Research progress of crop diseases and pests monitoring based on remote sensing

Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering

Volumn 28, Issue 20, 2012, Pages 1-11

  Zhang, Jingcheng ^a,b   Yuan, Lin ^a,b   Wang, Jihua ^a,b   Luo, Juhua ^a   Du, Shizhou ^a   Huang, Wenjiang ^a,c  

^a BEIJING RESEARCH CENTER FOR INFORMATION TECHNOLOGY IN AGRICULTURE   (China)

^b ZHEJIANG UNIVERSITY   (China)

^c CENTER FOR EARTH OBSERVATION AND DIGITAL EARTH   (China)

Author keywords

Crops; Diseases; Pest control; Remote sensing; Spectral characteristic bands; Vegetation index

Indexed keywords

ALTERNATIVE METHODS; AREA MONITORING; CROP DISEASE; DATA COLLECTION; DISEASE MONITORING; ENVIRONMENTAL INFORMATION; FIELD SAMPLING; KEY TECHNOLOGIES; LABOR INTENSIVE; MANUAL INSPECTION; PHYSIOLOGICAL MECHANISMS; REAL TIME MONITORING; RESEARCH PROGRESS; SPECTRAL CHARACTERISTICS; SPECTRAL RESPONSE; VEGETATION INDEX; YIELD LOSS;

DISEASES; MONITORING; PEST CONTROL; PHYSIOLOGICAL MODELS; REMOTE SENSING; VEGETATION;

CROPS;

EID: 84869142087     PISSN: 10026819     EISSN: None     Source Type: Journal    
DOI: 10.3969/j.issn.1002-6819.2012.20.001     Document Type: Article

References (62)

1. Strange R N, Scott P R. Plant Disease: A threat to global food security[J]. Annual reviews phytopathol, 2005, 43: 83-116.
2. Huo Zhiguo, Liu Wancai, Shao Zhenrun, et al. On developing long term meteorological prediction research of crop pests and diseases prevailing in China[J]. Journal of Natural Disasters, 2000, 9(1): 117-121.(in Chinese with English abstract)
3. Cheng Jia'an, Zhu Zengrong. Analysis on the key factors causing the outbreak of brown planthopper in Yangtze Area, China in 2005[J]. Plant Protection, 2006, 32(4): 1-4.(in Chinese with English abstract)
4. Gabrynowicz J I. The work of the US National Satellite Land Remote Sensing Data Archive Advisorycommittee: 1998-2000[J]. Space Policy, 2001, 17(1): 49-53.
5. Weng Q H. Advances in Environmental Remote Sensing: Sensors, Algorithms, and Applications[M]. CRC Press, 2011.
6. 2008.
7. 2009.
8. Cheng T, Rivard B, Sánchez-Azofeifa A, et al. Continuous wavelet analysis for the detection of green attack damage due to mountain pine beetle infestation[J]. Remote Sensing of Environment, 2010, 114(4): 899-910.
9. Zhang J C, Luo J H, Huang W J, et al. Continuous wavelet analysis based spectral feature selection for winter wheat yellow rust detection[J]. Intelligent Automation and Softcomputing, 2011, 17(5): 531-540.
10. Zhang J C, Pu R L, Huang W J, et al. Using in-situ hyperspectral data for detecting and discriminating yellow rust disease from nutrient stresses[J]. Field Crops Research, 2012, 134: 165-174.
11. Zhang J C, Pu R L, Wang J H, et al. Detecting powdery mildew of winter wheat using leaf level hyperspectral measurements[J]. Computers and Electronics in Agriculture, 2012, 85: 13-23.
12. Kobayashi T, Kanda E, Kitada K. Detection of rice panicle blast with multispectral radiometer and the potential of using airborne multispectral scanners[J]. The American Phytopathological Society, 2001, 91(3): 316-323.
13. Yang C M, Cheng C H. Spectral characteristics of rice plants infested by brown planthoppers[J]. Proceedings of the National Science Council Republic of China Part B Life Sciences, 2001, 25(3): 180-186.
14. Yang C M, Cheng C H, Chen R K. Changes in spectral characteristics of rice canopy infested with brown planthopper and leaffolder[J]. Crop Science, 2007, 47(1): 329-335.
15. 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 principalcomponents analysis[J]. Computers and Electronics in Agriculture, 2010, 72(2): 99-106.
16. Graeff S, Link J, Claupein W. Identification of powdery mildew (Erysiphe graminis sp. tritici) and take-all disease (Gaeumannomyces graminis sp. tritici) in wheat (Triticum aestivum L.) by means of leaf reflectance measurements[J]. Central European Journal of Biology, 2006, 1(2): 275-288.
17. Delwiche S R, Kim M S. Hyperspectral imaging for detection of scab in wheat[J]. Biological Quality and Precision Agriculture II, 2000, 4203: 13-20.
18. Huang Muyi, Wang Jihua, Huang Wenjiang, et al. Hyperspectral character of stripe rust on winter wheat and monitoring by remote sensing[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2003, 19(6): 154-158.(in Chinese with English abstract)
19. Liu Liangyun, Huang Muyi, Huang Wenjiang, et al. Monitoring stripe rust disease of winter wheat using multi-temporal hyperspectral airborne data[J]. Journal of Remote Sensing, 2004, 8(3): 275-281.(in Chinese with English abstract)
20. Moshou D, Bravo C, West J, et al. Automatic detection of 'yellow rust' in wheat using reflectance measurements and neural networks[J]. Computers and Electronics in Agriculture, 2004, 44(3): 173-188.
21. Moshou D, Bravo C, Oberti R, et al. Plant disease detection based on data fusion of hyper-spectral and multi-spectral fluorescence imaging using Kohonen maps[J]. Real-Time Imaging, 2005, 11(2): 75-83.
22. Sasaki Y, Okamoto T, Imo K, et al. Automatic diagnosis of plant disease: Recognition between healthy and diseased leaf [J]. Journal of the Japanese Society of Agricultural Machinery, 1999, 61(2): 119-126.
23. Zhang M H, Qin Z H, Liu X, et al. Detection of stress in tomatoes induced by late blight disease in California, USA, using hyperspectral remote sensing[J]. International Journal of Applied Earth Observation and Geo Information, 2003, 4(4): 295-310.
24. Zhang M H, Qin Z H, Liu X. Remote sensed spectral imagery to detect late blight in field tomatoes[J]. Precision Agriculture, 2005, 6(6): 489-508.
25. Xu H R, Ying Y B, Fu X P, et al. Near-infrared spectroscopy in detection leaf miner damage on tomato leaf[J]. Biosystems Engineering, 2007, 96(4): 447-454.
26. Jones C D, Jones J B, Lee W S. Diagnosis of bacterial spot of tomato using spectral signatures[J]. Computers and Electronics in Agriculture, 2010, 74(2): 329-335.
27. Naidu R A, Perry E M, Pierce F J, et al. The potential of spectral reflectance technique for the detection of Grapevine leafroll-associated virus-3in two red-berried wine grape cultivars[J]. Computers and Electronics in Agriculture, 2009, 66(1): 38-45.
28. Wang W, Thai C, Gitaitis R, et al. Detecting of sour skin diseases in Vidalia sweet onions using near-infrared hyperspectral imaging. 2009 ASABE Annual International Meeting, Reno, NV, 2009, Paper No. 096364.
29. Huang, J F, Apan, A. Detection of sclerotinia rot disease on celery using hyperspectal data and partial least squares regression[J]. Journal of Spatial Science, 2006, 52(2): 129-142.
30. Bravo C, Moshou D, West J S, et al. Early disease detection in wheat fields using spectral reflectance[J]. Biosystems Engineering, 2003, 84(2): 137-145.
31. Zhao C J, Huang M Y, Huang W J, et al. Analysis of winter wheat stripe rust characteristic spectrum and establishing of inversion models[J]. IGARSS, 2004, 6, 4318-4320.
32. Huang Muyi, Huang Wenjiang, Liu Liangyun, et al. Spectral reflectance feature of winter wheat single leaf infected with stripe rust and severity level inversion[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2004, 20(1): 176-180.(in Chinese with English abstract)
33. Huang W J, Huang M Y, Liu L Y, et al. Inversion of the severity of winter wheat yellow rust using proper hyper spectral index[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2005, 21(4): 97-103.
34. Jiang Jinbao, Chen Yunhao, Huang Wenjiang, et al. Using hyperspectral derivative index to monitor winter wheat disease[J]. Spectroscopy and Spectral Analysis, 2007, 27(12): 2475-2479.(in Chinese with English abstract)
35. Huang W J, Lamb D W, Niu Z, et al. Identification of yellow rust in wheat using in-situ spectral reflectance measurements and airborne hyperspectral imaging[J]. Precision Agriculture, 2007, 8(5): 187-197.
36. Devadas R, Lamb D W, Simpfendorfer S, et al. Evaluating ten spectral vegetation indices for identifying rust infection in individual wheat leaves[J]. Precision Agriculture, 2009, 10(6): 459-470.
37. Steddom K, Heidel G, Jones D. Remote detection of rhizomania in sugar beets[J]. Phytopathology, 2003, 93(6): 720-726.
38. Liu Zhanyu, Shi Jingjing, Wang Dacheng, et al. Discrimination and spectral response characteristic of stress leaves infected by rice aphelenchoides besseyi christie[J]. Spectroscopy and Spectral Analysis, 2010, 30(3): 710-713.(in Chinese with English abstract)
39. Li Bo, Liu Zhanyu, Huang Jingfeng, et al. Hyperspectral identification of rice diseases and pests based on principalcomponent analysis and probabilistic neural network[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2009, 25(9): 143-147.(in Chinese with English abstract)
40. Liu Zhanyu, Sun Huasheng, Huang Jingfeng. Classification of empty and healthy panicles in rice plants by hyperspectral reflectance based on learning vector quantizati0n (LVQ) Neural Network[J]. Chinese Journal of Rice Science, 2007, 21(6): 664-668.(in Chinese with English abstract)
41. 2010.
42. Liu Zhanyu, Huang Jingfeng, Tao Rongxiang, et al. Estimating rice brown spot disease severity based on principalcomponent analysis and radial basis function neural network[J]. Spectroscopy and Spectral Analysis, 2008, 28(9): 2156-2160.(in Chinese with English abstract)
43. Shi Jingjing, Liu Zhanyu, Zhang Lili, et al. Hyperspectral recognition of rice damaged by rice leaf roller based on support vector machine[J]. Chinese Journal of Rice Science, 2009, 23(3): 331-334.(in Chinese with English abstract)
44. Li Bo, Liu Zhanyu, Wu Hongfeng, et al. Differentiation of rice panicles blast by hyperspectral remote sensing based on probabilistic neural network[J]. Bulletin of Science and Technology, 2009, 25(6): 811-815.(in Chinese with English abstract)
45. Liu Zhanyu, Wang Dacheng, Li Bo, et al. Discrimination of lodged rice based on visible/near infrared spectroscopy[J]. Journal of Infrared and Millimeter Waves, 2009, 28(5): 342-345.(in Chinese with English abstract)
46. Liu ZY, Shi JJ, Zhang LW, et al. Discrimination of rice panicles by hyperspectral reflectance data based on principalcomponent analysis and support vector classification[J]. Journal of Zhejiang University-Science B: Biomedicine and Biotechnology, 2010, 11(1): 71-78.
47. Costa G, Noferini M, Fiori G, et al. Innovative application of non-destructive techniques for fruit quality and disease diagnosis[J]. Acta Horticulturae, 2007, 753(1): 275-282.
48. Liu Z Y, Huang J F, Shi J J, et al. Characterizing and estimating rice brown spot disease severity using stepwise regression, principalcomponent regression and partial least-square regression[J]. Journal of Zhejiang University-Science B: Biomedicine and Biotechnology, 2007, 8(10): 738-744.
49. Jones C D, Jones J B, Lee W S. Diagnosis of bacterial spot of tomato using spectral signatures[J]. Computers and Electronics in Agriculture, 2010, 74(2): 329-335.
50. Rumpf T, Mahlein A K, Steiner U, et al. Early detection and classification of plant diseases with support vector machines based on hyperspectral reflectance[J]. Computers and Electronics in Agriculture, 2010, 74(1): 91-99.
51. Muhammed H H, Larsolle A. Feature vector based analysis of hyperspectral crop reflectance data for discrimination and quantification of fungal disease severity in wheat[J]. Biosystems Engineering, 2003, 86(2): 125-134.
52. Muhammed H H. Hyperspectral crop reflectance data for characterising and estimating fungal disease severity in wheat[J]. Biosystems Engineering, 2005, 91(1): 9-20.
53. Qin J, Burks T F, Kim M S, et al. Citrus canker detection using hyperspectral reflectance imaging and PCA-based image classification method[J]. Sensing and Instrumentation for Food Quality and Safety, 2008, 2(3): 168-177.
54. Sighicell M, Colao F, Lai A, et al. Monitoring post-harvest orange fruit disease by fluorescence and reflectance hyperspectral imaging[J]. ISHS Acta Horticulturae, 2009, 817: 277-284.
55. Yang C H, Everitt J H, Fernandes C J. Comparison of airborne multispectral and hyperspectral imagery for mapping cotton root rot[J]. Biosystems Engineering, 2010, 107(2): 131-139.
56. Jonas F, Menz G. Multi-temporal wheat disease detection by multi-spectral remote sensing[J]. Precision Agriculture, 2007, 8(3): 161-172.
57. Mishra A, Karimi D, Ehsani R, et al. Evaluation of an active optical sensor for detection of Huanglongbing (HLB) disease[J]. Biosystems Engineering, 2011, 110(3): 302-309.
58. Kuckenberg J, Tartachnyk I, Noga G. Detection and differentiation of nitrogen-deficiency, powdery mildew and leaf rust at wheat leaf and canopy level by laser-induced chlorophyll fluorescence[J]. Biosystems Engineering, 2009, 103(2): 121-128.
59. Römer C, Bürling K, Hunsche M, et al. Robust fitting of fluorescence spectra for pre-symptomatic wheat leaf rust detection with Support Vector Machines[J]. Computers and Electronics in Agriculture, 2011, 79(2): 180-188.
60. Stoll M, Schultz H R, Baecker G, et al. Early pathogen detection under different water status and the assessment of spray application in vineyards through the use of thermal imagery[J]. Precision Agriculture, 2008, 9(6): 407-417.
61. Morais R, Fernandes M A, Matos S G, et al. A ZigBee multi-powered wireless acquisition device for remote sensing applications in precision viticulture[J]. Computers and Electronics in Agriculture, 2008, 62(2): 94-106.
62. Zhang Jingcheng. Methods for Information Extraction of Wheat Disease Based on Multi-source Remote Sensing Data[D]. Hangzhou: College of Environment and Resources of Zhejiang University, 2012.