Feeding theworld with big data: Uncovering spectral characteristics and dynamics of stressed plants

Studies in Computational Intelligence

Volumn 645, Issue , 2016, Pages 99-120

  Kersting, Kristian ^a   Bauckhage, Christian ^b   Wahabzada, Mirwaes ^b   Mahlein, Anne Kathrin ^b   Steiner, Ulrike ^b   Oerke, Erich Christian ^b   Römer, Christoph ^b   Plümer, Lutz ^b  

^a TU DORTMUND UNIVERSITY   (Germany)

^b UNIVERSITY OF BONN   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84990032618     PISSN: 1860949X     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-3-319-31858-5_6     Document Type: Chapter

References (69)

1. Abdeen, A., Schnell, J., Miki, B.: Transcriptome analysis reveals absence of unintended effects in drought-tolerant transgenic plants overexpressing the transcription factor ABF3. BMC Genomics 11(69) (2010)
2. Aitchison, J.: The Statistical Analysis of Compositional Data. Chapman and Hall, London (1986)
3. Arngren, M., Schmidt, M.N., Larsen, J.: Bayesian nonnegative matrix factorizationwith volume prior for unmixing of hyperspectral images. In: Proceedings of the IEEEWorkshop on Machine Learning for Signal Processing (MLSP), pp. 1-6 (2009)
4. Ballvora, A., Römer, C., Wahabzada, M., Rascher, U., Thurau, C., Bauckhage, C., Kersting, K., Plümer, L., Leon, J.: Deep phenotyping of early plant response to abiotic stress using noninvasive approaches in barley. In: Zhang, G., Li, C., Liu, X. (eds.) Advance in Barley Sciences, chapter 26, pp. 301-316. Springer (2013)
5. Bauckhage, C., Kersting, K., Schmidt, A.: Agriculture’s technological makeover. IEEE Pervasive Comput. 11(2), 4-7 (2012)
6. Bechar, I., Moisan, S., Thonnat, M., Bremond, F.: On-line video recognition and counting of harmful insects. In: Proceedings of the ICPR (2010)
7. Bergamaschi, S., Sala, A.: Creating and querying an integrated ontology for molecular and phenotypic cereals data. In: Sicilia, M.A., Lytras, M.D. (eds.) Metadata and Semantics, pp. 445-445. Springer (2009)
8. Blanco, P.D., Metternicht, G.I., Del Valle, H.F.: Improving the discrimination of vegetation and landform patterns in sandy rangelands: a synergistic approach. Int. J. Remote Sens. 30(10), 2579-2605 (2009)
9. Blumenthal, L.M.: Theory and Applications of Distance Geometry. Oxford University Press (1953)
10. Boyd, S., Vandenberghe, L.: Convex Optimization. Cambridge University Press (2004)
11. Boyer, J.S.: Plant productivity and environment. Science 218, 443-448 (1982)
12. Burrell, J., Brooke, T., Beckwith, R.: Vineyard computing: sensor networks in agricultural production. IEEE Pervasive Comput. 3(1), 38-45 (2004)
13. Çivril, A., Magdon-Ismail, M.: On selecting a maximum volume sub-matrix of a matrix and related problems. Theoret. Comput. Sci. 410(47-49), 4801-4811 (2009)
14. Çivril, A., Magdon-Ismail, M.: Column subset selection via sparse approximation of SVD. Theoret. Comput. Sci. (2011). (In Press). http://dx.doi.org/10.1016/j.tcs.2011.11.019
15. Chakraborty, S., Subramanian, L.: Location specific summarization of climatic and agricultural trends. In: Proceedings of the WWW (2011)
16. Cox, T., Cox, M.: Multidimensional Scaling. Chapman and Hall, London (1984)
17. Crowley, M., Poole, D.: Policy gradient planning for environmental decision making with existing simulators. In: Proceedings of the AAAI (2011)
18. Doyle, G., Elkan, C.: Financial topic models. In: Working Notes of the NIPS-2009 Workshop on Applications for Topic Models: Text and Beyond Workshop (2009)
19. Feng, P., Xiang, Z., Wei, W.: CRD: fast co-clustering on large datasets utilizing sampling based matrix decomposition. In: Proceedings of the ACM SIGMOD (2008)
20. Frieze, A., Kannan, R., Vempala, S.: Fast monte-carlo algorithms for finding lowrank approximations. J. ACM 51(6), 1025-1041 (2004)
21. Girard, A., Rasmussen, C.E., Quinonero Candela, J., Murray-Smith, R.: Gaussian process priors with uncertain inputs—application to multiple-step ahead time series forecasting. In: Proceedings of the NIPS (2002)
22. Gocht, A., Roder, N.: Salvage the treasure of geographic information in farm census data. In: Proceedings of the International Congress European Association of Agricultural Economists (2011)
23. Golovin, D., Krause, A., Gardner, B., Converse, S.J., Morey, S.: Dynamic resource allocation in conservation planning. In: Proceedings of the AAAI (2011)
24. Gomes, C.P.: Computational sustainability: computational methods for a sustainable environment, economy, and society. Bridge 39(4), 5-13 (2009)
25. Goreinov, S.A., Tyrtyshnikov, E.E.: The maximum-volume concept in approximation by lowrank matrices. In: DeTurck, D., Blass, A., Magid, A.R., Vogelius, M. (eds.) Contemporary Mathematics, vol. 280, pp. 47-51. AMS (2001)
26. Goreinov, S.A., Tyrtyshnikov, E.E., Zamarashkin, N.L.: A theory of pseudoskeleton approximations. Linear Algebra Appl. 261(1-3), 1-21 (1997)
27. Guo, P., Baum, M., Grando, S., Ceccarelli, S., Bai, G., Li, R., von Korff, M., Varshney, R.K., Graner, A., Valkoun, J.: Differentially expressed genes between drought-tolerant and droughtsensitive barley genotypes in response to drought stress during the reproductive stage. J. Exp. Bot. 60(12), 3531-3544 (2010)
28. György, A., Lugosi, G., Ottucsák, G.: On-line sequential bin packing. J. Mach. Learn. Res. 11, 89-109 (2010)
29. Hyvönen, S., Miettinen, P., Terzi, E.: Interpretable nonnegative matrix decompositions. In: ACM SIGKDD (2008)
30. Kailath, T.: The divergence and Bhattacharyya distance measures in signal selection. IEEE Trans. Commun. 15(1), 52-60 (1967)
31. Kersting, K., Wahabzada, M., Römer, C., Thurau, C., Ballvora, A., Rascher, U., Leon, J., Bauckhage, C., Plümer, L.: Simplex distributions for embedding data matrices over time. In: Proceedings of the SDM (2012)
32. Kersting, K., Xu, Z., Wahabzada, M., Bauckhage, C., Thurau, C., Römer, C., Ballvora, A., Rascher, U., Leon, J., Plümer, L.: Pre-symptomatic prediction of plant drought stress using dirichlet-aggregation regression on hyperspectral images. In: AAAI—Computational Sustainability and AI Track (2012)
33. Kersting, K., Xu, Z., Wahabzada, M., Bauckhage, C., Thurau, C., Römer, C., Ballvora, A., Rascher, U., Leon, J., Plümer, L.: Pre-symptomatic prediction of plant drought stress using dirichlet-aggregation regression on hyperspectral images. In: Proceedings of the AAAI (2012)
34. Kui, F., Juan, W., Weiqiong, B.: Research of optimized agricultural information collaborative filtering recommendation systems. In: Proceedings of the ICICIS (2011)
35. Kumar, V., Dave, V., Bhadauriya, R., Chaudhary, S.: Krishimantra: agricultural recommendation system. In: Proceedings of the ACM Symposium on Computing for Development (2013)
36. Laykin, S., Alchanatis, V., Edan, Y.: On-line multi-stage sorting algorithm for agriculture products. Pattern Recogn. 45(7), 2843-2853 (2012)
37. Lebreton, C., Lazic-Jancic, V., Steed, A., Pekic, S., Quarrie, S.A.: Identification of QTL for drought responses in maize and their use in testing causal relationships between traits. J. Exp. Bot. 46(7), 853-865 (1995)
38. Lin, H., Cheng, J., Pei, Z., Zhang, S., Hu, Z.: Monitoring sugarcane growth using envisat asar data. IEEE Trans. Geosci. Remote Sens. 47(8), 2572-2899 (2009)
39. Loew, A., Ludwig, R., Mauser, W.: Derivation of surface soil moisture from ENVISAT ASAR wide swath and image mode data in agricultural areas. IEEE Trans. Geosci. Remote Sens. 44(4), 889-899 (2006)
40. Mahoney, M.W., Drineas, P.: CUR matrix decompositions for improved data analysis. PNAS 106(3), 697-702 (2009)
41. McKay, J.K., Richards, J.H., Sen, S., Mitchell-Olds, T., Boles, S., Stahl, E.A., Wayne, T., Juenger, T.E.: Genetics of drought adaptation in Arabidopsis thaliana II. QTL analysis of a new mapping population, KAS-1 x TSU-1. Evolution 62(12), 3014-3026 (2008)
42. Medjahed, B., Gosky, W.: A notification infrastructure for semantic agricultural web services. In: Sicilia, M.A., Lytras, M.D. (eds.) Metadata and Semantics, pp. 455-462. Springer (2009)
43. Mewes, T., Franke, J., Menz, G.: Data reduction of hyperspectral remote sensing data for crop stress detection using different band selection methods. In: Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (2009)
44. Miao, L., Qi, H.: Endmember extraction from highly mixed data using minimum volume constrained nonnegative matrix factorization. IEEE Trans. Geosci. Remote Sens. 45(3), 765-777 (2007)
45. Neumann, M., Hallau, L., Klatt, B., Kersting, K., Bauckhage, C.: Erosion band features for cell phone image based plant disease classification. In: Proceedings of the 22nd International Conference on Pattern Recognition (ICPR-2014), pp. 3315-3320 (2014)
46. Passioura, J.B.: Environmental biology and crop improvement. Funct. Plant Biol. 29, 537-554 (2002)
47. Petrik, M., Zilberstein, S.: Linear dynamic programs for resource management. In: Proceedings of the AAAI (2011)
48. Pinnisi, E.: The blue revolution, drop by drop, gene by gene. Science 320(5873), 171-173 (2008)
49. Rabbani, M.A., Maruyama, K., Abe, H., Khan, M.A., Katsura, K., Ito, Y., Yoshiwara, K., Seki, M., Shinozaki, K., Yamaguchi-Shinozaki, K.: Monitoring expression profiles of rice genes under cold, drought, and high-salinity stresses and abscisic acid application using cDNA microarray and RNA gel-blot analyses. Plant Physiol. 133(4), 1755-1767 (2010)
50. Rascher, U., Nichol, C., Small, C., Hendricks, L.: Monitoring spatio-temporal dynamics of photosynthesis with a portable hyperspectral imaging system. Photogram. Eng. Remote Sens. 73(1), 45-56 (2007)
51. Rascher, U., Pieruschka, R.: Spatio-temporal variations of photosynthesis: the potential of optical remote sensing to better understand and scale light use efficiency and stresses of plant ecosystems. Precision Agric. 9(6), 355-366 (2008)
52. Rasmussen, C.E., Williams, C.K.I.: Gaussian Processes for Machine Learning. The MIT Press (2006)
53. Rocha, A., Hauagge, D.C., Wainer, J., Goldenstein, S.: Automatic fruit and vegetable classification from images. Comput. Electron. Agric. 70(1), 96-104 (2010)
54. Römer, C., Bürling, K., Rumpf, T., Hunsche, M., Noga, G., Plümer, L.: Robust fitting of fluorescence sprectra for presymptomatic wheat leaf rust detection with support vector machines. Comput. Electron. Agric. 74(1), 180-188 (2010)
55. Römer, C., Wahabzada, M., Ballvora, A., Pinto, F., Rossini, M., Panigada, C., Behmann, J., Leon, J., Thurau, C., Bauckhage, C., Kersting, K., Rascher, U., Plümer, L.: Early drought stress detection in cereals: simplex volume maximization for hyperspectral image analysis. Funct. Plant Biol. 39, 878-890 (2012)
56. Rumpf, T., Mahlein, A.-K., Steiner, U., Oerke, E.-C., Plümer, L.: Early detection and classification of plant diseases with support vector machines based on hyperspectral reflectance. Comput. Electron. Agric. 74(1), 91-99 (2010)
57. Ruß, G., Brenning, A.: Data mining in precision agriculture: management of spatial information. In: Proceedings of the IPMU (2010)
58. Sankaran, S., Mishra, A., Ehsani, R., Davis, C.: A review of advanced techniques for detecting plant diseases. Comput. Electron. Agric. 72(1), 1-13 (2010)
59. Satalino, G., Mattia, F., Le Toan, T., Rinaldi, M.: Wheat crop mapping by using ASAR AP data. IEEE Trans. Geosci. Remote Sens. 47(2), 527-530 (2009)
60. Schachtner, R., Pöppel, G., Tome, A.M., Lang, E.W.: Minimum determinant constraint for non-negative matrix factorization. In: ICA, pp. 106-113 (2009)
61. Schmitz, M., Martini, D., Kunisch, M., Mosinger, H.-J.: agroxml: enabling standardized, platform-independent internet data exchange in farm management information systems. In: Sicilia, M.A., Lytras, M.D. (eds.) Metadata and Semantics, pp. 463-467. Springer (2009)
62. Schweitzer, F., Fagiolo, G., Sornette, D., Vega-Redondo, F., Vespignani, A., White, D.R.: Economic networks: the new challenges. Science 5939(325), 422-425 (2009)
63. Sun, J., Xie, Y., Zhang, H., Faloutsos, C.: Less is more: compact matrix decomposition for large sparse graphs. In: SDM (2007)
64. Tenenbaum, J.B., De Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 5500(390), 2319-2323 (2000)
65. Thurau, C., Kersting, K., Wahabzada, M., Bauckhage, C.: Descriptive matrix factorization for sustainability: adopting the principle of opposites. DAMI 24(2), 325-354 (2012)
66. Thurau, C., Kersting, K., Wahabzada, M., Bauckhage, C.: Descriptive matrix factorization for sustainability: adopting the principle of opposites. J. Data Min. Knowl. Disc. 24(2), 325-354 (2012)
67. Vernon, R. (ed.): Knowing where you’re going: information systems for agricultural research management. Int. Serv. Agric. Res. (ISNAR) (2001)
68. Wahabzada, M., Mahlein, A.-K., Bauckhage, C., Steiner, U., Oerke, E.-C., Kersting, K.: Metro maps of plant disease dynamics—automated mining of differences using hyperspectral images. PLoS ONE 10(1) (2015)
69. Wark, T., Corke, P., Klingbeil, L., Guo, Y., Crossman, C., Valencia, P., Swain, D., Bishop-Hurley, G.: Transforming agriculture through pervasive wireless sensor networks. IEEE Pervasive Comput. 6(2), 50-57 (2007)