Segmenting and Detecting Nematode in Coffee Crops Using Aerial Images

  • Alexandre J. OliveiraEmail author
  • Gleice A. AssisEmail author
  • Vitor GuiziliniEmail author
  • Elaine R. FariaEmail author
  • Jefferson R. SouzaEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11754)


A challenge in precision agriculture is the detection of pests in agricultural environments. This paper describes a methodology to detect the presence of the nematode pest in coffee crops. An Unmanned Aerial Vehicle (UAV) is used to obtain high-resolution RGB images of a commercial coffee plantation. The proposed methodology enables the extraction of visual features from image regions and uses supervised machine learning (ML) techniques to classify areas into two classes: pests and non-pests. Several learning techniques were compared using approaches with and without segmentation. Results demonstrate the methodology potential, with an average for the f-measure of 63% for Convolutional Neural Network (U-net) with manual segmentation.


Pest detection Coffee crops UAV Machine learning 



The Titan Xp used for this research was donated by the NVIDIA Corporation. This work was supported by the Federal University of Uberlandia, CNPq scholarship (process number 163641/2018-8) and CNPq under Grant 400699/2016-8.


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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Faculty of Computer ScienceFederal University of UberlandiaUberlândiaBrazil
  2. 2.Institute of Agricultural SciencesFederal University of UberlandiaUberlândiaBrazil
  3. 3.Toyota Research InstituteLos AltosUSA

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