Combining ultrasonic sward height and spectral signatures to assess the biomass of legume-grass swards

Computers and Electronics in Agriculture

Volumn 99, Issue , 2013, Pages 236-247

  Fricke, Thomas ^a   Wachendorf, Michael ^a  

^a UNIVERSITY OF KASSEL   (Germany)

Author keywords

Grassland; Precision farming; Ultrasonic sensor; Vegetation index; Yield

Indexed keywords

BINARY MIXTURES; BIOMASS; SENSORS; ULTRASONIC SENSORS; VEGETATION;

GRASSLAND; PRECISION FARMING; PREDICTION ACCURACY; SPECTRAL SIGNATURE; SPECTRAL VEGETATION INDICES; ULTRASONIC DISTANCE SENSORS; VEGETATION INDEX; YIELD;

FORECASTING;

ACCURACY ASSESSMENT; BIOMASS; ESTIMATION METHOD; GRASS; GROWTH RATE; HEIGHT DETERMINATION; RADIOMETRIC METHOD; SENSOR; SPECTRAL ANALYSIS; ULTRASONICS; VEGETATION INDEX;

LOLIUM PERENNE; MEDICAGO SATIVA; TRIFOLIUM PRATENSE; TRIFOLIUM REPENS;

EID: 84886865428     PISSN: 01681699     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compag.2013.10.004     Document Type: Article

References (60)

1. Andújar D., Weis M., Gerhards R. An ultrasonic system for weed detection in cereal crops. Sensors 2012, 12:17343-17357.
2. Asner G.P. Biophysical and biochemical sources of variability in canopy reflectance. Remote Sensing of Environment 1998, 64:234-253.
3. Bannari A., Morin D., Bonn F., Huete A.R. A review of vegetation indices. Remote Sensing Reviews 1995, 13(1-2):95-120.
4. Biewer S., Fricke T., Wachendorf M. Determination of dry matter yield from legume/grass swards by field spectroscopy. Crop Science 2009, 49:1927-1936.
5. Blackburn G.A. Quantifying chlorophylls and carotenoids at leaf and canopy scales: an evaluation of some hyperspectral approaches. Remote Sensing of Environment 1998, 66:273-285.
6. Boschetti M., Bocchi S., Brivio P.A. Assessment of pasture production in the Italian Alps using spectrometric and remote sensing information. Agriculture, Ecosystems and Environment 2007, 118:267-272.
7. Broge N.H., Leblanc E. Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density. Remote Sensing of Environment 2000, 76:156-172.
8. Chen J., Song G.U., Miaogen S., Yanhong T., Matsushita B. Estimating aboveground biomass of grassland having a high canopy cover: an exploratory analysis of in situ hyperspectral data. International Journal of Remote Sensing 2009, 30(24):6497-6517.
9. Cozzolino D., Fassio A., Fernandez E., Restaino E., La Manna A. Measurement of chemical composition in wet whole maize silage by visible and near infrared reflectance spectroscopy. Animal Feed Science and Technology 2006, 129:329-336.
10. Ehlert D., Hammen V., Adamek R. On-line sensor pendulum-meter for determination of plant mass. Precision Agriculture 2003, 4:139-148.
11. Ehlert D., Horn H.-J., Adamek R. Measuring crop biomass density by laser triangulation. Computers and Electronics in Agriculture 2008, 61:117-125.
12. Elvidge C.D., Lyon R.J.P. Influence of rock-soil spectral variation on the assessment of green biomass. Remote Sensing of Environment 1985, 17:265-279.
13. Erasmi, S., Dobers, E.S., 2004. Potential and limitations of spectral reflectance measurements for the estimation of the site specific variability in crops. p. 42-51. In: Owe, M., D'Urso, G. (Eds.), Remote Sensing for Agriculture, Ecosystems, and Hydrology V: Proc. of SPIE Vol. 5232. Barcelona, Spain. 8 September 2003. SPIE, Bellingham, WA.
14. Farooque A.A., Chang Y.K., Zaman Q.U., Groulx D., Schumann A.W., Esau T.J. Performance evaluation of multiple ground based sensors mounted on a commercial wild blueberry harvester to sense plant height, fruit yield and topographic features in real-time. Computers and Electronics in Agriculture 2013, 91:135-144.
15. Ferwerda J.G., Skidmore A.K., Mutanga O. Nitrogen detection with hyperspectral normalized ratio indices across multiple plant species. International Journal of Remote Sensing 2005, 26:4083-4095.
16. Flynn E.S., Dougherty C.T., Wendroth O. Assessment of pasture biomass with the normalized difference vegetation index from active ground-based sensors. Agronomy Journal 2008, 100:114-121.
17. Fricke T., Richter F., Wachendorf M. Assessment of forage mass from grassland swards by height measurement using an ultrasonic sensor. Computer and Electronics in Agriculture 2011, 79:142-152.
18. Gebbers R., Ehlert D., Adamek R. Rapid mapping of the leaf area index in agricultural crops. Agronomy Journal 2011, 103(5):1532-1541.
19. Goel N.S. Inversion of canopy reflectance models for estimation of biophysical parameters from reflectance data. Theory and Applications of Optical Remote Sensing 1989, 205-251. Wiley & Sons, New York. G. Asrar (Ed.).
20. Hansen P.M., Schjoerring J.K. Reflectance measurement of canopy biomass and nitrogen status in wheat crops using normalized diff erence vegetation indices and partial least squares regression. Remote Sensing of Environment 2003, 86:542-553.
21. Heege H.J., Reusch S., Thiessen E. Prospects and results for optical systems for site-specific on-the-go control of nitrogen-top-dressing in Germany. Precision Agriculture 2008, 9:115-131.
22. Henning B., Prange S., Dierks K., Daur P.-C. Ultrasonic sensor properties characterized by a PC-controlled scanning measuring system. Ultrasonics 2000, 38:852-856.
23. Hewlett Packard, 2012: Data sheet: High performance T 1 3/4 (58mm), TS AlGaAs Infrared (875nm) Lamp.
24. Huete A.R., Jackson R.D., Post D.F. Spectral response of a plant canopy with different soil backgrounds. Remote Sensing of Environment 1985, 17:37-54.
25. Hutchings N.J. Spatial heterogeneity and other sources of variance in sward height as measured by the sonic and HFRO sward sticks. Grass and Forage Science 1991, 46(3):277-282.
26. Hutchings N.J. Factors affecting sonic sward stick measurements: the effect of different leaf characteristics and the area of sward sampled. Grass and Forage Science 1992, 47(2):153-160.
27. Hutchings N.J., Phillips A.H., Dobson R.C. An ultrasonic rangefinder for measuring the undisturbed surface height of continuously grazed grass swards. Grass and Forage Science 1990, 45(2):119-127.
28. 2 flux measurements in rice. Remote Sensing of Environment 2008, 112:156-172.
29. Jacqemoud S., Bacour C., Poilvé H., Frangi J.-P. Comparison of four radiative transfer models to simulate plant canopy reflectance: direct and inverse mode. Remote Sensing of Environment 2000, 74:471-481.
30. Joggi D., Hofer U., Nösberger J. Leaf area index, canopy structure and photosythesis of red clover (Trifolium pratense L.). Plant, Cell and Environment 1983, 6(8):611-616.
31. Jones, C.L., Maness, N.O., Stone, M.L., Jayasekara, R., 2004. Sonar and digital imagery for estimating crop biomass. In: 2004 ASAE/CSAE Annual International Meeting, Fairmont Chateau Laurier, The Westin, Government Centre, Ottawa, Ontario, Canada, 1-4 August 2004.
32. Kawamura K., Mackay A.D., Tuohy M.P., Betteridge K., Sanches I.D., Inoue Y. Potential for spectral indices to remotely sense phosphorus and potassium content of legume-based pasture as a means of assessing soil phosphorus and potassium fertility status. International Journal of Remote Sensing 2011, 32(1):103-124.
33. Kayser M., Müller J., Isselstein J. Nitrogen management in organic farming: comparison of crop rotation residual effects on yields, N leaching and soil conditions. Nutrient Cycling in Agroecosystems 2010, 87:21-31.
34. Laidlaw A.S., Withers J.A. Changes in contribution of white clover to canopy structure in perennial ryegrass/white clover swards in response to N fertilizer. Grass and Forage Science 1998, 53(1):287-291.
35. Lan Y., Zhang H., Lacey R., Hoffmann W.C., Wu W. Development of an integrated sensor and instrumentation system for measuring crop conditions. Agricultural Engineering International: the CIGR Ejournal 2009, XI(April). (Manuscript IT 08 1115).
36. Lee W.S., Alchanatis V., Yang C., Hirafuji M., Moshou D., Li C. Sensing technologies for precision specialty crop production. Computers and Electronics in Agriculture 2010, 74:2-33.
37. Llorens J., Gil E., Llop J., Escolà A. Ultrasonic and LIDAR sensors for electronic canopy characterization in vineyards: advances to improve pesticide application methods. Sensors 2011, 11:2177-2194.
38. Mahmood H.S., Hoogmoed W.B., van Henten E.J. Sensor data fusion to predict multiple soil properties. Precision Agriculture 2012, 10.1007/s11119-012-9280-7.
39. Mutanga O., Skidmore A.K. Hyperspectral band depth analysis for a better estimation of grass biomass (Cenchrus ciliaris) measured under controlled laboratory conditions. International Journal of Applied Earth Observation and Geoinformation 2004, 5:87-96.
40. Nelder J.A. The statistics of linear models: back to the basics. Statistics and Computing 1994, 4:221-234.
41. Nelder J.A., Lane P.W. The computer analysis of factorial experiments. In memoriam: Frank Yates. American Statistician 1995, 49:382-385.
42. Numata I., Roberts D.A., Chadwick O.A., Schimel J., Sampaio F.R., Leonidas F.C., Soares J.V. Characterization of pasture biophysical properties and the impact of grazing intensity using remotely sensed data. Remote Sensing of Environment 2007, 109:314-327.
43. Park R.S., Gordon F.J., Agnew R.E., Barnes R.J., Steen R.W.J. The use of near infrared reflectance spectroscopy on dried samples to predict biological parameters of grass silage. Animal Feed Science Technology 1997, 68:235-246.
44. Pepperl-Fuchs, 2013. Documents: UC2000-30GM-IUR2-V15, Instruction leaflet. <> (accessed 13.06.13). http://www.pepperl-fuchs.co.uk/great_britain/en/classid_186.htm?view=productdetails-@@-prodid=4221#107280.
45. Psomas A., Kneubühler M., Huber S., Itten K., Zimmermann N.E. Hyperspectral remote sensing for estimating aboveground biomass and for exploring species richness patterns of grassland habitats. International Journal of Remote Sensing 2011, 32(24):9007-9031.
46. Radtke P.J., Boland H.T., Scaglia G. An evaluation of overhead laser scanning to estimate herbage removals in pasture quadrats. Agricultural and Forest Meteorology 2010, 150(12):1523-1528.
47. Reusch S. Use of ultrasonic transducers for on-line biomass estimation in winter wheat. Proceedings Precision Agriculture '09 2009, 169-175. Wageningen Academic Publishers. E.J. van Henten, D. Goense, D. Lokhorst (Eds.).
48. Robertson M.J., Llewellyn R.S., Mandel R., Lawes R., Bramley R.G.V., Swift L., Metz N., O'Callaghan C. Adoption of variable rate fertiliser application in the Australian grains industry: status, issues and prospects. Precision Agriculture 2012, 13:181-199.
49. Rouse, J.W., Hass, R.H., Shell, J.A. and Deering, D.W., 1974: Monitoring vegetation systems in the great plains with ERTS-1. In: Proceedings 3rd Earth Resources Technology Satellite Symposium, 10-14 December 1974, Washington, DC (Washington: NASA), vol. 1, pp. 309-317.
50. Sanderson M.A., Soder K.J., Brzezinski N., Taube F., Klement K., Muller L.D., Wachendorf M. Sward structure of simple and complex mixtures of temperate forages. Agronomy Journal 2006, 98:238-244.
51. Savitzky A., Golay M.J.E. Smoothing and differentiation of data by simplified least squares procedures. Analals of Chemistry 1964, 36:1627-1639.
52. Schellberg J., Hill M.J., Gerhards R., Rothmund M., Braun M. Precision agriculture on grassland: applications, perspectives and constraints. European Journal of Agronomy 2008, 29(2-3):59-71.
53. 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.
54. Serrano L., Filella I., Peñuelas J. Remote sensing of biomass and yield of winter wheat under different nitrogen supplies. Crop Science 2000, 40:723-731.
55. Terhoeven-Urselmans T., Michel K., Helfrich M., Flessa H., Ludwig B. Nearinfrared spectroscopy can predict the composition of organic matter in soil and litter. Journal of Plant Nutrition and Soil Science 2006, 169:168-174.
56. Thenkabail P.S., Smith R.B., De Pauw E. Hyperspectral vegetation indices and their relationships with agricultural crop characteristics. Remote Sensing of Environment 2000, 71:158-182.
57. Vescovo L., Wohlfahrt G., Balzarolo M., Pilloni S., Sottocornola M., Rodeghiero M., Gianelle D. New spectral vegetation indices based on the near-infrared shoulder wavelengths for remote detection of grassland phytomass. International Journal of Remote Sensing 2011, 33(7):2178-2195.
58. Virkajärvi P. Effects of defoliation height on regrowth of timothy and meadow fescue in generative and vegetative phases of growth. Agriculture and Food Science in Finland 2003, 12:177-193.
59. Weiser R.L., Asrar G., Miller G.P., Kanemasu E.T. Assessing grassland biophysical characteristics from spectral measurements. Remote Sensing of Environment 1986, 20:141-152.
60. Zaman Q.U., Esau T.J., Schumann A.W., Percival D.C., Chang Y.K., Read S.M., Farooque A.A. Development of prototype automated variable rate sprayer for real-time spot-application of agrochemicals in wild blueberry fields. Computers and Electronics in Agriculture 2011, 76:175-182.