Integrated sensor system for monitoring rice growth conditions based on unmanned ground vehicle system

International Journal of Agricultural and Biological Engineering

Volumn 7, Issue 2, 2014, Pages 75-81

  Pei, Wang ^a   Lan, Yu Bin ^b   Luo, Xi Wen ^a   Zhou, Zhi Yan ^a   Wang, Zhigang ^b   Wang, Yonghui ^c  

^a SOUTH CHINA AGRICULTURAL UNIVERSITY   (China)

^b AGRICULTURAL RESEARCH SERVICE   (United States)

^c PRAIRIE VIEW A AND M UNIVERSITY   (United States)

Author keywords

Leaf area index (LAI); Multi sensors; Rice growth condition; Spectral vegetation indices; Unmanned ground vehicle system (UGVS)

Indexed keywords

INTELLIGENT VEHICLE HIGHWAY SYSTEMS; PLANTS (BOTANY); SENSORS; SPECTROMETERS; TESTING;

LEAF AREA INDEX; MULTI SENSOR; RICE GROWTHS; SPECTRAL VEGETATION INDICES; UNMANNED GROUND VEHICLES;

CROPS;

EID: 84899631855     PISSN: 19346344     EISSN: 19346352     Source Type: Journal    
DOI: 10.3965/j.ijabe.20140702.009     Document Type: Article

References (37)

1. Doraiswamy P C, Moulin S, Cook P W, Stern A. Crop yield assessment from remote sensing. Photogrammetric Engineering & Remote Sensing, 2003; 69(6): 665-674.
2. Zhang N, Taylor R K. Applications of a field-level geographic information system (FIS) in precision agriculture. Applied Engineering in Agriculture, 2001; 17(6): 885-892.
3. Pinter P J Jr, Hatfield J L, Schepers J S, Barnes E M, Moran M S, Daughtry C S T, et al. Remote sensing for crop management. Photogrammetric Engineering & Remote Sensing, 2003; 69(6): 647-664.
4. Stafford J V. Implementing precision agriculture in the 21st century. Journal of Agricultural Engineering Research, 2000; 76: 267-275.
5. Warren G, Metternicht G. Agricultural applications of high-resolution digital multispectral imagery: Evaluating within-field spatial variability of canola (Brassica napus) in Western Australia. Photogrammetric Engineering and Remote Sensing, 2005; 71: 595-602.
6. Lan Y, Thomson S J, Huang Y, Hoffmann W C, Zhang H. Current status and future directions of precision aerial application for site-specific crop management in the US.A. Computers and Electronics in Agriculture, 2010; 74: 34-38.
7. Moran M S, Inoue Y, Barnes E M. Opportunities and limitation for image-based remote sensing in precision crop Management. Remote Sensing of Environment, 1997; 61: 319-346.
8. Zhang J H, Wang K, Bailey J S, Wang R C. Predicting nitrogen status of rice using multispectral data at canopy scale. Pedosphere, 2006; 16, 108-117.
9. Berni J A J, Zarco-Tejada P J, Suarez L. Thermal and narrowband multispectral remote sensing for vegetation monitoring from an unmanned aerial vehicle. IEEE Transactions on Geoscience and Remote Sensing, 2009; 47: 722-738.
10. Rango A, Laliberte A S, Herrick J E, Winters C, Havstad K, Steele C. Unmanned aerial vehicle-based remote sensing for rangeland assessment, monitoring, and management. Journal of Applied Remote Sensing, 2009; 3(1): 33542.
11. Rango A, Laliberte A, Steele C, Jeffrey E H, Bestelmeyer B, Schmugge T, et al. Using unmanned aerial vehicles for rangelands: current applications and future potentials. Environmental Practice, 2006; 8: 159-168.
12. Li J Y, Zhang T M, Peng X D, Yan G Q, Chen Y. The application of small UAV (SUAV) in farmland information monitoring system. Journal of Agricultural Mechanization Research, 2010; (5): 183-186.
13. Luck J, Spackman M, Freeman A, Trebicki P, Griffiths W, Finlay K, et al. Climate change and diseases of food crops. Plant Pathology, 2011; 60: 113-121.
14. Zhang H, Yao X G, Zhang X B, Zheng K F. A primary study on nitrogen content of rice leaf based on remote sensing at spiking stage. Journal of Nuclear Agricultural Sciences, 2009; 23(3): 364-368.
15. Inoue Y. Synergy of remote sensing and modeling for estimating ecophysiological processes in plant production. Plant Production Science, 2003; 6(1): 3-16.
16. Chang K W, Shen Y, Lo J C. Predicting rice yield using canopy reflectance measured at booting stage. Agronomy Journal, 2005; 97: 872-878.
17. Jiang N, Li W G, Du P J. Application of different remote sensing data fusion methods to rice area monitoring in south area. Journal of Southwest University (Natural Science Edition), 2012; 34(6): 1-8.
18. Zhang H, Lan Y, Lacey R, Hoffmann W C, Huang Y. Analysis of vegetation indices derived from aerial multispectral and ground hyperspectral data. Int J Agric & Biol Eng, 2009; 2(3): 1-8.
19. Bauseh W C, Duke H. R. Remote sensing of plant nitrogen status in corn. Transactions of the ASAE, 1996; 39: 1869-1875.
20. Tumbo S D, Wagner D G, Heinemann P H. Hyper spectral-based neural network for predicting chlorophyll status in corn. Transactions of the ASAE, 2002; 45(3): 825-832.
21. Casanova D, Epema G F, Goudriaan J. Monitoring rice reflectance at field level for estimating biomass and LA.I. Field Crops Research, 1998; 55: 83-92.
22. Sun X M, Zhou Q F, He Q X. Hyperspectral variables in predictingleaf chlorophyll content and grain protein content in rice. Acta Agronomica Sinica, 2005; 31(7): 844-850.
23. Tian Y C, Yang J, Yao X, Zhu Y, Cao W X. Quantitative relationship between hyper-spectral vegetation indices and leaf area index of rice. Chinese Journal of Applied Ecology, 2009; 20 (7): 1685-1690.
24. Tian Y C, Yang J, Yao X, Cao W X, Zhu Y. Monitoring canopy leaf nitrogen concentration based on leaf hyper spectral indices in rice. Acta Agronomica Sinica, 2010; 36(9): 1529-1537.
25. Yao X, Tian Y C, Ni J, Zhang Y S, Cao W X, Zhu Y. Estimation of leaf pigment concentration in rice by near infrared reflectance spectroscopy. Analytical Chemistry, 2012; 40(4): 589-595.
26. Chen Q C, Tian Y C, Gu K J, Wang W, Yao X, Cao W X, et al. Monitoring plant nitrogen accumulation with different canopy spectrometers at early growth stages in rice. Transactions of the CSAE, 2011; 27(1): 223-229.
27. Tumbo S D, Wagner D G, Heinemann P H. On-the-go sensing of chlorophyll status in corn. Transactions of the ASAE, 2002; 45(4): 1207-1215.
28. Scotford I M, Miller P C H. Combination of spectral reflectance and ultrasonic sensing to monitor the growth of winter wheat. Biosystems Engineering, 2004; 87(1): 27-38.
29. Reyniers M, Vrindts E. Measuring wheat nitrogen status from space and ground-based platform. International Journal of Remote Sensing, 2006; 27(3): 549-567.
30. Lan Y, Zhang H, Lacey R, Hoffmann W C, Wu W F. Development of an integrated sensor and instrumentation system for measuring crop conditions. Agricultural Engineering International: the CIGR Ejournal, 2009; Manuscript IT 08 1115. Vol. 11.
31. Zhu H. The research of using the multi-source remote sensing data fusion for crop nitrogen monitoring and spraying control system. PhD Dissertation, Jilin University, 2011.
32. Castro-Esau K L, Sanchez-Azofeifa G A, Rivard B. Comparison of spectral indices obtained using multiple spectroradiometers. Remote Sens. Environ, 2006; 103: 276-288.
33. Tian Y C, Yang J, Yao X, Zhu Y, Cao W X. Quantitative relationship between hyper-spectral red edge position and canopy leaf nitrogen concentration in rice. Acta Agronomica Sinica, 2009; 35(9): 1681-1690.
34. Jones C L, Weckler P R, Maness N O, Jayasekara R, Stone M L, Chrz D. Remote sensing to estimate chlorophyll concentration in spinach using multi-spectral plant reflectance. Transactions of ASAE, 2007; 50(6): 2267-2273.
35. Bechtel A, Puttmann W, Carlson T N, Ripley D A. On the relation between NDVI, fractional vegetation cover, and leaf area index. Remote Sensing Environment, 1997; 62(3): 241-252.
36. Aparicio N, Villegas D, Araus J L, Casadeśs J, Royo C. Relationship between growth traits and spectral vegetation indices in durum wheat. Crop Science, 2002; 42: 1547-1555.
37. Leon C T, Shaw D R, Cox M S, Abshire M J, Ward B, Wardlaw M C. Utility of remote sensing in predicting crop and soil characteristics. Precision Agriculture, 2003; 4: 359-384.