensor J, 2014,14:882-891.
[19] 王丽爱, 周旭东, 朱新开, 郭文善. 基于HJ-CCD数据和随机森林算法的小麦叶面积指数反演. 农业工程学报, 2016,32(3):149-154.
Wang L A, Zhou X D, Zhu X K, Guo W S. Inverting wheat leaf area index based on HJ-CCD remote sensing data and random forest algorithm. Trans CSAE, 2016,32(3):149-154 (in Chinese with English abstract).
[20] 胡小平, 杨之为, 李振岐, 邓志勇, 柯长华, 汉中地区小麦条锈病的BP神经网络预测. 西北农业学报, 2000,9(3):28-31.
Hu X P, Yang Z W, Li Z Q, Deng Z Y, Ke C H. BP neural network prediction of wheat stripe rust in Huazhong region. Acta Agric Boreali-Occident Sin, 2000,9(3):28-31 (in Chinese with English abstract).
[21] Rodriguez-Gailano V, Mendes M P, Garcia-Soldadoo M J, Chica-Olmo M, Ribeiro L. Predictive modeling of groundwater nitratepollution using Random Forest and multisource variables related to intrinsic and specific vulnerability: a case study in an agricultural setting (Southern Spain). Sci Total Environ, 2014,476:189-206.
doi: 10.1016/j.scitotenv.2014.01.001 pmid: 24463255
[22] Ashour loo D, Mobasheri M R, Huete A. Developing two spectral disease indices for detection for wheat leaf rust ( Puccinia triticina). Remote Sens, 2014,6:4723-4740.
[23] Song L, Guanter L, Guan K, You L, Huete A, Ju W, Zhang Y. Satellite sun-induced chlorophyll fluorescence detects early response of winter wheat to heat stress in the Indian Indo-Gangetic Plains. Global Change Biol, 2018,24:4023-4037.
[24] 竞霞, 白宗璠, 高媛, 刘良云. 利用随机森林法协同SIF和反射率光谱监测小麦条锈病. 农业工程学报, 2019,35(13):154-161.
Jing X, Bai Z F, Gao Y, Liu L Y. Wheat stripe rust monitoring by random forest algorithm combined with SIF and reflectance spectrum. Trans CSAE, 2019,35(13):154-161. (in Chinese with English abstract).
[25] 陈思媛, 竞霞, 董莹莹, 刘良云. 基于日光诱导叶绿素荧光与反射率光谱的小麦条锈病探测研究. 遥感技术与应用, 2019,34:511-520.
Chen S Y, Jing X, Dong Y Y, Liu L Y. Detection of wheat stripe rust using solar-induced chlorophyll fluorescence and reflectance spectral indices. Remot Sens Technol Appl, 2019,34:511-520 (in Chinese with English abstract).
[26] 孙红, 郑涛, 刘宁, 程明, 李民赞, Zhang Q. 高光谱图像检测马铃薯植株叶绿素含量垂直分布. 农业工程学报, 2018,34(1):149-156.
Sun H, Zheng T, Liu N, Cheng M, Li M Z, Zhang Q. Vertical distribution of chlorophyll in potato plants based on hyperspectral imaging. Trans CSAE, 2018,34(1):149-156 (in Chinese with English abstract).
[27] 鲁军景, 黄文江, 蒋金豹, 张竞成. 小波特征与传统光谱特征预测冬小麦条锈病病情严重度的对比研究. 麦类作物学报, 2015,35:1456-1461.
Lu J J, Huang W J, Jiang J B, Zhang J C. A comparative study on the severity of stripe rust disease in winter wheat estimated by little baud sign and traditional spectral features. J Triticeae Crops, 2015,35:1456-1461 (in Chinese with English abstract).
[28] 刘新杰. 日光诱导叶绿素荧光的遥感反演研究. 中国科学院大学博士论文, 北京, 2016. pp 17-19.
Liu X J. Retrieval of Solar-Induced Chlorophyll Fluorescence by Remote Sensing. PhD Dissertation of University of Chinese Academy of Sciences, Beijing, China, 2016. pp 17-19 (in Chinese with English abstract).
[29] 刘良云. 植被定量遥感原理与应用. 北京: 科学出版社, 2014. pp 146-153
Liu L Y. Principles and Applications of Quantitative Remote Sensing for Vegetation. Beijing: Science Press, 2014. pp 146-153(in Chinese).
[30] Maier S W, Günther K P, Stellmes M. Sun-induced fluorescence: a new tool for precision farming. In: McDonald M, Schepers J, Tartly L, eds. Digital Imaging Spectral Techniques: Applications to Precision Agriculture Crop Physiology. Madison, USA: American Society of Agronomy, 2003. pp 209-222.
[31] Liu X J, Liu L Y. Improving chlorophyll fluorescence retrieval using reflectance reconstruction based on principal components analysis. IEEE Geosci Remote Sens Lett, 2015,12:1645-1649.
[32] Cordon G, Lagorio M G, Paruelo J M. Chlorophyll fluorescence, photochemical reflective index and normalized difference vegetative index during plant senescence. J Plant Physiol, 2016,199:100-110.
doi: 10.1016/j.jplph.2016.05.010 pmid: 27302011
[33] 曹引, 冶运涛, 赵红莉, 蒋云钟, 王浩, 严登明. 基于离散粒子群和偏最小二乘的水源地浊度高光谱反演. 农业机械学报, 2018,49(1):173-182.
Cao Y, Ye Y T, Zhao H L, Jiang Y Z, Wang H, Yan D M. Satellite hyperspectral retrieval of turbidity for water source based on discrete particle swarm and partial least squares. Trans CSAM, 2018,49(1):173-182 (in Chinese with English abstract).
[34] Yang H C, Zhang S B, Deng K Z, Du P J. Research into a feature selection method for hyperspectral imagery using PSO and SVM. J China Univ Min Technol, 2007,17:473-478.
[35] 张绘娟, 张达敏, 闫威, 陈忠云, 辛梓芸. 异构网络中基于吞吐量优化的资源分配机制. 计算机科学, 2019,46(10):109-115.
Zhang H J, Zhang D M, Yan W, Chen Z Y, Xin Z Y. Throughput optimization based resource allocation mechanism in heterogeneous networks. Compute Sci, 2019,46(10):109-115 (in Chinese with English abstract).
[36] 李健丽, 董莹莹, 师越, 朱溢佞, 黄文江. 基于随机森林模型的小麦白粉病遥感监测方法. 植物保护学报, 2018,45:395-396.
Li J L, Dong Y Y, Shi Y, Zhu Y N, Huang W J. Remote sensing monitoring of wheat powdery mildew based on random forest model. J Plant Prot, 2018,45:395-396 (in Chinese with English abstract).
[37] 袁冰清, 程功, 郑柳刚. BP神经网络基本原理. 数字通信世界, 2008,8(17):28-29.
Yuan B Q, Cheng G, Zheng L G. Basic principle of BP neural networks. Digital Commun World, 2008,8(17):28-29 (in Chinese with English abstract).
[38] 姚雄, 余坤勇, 杨玉洁, 曾琪, 陈璋昊, 刘健. 基于随机森林模型的林地叶面积指数遥感估算. 农业机械学报, 2017,48(5):159-166.
Yao X, Yu K Y, Yang Y J, Zeng Q, Chen Z H, Liu J. Estimation of forest leaf area index based on random forest model and remote sensing data. Trans CSAM, 2017,48(5):159-166 (in Chinese with English abstract).
[39] 依尔夏提·阿不来提, 买买提·沙吾提, 白灯莎·买买提艾力, 安申群, 马春玥. 基于随机森林法的棉花叶片叶绿素含量估算. 作物学报, 2019,45:81-90.
Ershat A, Mamat S, Baidengsha M, An S Q, Ma C Y. Estimation of leaf chlorophyll content in cotton based on the random forest approach. Acta Agron Sin, 2019,45:81-90 (in Chinese with English abstract).
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