Development of methods to improve soybean yield estimation and predict plant maturity with an unmanned aerial vehicle based platform

Remote Sensing of Environment

Volumn 187, Issue , 2016, Pages 91-101

  Yu, Neil ^a   Li, Liujun ^a   Schmitz, Nathan ^a   Tian, Lei F ^a   Greenberg, Jonathan A ^a   Diers, Brian W ^a  

^a UNIVERSITY OF ILLINOIS AT URBANA CHAMPAIGN   (United States)

Author keywords

Breeding efficiency; Multispectral image; Object classification; Soybean; UAV

Indexed keywords

AMINO ACIDS; ARTIFICIAL INTELLIGENCE; CROPS; DECISION TREES; EFFICIENCY; FORECASTING; LEARNING SYSTEMS; NITROGEN FIXATION; SUPERVISED LEARNING;

HIGH-THROUGHPUT PHENOTYPING; LARGE SCALE EXPERIMENTS; MULTISPECTRAL IMAGES; OBJECT CLASSIFICATION; REAL WORLD ENVIRONMENTS; SOYBEAN; SOYBEAN [GLYCINE MAX (L.) MERR.]; SUPERVISED MACHINE LEARNING;

UNMANNED AERIAL VEHICLES (UAV);

AIRBORNE SURVEY; BREEDING POPULATION; DEMAND ANALYSIS; GENETIC ENGINEERING; GLOBAL PERSPECTIVE; IMAGE CLASSIFICATION; MACHINE LEARNING; MULTISPECTRAL IMAGE; NUMERICAL MODEL; PHENOLOGY; PREDICTION; SOYBEAN; YIELD RESPONSE;

GLYCINE MAX;

EID: 84991461437     PISSN: 00344257     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.rse.2016.10.005     Document Type: Article

References (56)

1. Ahamed, T., Tian, L., Jiang, Y., Zhao, B., Liu, H., Ting, K.C., Tower remote sensing system for monitoring energy crops; image acquisition and geometric corrections. Biosyst. Eng. 112 (2012), 93–107.
2. Aparicio, N., Villegas, D., Casadesus, J., Araus, J.L., Royo, C., Spectral vegetation indices as nondestructive tools for determining durum wheat yield. Agron. J. 92 (2000), 83–91.
3. Atzberger, C., Advances in remote sensing of agriculture: context description, existing operational monitoring systems and major information needs. Remote Sens. 5 (2013), 949–981.
4. Berman, M.E., DeJong, T.M., Water stress and crop load effects on fruit fresh and dry weights in peach (Prunus persica). Tree Physiol. 16 (1996), 859–864.
5. Bernard, R., The inheritance of near-gray pubescence color. Soybean Genetics Newsletter 2 (1975), 31–33.
6. Bivand, R., Pebesma, E., Gomez-Rubio, V., Classes and methods for spatial data in R. R News, 5, 2005.
7. Board, J., Kamal, M., Harville, B., Temporal importance of greater light interception to increased yield in narrow-row soybean. Agron. J. 84 (1992), 575–579.
8. Breiman, L., Random forests. Mach. Learn. 45 (2001), 5–32.
9. Cairns, J.E., Crossa, J., Zaidi, P., Grudloyma, P., Sanchez, C., Araus, J.L., Thaitad, S., Makumbi, D., Magorokosho, C., Bänziger, M., Identification of drought, heat, and combined drought and heat tolerant donors in maize. Crop Sci. 53 (2013), 1335–1346.
10. Daughtry, C., Walthall, C., Kim, M., de Colstoun, E., McMurtrey, J., Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance. Remote Sens. Environ. 74 (2000), 229–239.
11. Dinka, S.J., Campbell, M.A., Demers, T., Raizada, M.N., Predicting the size of the progeny mapping population required to positionally clone a gene. Genetics 176 (2007), 2035–2054.
12. Fehr, W., Caviness, C., Burmood, D., Penningt, J.S., Stage of development descriptions for soybeans, Glycine max (L.) Merrill. Crop Sci. 11 (1971), 929–931.
13. Gebhardt, S., Schellberg, J., Lock, R., Kühbauch, W., Identification of broad-leaved dock (Rumex obtusifolius L.) on grassland by means of digital image processing. Precis. Agric. 7 (2006), 165–178.
14. Gitelson, A., Gritz, Y., Merzlyak, M., Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves. J. Plant Physiol. 160 (2003), 271–282.
15. Goel, N.S., Models of vegetation canopy reflectance and their use in estimation of biophysical parameters from reflectance data. Remote Sens. Rev. 4:1 (1988), 1–212.
16. Gonzalez-Dugo, V., Hernandez, P., Solis, I., Zarco-Tejada, P.J., Using high-resolution hyperspectral and thermal airborne imagery to assess physiological condition in the context of wheat phenotyping. Remote Sens. 7 (2015), 13586–13605.
17. Guo, W., Fukatsu, T., Ninomiya, S., Automated characterization of flowering dynamics in rice using field-acquired time-series RGB images. Plant Methods, 11, 2015, 7.
18. Gwathmey, C.O., Tyler, D.D., Yin, X., Prospects for monitoring cotton crop maturity with normalized difference vegetation index. Agron. J. 102 (2010), 1352–1360.
19. Hall, F.G., Strebel, D.E., Nickeson, J.E., Goetz, S.J., Radiometric rectification: toward a common radiometric response among multidate, multisensor images. Remote Sens. Environ. 35 (1991), 11–27.
20. Hanrahan, P., Krueger, W., Reflection From Layered Surfaces due to Subsurface Scattering. Proceedings of the 20th Annual Conference on Computer Graphics and Interactive Techniques. ACM, 1993. 1993, 165–174.
21. Hatfield, J.L., Prueger, J.H., Value of using different vegetative indices to quantify agricultural crop characteristics at different growth stages under varying management practices. Remote Sens. 2 (2010), 562–578.
22. Hijmans, R.J., Raster: Geographic Data Analysis and Modeling. R Package Version 2.4.18. 2015.
23. Horning, N., Random Forests: An Algorithm for Image Classification and Generation of Continuous Fields Data Sets. Proceeding of International Conference on Geoinformatics for Spatial Infrastructure Development in Earth and Allied Sciences. 2010. 2010, 9–11.
24. Hunt, E.R., Jr. Hively, W.D., Fujikawa, S.J., Linden, D.S., Daughtry, C.S.T., McCarty, G.W., Acquisition of NIR-green-blue digital photographs from unmanned aircraft for crop monitoring. Remote Sens. 2 (2010), 290–305.
25. Ishwaran, H., Kogalur, U.B., Random Forests for Survival, Regression and Classification (RF-SRC). R Package Version 1.6.1. 2015.
26. Ishwaran, H., Kogalur, U.B., Blackstone, E.H., Lauer, M.S., Random Survival Forests. The Annals of Applied Statistics. 2008, 841–860.
27. Koester, R.P., Skoneczka, J.A., Cary, T.R., Diers, B.W., Ainsworth, E.A., Historical gains in soybean (Glycine max Merr.) seed yield are driven by linear increases in light interception, energy conversion, and partitioning efficiencies. J. Exp. Bot. 65 (2014), 3311–3321.
28. Kuhn, M., Caret: Classification and Regression Training. R Package Version 6.0.52. 2015.
29. Liebisch, F., Kirchgessner, N., Schneider, D., Walter, A., Hund, A., Remote, aerial phenotyping of maize traits with a mobile multi-sensor approach. Plant Methods, 11, 2015, 9.
30. Lobell, D., Asner, G., Law, B., Treuhaft, R., View angle effects on canopy reflectance and spectral mixture analysis of coniferous forests using AVIRIS. Int. J. Remote Sens. 23 (2002), 2247–2262.
31. Luis Araus, J., Cairns, J.E., Field high-throughput phenotyping: the new crop breeding frontier. Trends Plant Sci. 19 (2014), 52–61.
32. Ma, B., Dwyer, L.M., Costa, C., Cober, E.R., Morrison, M.J., Early prediction of soybean yield from canopy reflectance measurements. Agron. J. 93 (2001), 1227–1234.
33. Masuka, B., Araus, J.L., Das, B., Sonder, K., Cairns, J.E., Phenotyping for abiotic stress tolerance in Maize. J. Integr. Plant Biol. 54 (2012), 238–249.
34. Merzlyak, M.N., Gitelson, A.A., Chivkunova, O.B., Rakitin, V.Y., Non-destructive optical detection of pigment changes during leaf senescence and fruit ripening. Physiol. Plant. 106 (1999), 135–141.
35. Monteith, J.L., Climate and efficiency of crop production in Britain. Philosophical Transactions of the Royal Society of London Series B-Biological Sciences 281 (1977), 277–294.
36. Nijland, W., de Jong, R., de Jong, S.M., Wulder, M.A., Bater, C.W., Coops, N.C., Monitoring plant condition and phenology using infrared sensitive consumer grade digital cameras. Agric. For. Meteorol. 184 (2014), 98–106.
37. Penuelas, J., Gamon, J., Fredeen, A., Merino, J., Field, C., Reflectance indexes associated with physiological-changes in nitrogen-limited and water-limited sunflower leaves. Remote Sens. Environ. 48 (1994), 135–146.
38. Peters, J., de Baets, B., Verhoest, N.E.C., Samson, R., Degroeve, S., de Becker, P., Huybrechts, W., Random forests as a tool for ecohydrological distribution modeling. Ecol. Model. 207 (2007), 304–318.
39. Purcell, L.C., Soybean canopy coverage and light interception measurements using digital imagery. Crop Sci. 40 (2000), 834–837.
40. Robertson, M.J., Lyle, G., Bowden, J.W., Within-field variability of wheat yield and economic implications for spatially variable nutrient management. Field Crop Res. 105 (2008), 211–220.
41. Robson, A., Phinn, S., Wright, G., Fox, G., Combining Near Infrared Spectroscopy and Infrared Aerial Imagery for Assessment of Peanut Crop Maturity and Aflatoxin Risk. New Directions for a Diverse Planet, Proceedings of the 4th International Crop Science Congress, Brisbane, Australia. 2004, 16.
42. Rogers, S.B., Edzer, P., Virgilio, G., Applied Spatial Data Analysis with R. Second ed, 2013, Springer, New York.
43. Sankaran, S., Khot, L.R., Espinoza, C.Z., Jarolmasjed, S., Sathuvalli, V.R., Vandemark, G.J., Miklas, P.N., Carter, A.H., Pumphrey, M.O., Knowles, N.R., Pavek, M.J., Low-altitude, high-resolution aerial imaging systems for row and field crop phenotyping: a review. Eur. J. Agron. 70 (2015), 112–123.
44. Tester, M., Langridge, P., Breeding technologies to increase crop production in a changing world. Science 327 (2010), 818–822.
45. Thomson, M.J., High-throughput SNP genotyping to accelerate crop improvement. Plant Breeding and Biotechnology 2 (2014), 195–212.
46. Thorp, K.R., Steward, B.L., Kaleita, A.L., Batchelor, W.D., Using aerial hyperspectral remote sensing imagery to estimate corn plant stand density. Trans. ASABE 51 (2008), 311–320.
47. Turner, D., Lucieer, A., Watson, C., An automated technique for generating georectified mosaics from ultra-high resolution Unmanned Aerial Vehicle (UAV) imagery, based on Structure from Motion (SfM) point clouds. Remote Sens. 4 (2012), 1392–1410.
48. van Beijma, S., Comber, A., Lamb, A., Random forest classification of salt marsh vegetation habitats using quad-polarimetric airborne SAR, elevation and optical RS data. Remote Sens. Environ. 149 (2014), 118–129.
49. Wang, K., Franklin, S.E., Guo, X., Cattet, M., Remote sensing of ecology, biodiversity and conservation: a review from the perspective of remote sensing specialists. Sensors 10 (2010), 9647–9667.
50. White, J.W., Andrade-Sanchez, P., Gore, M.A., Bronson, K.F., Coffelt, T.A., Conley, M.M., Feldmann, K.A., French, A.N., Heun, J.T., Hunsaker, D.J., Jenks, M.A., Kimball, B.A., Roth, R.L., Strand, R.J., Thorp, K.R., Wall, G.W., Wang, G., Field-based phenomics for plant genetics research. Field Crop Res. 133 (2012), 101–112.
51. Wiesmeier, M., Barthold, F., Blank, B., Koegel-Knabner, I., Digital mapping of soil organic matter stocks using Random Forest modeling in a semi-arid steppe ecosystem. Plant Soil 340 (2011), 7–24.
52. Xiang, H., Tian, L., Development of a low-cost agricultural remote sensing system based on an autonomous unmanned aerial vehicle (UAV). Biosyst. Eng. 108 (2011), 174–190.
53. Yang, X., Lo, C., Relative radiometric normalization performance for change detection from multi-date satellite images. Photogramm. Eng. Remote Sens. 66 (2000), 967–980.
54. Zaman-Allah, M., Vergara, O., Araus, J.L., Tarekegne, A., Magorokosho, C., Zarco-Tejada, P.J., Hornero, A., Alba, A.H., Das, B., Craufurd, P., Olsen, M., Prasanna, B.M., Cairns, J.E., Unmanned aerial platform-based multi-spectral imaging for field phenotyping of maize. Plant Methods, 11, 2015, 35.
55. Zhang, C., Kovacs, J.M., The application of small unmanned aerial systems for precision agriculture: a review. Precis. Agric. 13 (2012), 693–712.
56. Zheng, L., Shi, D., Zhang, J., Segmentation of green vegetation of crop canopy images based on mean shift and fisher linear discriminant. Pattern Recogn. Lett. 31 (2010), 920–925.