Cassava Spectral and Image Dataset

  • Owomugisha Godliver (Contributor)
  • M. Biehl (Contributor)
  • Nakatumba-Nabende Joyce (Contributor)
  • Ephraim Nuwamanya (Contributor)
  • Dalton Kanyesigye (Contributor)
  • Nicholas Muhumuza (Contributor)
  • Maurice Katusiime (Contributor)
  • Joshua Jeremy Dhikusooka (Contributor)
  • Tobius Saolo (Contributor)
  • Bamundaga Aloyzius (Contributor)
  • Joan Nabadda (Contributor)
  • Nakalyango Molly (Contributor)
  • Nahima Musa (Contributor)

Dataset

Description

We present a spectral dataset, procedures and steps we adopted to collect disease data in a controlled environment aiming at early disease detection in cassava. As a baseline, we extended these procedures to an open-field experiment. We collected visible and near-infrared spectra captured from leaves infected with two common cassava diseases. Together we collected plant image data from leaves where spectral data was captured. In this experiment, biochemical data was collected and taken as the ground truth. Finally, agricultural experts provided a disease score for each plant where data was collected. The process of disease monitoring and data collection took 19 and 15 consecutive weeks for screen house and open field respectively until disease symptoms were visibly seen by the human eye.
Date made available19-Jul-2022
PublisherHarvard Dataverse

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