| 光谱学与光谱分析 |
|
|
|
|
|
| Disease Index Inversion of Wheat Stripe Rust on Different Wheat Varieties with Hyperspectral Remote Sensing |
| GUO Jie-bin, HUANG Chong, WANG Hai-guang, SUN Zhen-yu, MA Zhan-hong* |
| State Key Lab of Plant Pathology, Ministryof Agriculture, Department of Plant Pathology, China Agricultural University, Beijing 100094, China |
|
|
|
|
Abstract It is becoming more and more important to use mixed wheat varieties to control wheat stripe rust. Different wheat varieties were planted in field and stripe rust was caused by artificial inoculation. Disease index (DI) was assessed and the canopy reflection data of wheat canopy were obtained by ASD FieldSpec HandHeld FR(325-1 075 nm) made by ASD Company. The correlation analysis between DI and spectral data (reflectance and the first derivative) was conducted, and the estimation models between DI and reflection data (reflectance at 690 and 850 nm, SDr, NDVI and RVI) were built using linear regression method. The results showed that different combinations of wheat varieties had the similar variation at different disease index. DI has positive correlation with reflectance of wheat canopy in visible region, and has significant negative correlation in the near infrared region. DI has stable negative correlation with the first derivative in the region of 700-760 nm and with big fluctuation in other regions. The correlation was compared between DI and hyperspectral derivative index, and SDr has the best correlation with DI. DI estimation models were built based on the canopy reflectance at 690 and 850 nm, SDr, NDVI and RVI. The determinant coefficient of themodels is between 0.588 and 0.855, 0.669 and 0.911, 0.534 and 0.773, and 0.587 and 0.751, respectively, and all the models were fit well. The results indicated that DI of wheat stripe rust could be inverted using hyperspectral remote sensing technique and that the inversion effect was hardly influenced by the different combinations of wheat varieties.
|
|
Received: 1900-01-01
Accepted: 1900-01-01
|
|
|
|
Corresponding Authors:
MA Zhan-hong
E-mail: mazh@cau.edu.cn
|
|
[1] LI Guang-bo, ZENG Shi-mai, LI Zhen-qi(李光博, 曾士迈, 李振歧). Comprehensive Management of Disease, Pest, Weeds and Rats on Wheat(小麦病虫草鼠害综合治理). Beijing: China Agricultura Scientech Press(北京: 中国农业科技出版社), 1989.
[2] Moshou D, Brav C, West J, et al. Computers and Electronics in Agriculture，2004, 44: 173.
[3] HUANG Mu-yi, WANG Ji-hua, HUANG Yi-de, et al(黄木易, 王纪华, 黄义德, 等). Journal of Anhui Agricultural University(安徽农业大学学报), 2004, 31(1): 119.
[4] AN Hu, WANG Hai-guang, LIU Rong-ying, et al(安 虎, 王海光, 刘荣英, 等). China Plant Protection(中国植保导刊)，2005, 25(11): 8.
[5] JIANG Jin-bao, CHEN Yun-hao, HUANG Wen-jiang, et al(蒋金豹, 陈云浩, 黄文江，等). Optical Technique(光学技术)，2007, 33(4): 620.
[6] LI Jing, CHEN Yun-hao, JIANG Jin-bao, et al(李 京, 陈云浩, 蒋金豹, 等). Science & Technology Review(科技导报)，2007, 25(6): 23.
[7] CAI Cheng-jing, MA Zhan-hong, WANG Hai-guang, et al(蔡成静, 马占鸿, 王海光, 等). Acta Phytopathologica Sinica(植物病理学报)，2007, 37(1): 77.
[8] LIU Liang-yun, HUANG Mu-yi, HUANG Wen-jiang, et al(刘良云, 黄木易, 黄文江, 等). Journal of Remote Sensing(遥感学报)，2004, 8(3): 275.
[9] Chen X M. Canadian Journal of Plant Pathology, 2005, 27(3): 314.
[10] CAO Ke-qiang, LI Xiao-li, ZENG Shi-mai(曹克强, 李晓莉, 曾士迈). Journal of Agricultural University of Hebei(河北农业大学学报)，1995, 18(2): 30.
[11] GUO Shi-bao, KANG Zhen-sheng, ZHANG Long-zhi, et al(郭世保, 康振声, 张龙芝, 等). Journal of Northwest A & F University(西北农林科技大学学报)，2007, 35(11): 125.
[12] CHEN Qi-cun, ZHU You-yong, LI Zhen-qi, et al(陈企村, 朱有勇, 李振崎, 等). Journal of Northwest A & F University(西北农林科技大学学报)，2008, 36(5): 119.
[13] JIANG Jin-bao, CHEN Yun-hao, HUANG Wen-jiang, et al(蒋金豹, 陈云浩, 黄文江，等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报)，2008, 24(1): 35.
[14] YANG Ke-ming, GUO Da-zhi(杨可明, 郭达志). Geography and Geo-Information Science(地理与地理信息科学)，2006, 22(4): 31.
[15] Adam K Tillinga, Garry J O’Learyb, Jelle G Ferwerdaa, et al. Field Crops Research，2007, 104: 77.
[16] LI Wei-guo, WANG Ji-hua, ZHAO Chun-jiang, et al(李卫国, 王纪华, 赵春江, 等). Journal of Remote Sensing(遥感学报)，2008, 12(3): 506.
[17] MA Li, XU Xin-gang, LIU Liang-yun, et al(马 丽, 徐新刚, 刘良云, 等). Remote Sensing Technology and Application(遥感技术与应用), 2008, 23(5): 520.
|
| [1] |
XU Tian1, 2, LI Jing1, 2, LIU Zhen-hua1, 2*. Remote Sensing Inversion of Soil Manganese in Nanchuan District, Chongqing[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 69-75. |
| [2] |
WANG Cai-ling1，ZHANG Jing1，WANG Hong-wei2*, SONG Xiao-nan1, JI Tong3. A Hyperspectral Image Classification Model Based on Band Clustering and Multi-Scale Structure Feature Fusion[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 258-265. |
| [3] |
GAO Hong-sheng1, GUO Zhi-qiang1*, ZENG Yun-liu2, DING Gang2, WANG Xiao-yao2, LI Li3. Early Classification and Detection of Kiwifruit Soft Rot Based on
Hyperspectral Image Band Fusion[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 241-249. |
| [4] |
WU Hu-lin1, DENG Xian-ming1*, ZHANG Tian-cai1, LI Zhong-sheng1, CEN Yi2, WANG Jia-hui1, XIONG Jie1, CHEN Zhi-hua1, LIN Mu-chun1. A Revised Target Detection Algorithm Based on Feature Separation Model of Target and Background for Hyperspectral Imagery[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 283-291. |
| [5] |
HUANG You-ju1, TIAN Yi-chao2, 3*, ZHANG Qiang2, TAO Jin2, ZHANG Ya-li2, YANG Yong-wei2, LIN Jun-liang2. Estimation of Aboveground Biomass of Mangroves in Maowei Sea of Beibu Gulf Based on ZY-1-02D Satellite Hyperspectral Data[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3906-3915. |
| [6] |
ZHOU Bei-bei1, LI Heng-kai1*, LONG Bei-ping2. Variation Analysis of Spectral Characteristics of Reclaimed Vegetation in an Ionic Rare Earth Mining Area[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3946-3954. |
| [7] |
CHU Bing-quan1, 2, LI Cheng-feng1, DING Li3, GUO Zheng-yan1, WANG Shi-yu1, SUN Wei-jie1, JIN Wei-yi1, HE Yong2*. Nondestructive and Rapid Determination of Carbohydrate and Protein in T. obliquus Based on Hyperspectral Imaging Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3732-3741. |
| [8] |
YUAN Wei-dong1, 2, JU Hao2, JIANG Hong-zhe1, 2, LI Xing-peng2, ZHOU Hong-ping1, 2*, SUN Meng-meng1, 2. Classification of Different Maturity Stages of Camellia Oleifera Fruit
Using Hyperspectral Imaging Technique[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3419-3426. |
| [9] |
FU Gen-shen1, LÜ Hai-yan1, YAN Li-peng1, HUANG Qing-feng1, CHENG Hai-feng2, WANG Xin-wen3, QIAN Wen-qi1, GAO Xiang4, TANG Xue-hai1*. A C/N Ratio Estimation Model of Camellia Oleifera Leaves Based on
Canopy Hyperspectral Characteristics[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3404-3411. |
| [10] |
SHEN Ying, WU Pan, HUANG Feng*, GUO Cui-xia. Identification of Species and Concentration Measurement of Microalgae Based on Hyperspectral Imaging[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3629-3636. |
| [11] |
XIE Peng, WANG Zheng-hai*, XIAO Bei, CAO Hai-ling, HUANG Yi, SU Wen-lin. Hyperspectral Quantitative Inversion of Soil Selenium Content Based on sCARS-PSO-SVM[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3599-3606. |
| [12] |
QIAN Rui1, XU Wei-heng2, 3 , 4*, HUANG Shao-dong2, WANG Lei-guang2, 3, 4, LU Ning2, OU Guang-long1. Tea Plantations Extraction Based on GF-5 Hyperspectral Remote Sensing
Imagery in the Mountainous Area[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3591-3598. |
| [13] |
ZHU Zhi-cheng1, WU Yong-feng2*, MA Jun-cheng2, JI Lin2, LIU Bin-hui3*, JIN Hai-liang1*. Response of Winter Wheat Canopy Spectra to Chlorophyll Changes Under Water Stress Based on Unmanned Aerial Vehicle Remote Sensing[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3524-3534. |
| [14] |
YANG Lei1, 2, 3, ZHOU Jin-song1, 2, 3, JING Juan-juan1, 2, 3, NIE Bo-yang1, 3*. Non-Uniformity Correction Method for Splicing Hyperspectral Imager Based on Overlapping Field of View[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3582-3590. |
| [15] |
SUN Lin1, BI Wei-hong1, LIU Tong1, WU Jia-qing1, ZHANG Bao-jun1, FU Guang-wei1, JIN Wa1, WANG Bing2, FU Xing-hu1*. Identification Algorithm of Green Algae Using Airborne Hyperspectral and Machine Learning Method[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3637-3643. |
|
|
|
|
