International Provenance and Annotation Workshop

IPAW 2014: Provenance and Annotation of Data and Processes pp 229-231 | Cite as

Early Discovery of Tomato Foliage Diseases Based on Data Provenance and Pattern Recognition

  • Diogo Nunes
  • Carlos Werly
  • Gizelle Kupac Vianna
  • Sérgio Manuel Serra da Cruz
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8628)


This work presents an approach focused in enhancing the quality of tomato crops. We are developing and using low cost computational strategies to support early detection of the late blight. Our approach consorts tomatoes cultivars in an experimental field with inexpensive computer-aided resources based on Web and Android mobile tools in which workers collect scouting data and annotations and take images about the state of the crop, and in image filtering techniques and pattern recognition to detect foliage diseases on tomatoes images. In this study, we use provenance metadata about field observations, images and farmers’ annotations as well, to improve the efficiency and accuracy of the patterns recognition algorithms. Our identification method achieved a hit rate of 94.12 %, using a reduced set of digital images of the tomato crops.


Provenance Pattern recognition Neural networks 



We are grateful by the financial support provided by FAPERJ (E-26/112.588/2012 and E-26/110.928/2013 and FNDE-MEC-SeSU.


  1. 1.
  2. 2.
    IBGE - Contas Nacionais Trimestrais Indicadores de Volume e Valores Correntes. (2013)
  3. 3.
    Nakano, O.: As pragas das hortaliças: seu controle e o selo verde. Horticultura Brasileira, Vol. 17, n.1 UnB (1999)Google Scholar
  4. 4.
    Correa, F.M., Bueno Filho, J.S.S., Carmo, M.G.F.: Comparison of three diagrammatic keys for the quantification of late blight in tomato leaves. Plant Pathol. 58, 1128–1133 (2009)CrossRefGoogle Scholar
  5. 5.
    Vianna, G.K., Cruz, S.M.S.: Análise Inteligente de Imagens Digitais no Monitoramento da Requeima em Tomateiros. Anais do IX Congresso Brasileiro de Agroinformática. Cuiabá, MT (2013)Google Scholar
  6. 6.
    Cruz, S.M.S., Campos, M.L.M., Mattoso, M.: Towards a taxonomy of provenance in scientific workflow management systems. In: Proceedings of the SERVICES 2009 Congress, pp. 259–266, Los Angeles (2009)Google Scholar
  7. 7.
    Bishop, C.M.: Neural Networks for Pattern Recognition. Oxford University Press, New York (1995)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Diogo Nunes
    • 1
  • Carlos Werly
    • 1
  • Gizelle Kupac Vianna
    • 1
  • Sérgio Manuel Serra da Cruz
    • 1
    • 2
    • 3
  1. 1.UFRRJ – Universidade Federal Rural do Rio de JaneiroSeropédicaBrazil
  2. 2.PPGMMC/UFRRJ – Programa de Pós Graduação Modelagem Matemática e ComputacionalSeropédicaBrazil
  3. 3.PET-SI/UFRRJ – Programa de Educação Tutorial-Sistemas de InformaçãoSeropédicaBrazil

Personalised recommendations