KI - Künstliche Intelligenz

, Volume 27, Issue 4, pp 313–324 | Cite as

Data Mining and Pattern Recognition in Agriculture

Technical Contribution

Abstract

Modern communication, sensing, and actuator technologies as well as methods from signal processing, pattern recognition, and data mining are increasingly applied in agriculture. Developments such as increased mobility, wireless networks, new environmental sensors, robots, and the computational cloud put the vision of a sustainable agriculture for anybody, anytime, and anywhere within reach. Yet, precision farming is a fundamentally new domain for computational intelligence and constitutes a truly interdisciplinary venture. Accordingly, researchers and experts of complementary skills have to cooperate in order to develop models and tools for data intensive discovery that allow for operation through users that are not necessarily trained computer scientists. We present approaches and applications that address these challenges and underline the potential of data mining and pattern recognition in agriculture.

References

  1. 1.
    Abdeen A, Schnell J, Miki B (2010) Transcriptome analysis reveals absence of unintended effects in drought-tolerant transgenic plants overexpressing the transcription factor abf3. BMC Genomics 11(69) Google Scholar
  2. 2.
    Agrios G (1997) Plant pathology, 4th edn. Academic Press, San Diego Google Scholar
  3. 3.
    Ballvora A, Römer C, Wahabzada M, Rascher U, Thurau C, Bauckhage C, Kersting K, Plümer L, Leon J (2013) Deep phenotyping of early plant response to abiotic stress using non-invasive approaches in barley. In: Zhang G, Li C, Liu X (eds) Advance in Barley sciences. Springer, Berlin, pp 301–316. Chap 26 Google Scholar
  4. 4.
    Bauckhage C (2006) Tree-based signatures for shape classification. In: Proc ICIP Google Scholar
  5. 5.
    Bauckhage C, Kersting K, Schmidt A (2012) Agriculture’s technological makeover. IEEE Pervasive Comput 11(2):4–7 CrossRefGoogle Scholar
  6. 6.
    Bechar I, Moisan S, Thonnat M, Bremond F (2010) On-line video recognition and counting of harmful insects. In: Proc ICPR Google Scholar
  7. 7.
    Bergamaschi S, Sala A (2009) Creating and querying an integrated ontology for molecular and phenotypic cereals data. In: Sicilia M, Lytras M (eds) Metadata and semantics. Springer, Berlin, p 445 CrossRefGoogle Scholar
  8. 8.
    Blanco P, Metternicht G, Del Valle H (2009) Improving the discrimination of vegetation and landform patterns in sandy rangelands: a synergistic approach. Int J Remote Sens 30(10):2579–2605 CrossRefGoogle Scholar
  9. 9.
    Boyer J (1982) Plant productivity and environment. Science 218:443–448 CrossRefGoogle Scholar
  10. 10.
    Burrell J, Brooke T, Beckwith R (2004) Vineyard computing: sensor networks in agricultural production. IEEE Pervasive Comput 3(1):38–45 CrossRefGoogle Scholar
  11. 11.
    Chakraborty S, Subramanian L (2011) Location specific summarization of climatic and agricultural trends. In: Proc WWW Google Scholar
  12. 12.
    Civril A, Magdon-Ismail M (2009) On selecting a maximum volume sub-matrix of a matrix and related problems. Theor Comput Sci 410(47–49):4801–4811 MathSciNetCrossRefMATHGoogle Scholar
  13. 13.
    Crowley M, Poole D (2011) Policy gradient planning for environmental decision making with existing simulators. In: Proc AAAI Google Scholar
  14. 14.
    Ebrahim Y, Ahmed M, Chau S, Abdelsalam W (2007) An efficient shape representation and description technique. In: Proc ICIP Google Scholar
  15. 15.
    Girard A, Rasmussen C, Quinonero Candela J, Murray-Smith R (2002) Gaussian process priors with uncertain inputs—application to multiple-step ahead time series forecasting. In: Proc NIPS Google Scholar
  16. 16.
    Gnomes C (2009) Computational sustainability: computational methods for a sustainable environment, economy, and society. The Bridge 39(4):5–13 Google Scholar
  17. 17.
    Gocht A, Roder N (2011) Salvage the treasure of geographic information in farm census data. In: Proc int Congress European association of agricultural economists Google Scholar
  18. 18.
    Golovin D, Krause A, Gardner B, Converse S, Morey S (2011) Dynamic resource allocation in conservation planning. In: Proc AAAI Google Scholar
  19. 19.
    Gonzales R, Woods R (2008) Digital image processing, 3rd edn. Pearson Prentice Hall, New York Google Scholar
  20. 20.
    Guo P, Baum M, Grando S, Ceccarelli S, Bai G, Li R, von Korff M, Varshney R, Graner A, Valkoun J (2010) Differentially expressed genes between drought-tolerant and drought-sensitive barley genotypes in response to drought stress during the reproductive stage. J Exp Bot 60(12):3531–3544 CrossRefGoogle Scholar
  21. 21.
    Hafiane A, Seetharaman G, Palaniappan K, Zavidovique B (2008) Rotationally invariant hashing of median binary patterns for texture classification. In: Proc ICIAR Google Scholar
  22. 22.
    Kersting K, Wahabzada M, Roemer C, Thurau C, Ballvora A, Rascher U, Leon J, Bauckhage C, Pluemer L (2012) Simplex distributions for embedding data matrices over time. In: Proc SDM Google Scholar
  23. 23.
    Kersting K, Xu Z, Wahabzada M, Bauckhage C, Thurau C, Römer C, Ballvora A, Rascher U, Leon J, Plümer L (2012) Pre-symptomatic prediction of plant drought stress using Dirichlet-aggregation regression on hyperspectral images. In: Proc AAAI Google Scholar
  24. 24.
    Kui F, Juan W, Weiqiong B (2011) Research of optimized agricultural information collaborative filtering recommendation systems. In: Proc ICICIS Google Scholar
  25. 25.
    Kumar V, Dave V, Bhadauriya R, Chaudhary S (2013) Krishimantra: agricultural recommendation system. In: Proc ACM symp on computing for development Google Scholar
  26. 26.
    Laykin S, Alchanatis V, Edan Y (2012) On-line multi-sateg sorting algorithm for agriculture products. Pattern Recognit 45(7):2843–2853 CrossRefGoogle Scholar
  27. 27.
    Lebreton C, Lazic-Jancic V, Steed A, Pekic S, Quarrie S (1995) Identification of qtl for drought responses in maize and their use in testing causal relationships between traits. J Exp Bot 46(7):853–865 CrossRefGoogle Scholar
  28. 28.
    Lin H, Cheng J, Pei Z, Zhang S, Hu Z (2009) Monitoring sugarcane growth using envisat asar data. IEEE Trans Geosci Remote Sens 47(8):2572–2580 CrossRefGoogle Scholar
  29. 29.
    Loew A, Ludwig R, Mauser W (2006) 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 CrossRefGoogle Scholar
  30. 30.
    McKay J, Richards J, Sen S, Mitchell-Olds T, Boles S, Stahl E, Wayne T, Juenger T (2008) Genetics of drought adaptation in arabidopsis thaliana ii. qtl analysis of a new mapping population, kas-1 × tsu-1. Evolution 62(12):3014–3026 CrossRefGoogle Scholar
  31. 31.
    Medjahed B, Gosky W (2009) A notification infrastructure for semantic agricultural web services. In: Sicilia M, Lytras M (eds) Metadata and semantics. Springer, Berlin, pp 455–462 CrossRefGoogle Scholar
  32. 32.
    Mewes T, Franke J, Menz G (2009) Data reduction of hyperspectral remote sensing data for crop stress detection using different band selection methods. In: Proc IEEE int geoscience and remote sensing symp Google Scholar
  33. 33.
    Mitchell T (1997) Machine learning. McGraw-Hill, New York MATHGoogle Scholar
  34. 34.
    Ojala T, Pietikäinen M, Mäenpää T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 24(7):971–987 CrossRefGoogle Scholar
  35. 35.
    Passioura J (2002) Environmental biology and crop improvement. Funct Plant Biol 29:537–554 CrossRefGoogle Scholar
  36. 36.
    Petrik M, Zilberstein S (2011) Linear dynamic programs for resource management. In: Proc AAAI Google Scholar
  37. 37.
    Pinnisi E (2008) The blue revolution, drop by drop, gene by gene. Science 320(5873):171–173 CrossRefGoogle Scholar
  38. 38.
    Rabbani M, Maruyama K, Abe H, Khan M, Katsura K, Ito Y, Yoshiwara K, Seki M, Shinozaki K, Yamaguchi-Shinozaki K (2010) 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 CrossRefGoogle Scholar
  39. 39.
    Rascher U, Nichol C, Small C, Hendricks L (2007) Monitoring spatio-temporal dynamics of photosynthesis with a portable hyperspectral imaging system. Photogramm Eng Remote Sens 73(1):45–56 CrossRefGoogle Scholar
  40. 40.
    Rascher U, Pieruschka R (2008) Spatio-temporal variations of photosynthesis: the potential of optical remote sensing to better understand and scale light use efficiency and stresses of plant ecosystems. Precis Agric 9(6):355–366 CrossRefGoogle Scholar
  41. 41.
    Rasmussen C, Williams C (2006) Gaussian processes for machine learning. MIT Press, Cambridge MATHGoogle Scholar
  42. 42.
    Rocha A, Hauagge D, Wainer J, Goldenstein S (2010) Automatic fruit and vegetable classification from images. Comput Electron Agric 70(1):96–104 CrossRefGoogle Scholar
  43. 43.
    Römer C, Bürling K, Rumpf T, Hunsche M, Noga G, Plümer L (2010) Robust fitting of fluorescence spectra for presymptomatic wheat leaf rust detection with support vector machines. Comput Electron Agric 74(1):180–188 Google Scholar
  44. 44.
    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 (2012) Early drought stress detection in cereals: simplex volume maximization for hyperspectral image analysis. Funct Plant Biol 39(11):878–890 CrossRefGoogle Scholar
  45. 45.
    Rumpf T, Mahlein AK, Steiner U, Oerke EC, Plümer L (2010) Early detection and classification of plant diseases with support vector machines based on hyperspectral reflectance. Comput Electron Agric 74(1):91–99 CrossRefGoogle Scholar
  46. 46.
    RußG, Brenning A (2010) Data mining in precision agriculture: management of spatial information. In: Proc IPMU Google Scholar
  47. 47.
    Sankaran S, Mishra A, Ehsani R, Davis C (2010) A review of advanced techniques for detecting plant diseases. Comput Electron Agric 72(1):1–13 CrossRefGoogle Scholar
  48. 48.
    Satalino G, Mattia F, Le Toan T, Rinaldi M (2009) Wheat crop mapping by using asar ap data. IEEE Trans Geosci Remote Sens 47(2):527–530 CrossRefGoogle Scholar
  49. 49.
    Schmitz M, Martini D, Kunisch M, Mosinger HJ (2009) Agroxml: enabling standardized, platform-independent Internet data exchange in farm management information systems. In: Sicilia M, Lytras M (eds) Metadata and semantics. Springer, Berlin, pp 463–467 CrossRefGoogle Scholar
  50. 50.
    Thurau C, Kersting K, Wahabzada M, Bauckhage C (2012) Descriptive matrix factorization for sustainability: adopting the principle of opposites. Data Min Knowl Discov 24(2):325–354 MathSciNetCrossRefMATHGoogle Scholar
  51. 51.
    Vernon R (ed) (2001) Knowing where you’re going: information systems for agricultural research management. International Service for Agricultural Research (ISNAR) (2001) Google Scholar
  52. 52.
    Wark T, Corke P, Klingbeil L, Guo Y, Crossman C, Valencia P, Swain D, Bishop-Hurley G (2007) Transforming agriculture through pervasive wireless sensor networks. IEEE Pervasive Comput 6(2):50–57 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.B-ITUniversity of BonnBonnGermany
  2. 2.IGGUniversity of BonnBonnGermany
  3. 3.Fraunhofer IAISSankt AugustinGermany

Personalised recommendations