A labeled spectral dataset with cassava disease occurrences using virus titre determination protocol

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dc.contributor.authorOwomugisha, Godliver
dc.contributor.authorNakatumba-Nabende, Joyce
dc.contributor.authorDhikusooka, Joshua Jeremy
dc.contributor.authorTaravera, Estefania
dc.contributor.authorNuwamanya, Ephraim
dc.contributor.authorMwebaze, Ernest
dc.date.accessioned2023-07-26T06:52:06Z
dc.date.available2023-07-26T06:52:06Z
dc.date.issued2023
dc.descriptionData article
dc.description.abstractIn this work, we present a novel dataset composed of spectral data and images of cassava crops with and without diseases. Together with the description of the dataset, we describe the protocol to collect such data in a controlled environment and in an open field where pests are not controlled. Crop disease diagnosis has been done in the past through the analysis of plant images taken with a smartphone camera. However, in some cases, disease symptoms are not visible. Furthermore, for some cassava diseases, once symptoms have manifested on the aerial part of the plant, the root which is the edible part of the plant has been totally destroyed. The goal of collecting this multimodality of the crop disease is early intervention, following the hypothesis that diseased crops without visible symptoms can be detected using spectral information. We collected visible and near-infrared spectra captured from leaves infected with two common cassava diseases namely; Cassava Brown Streak Disease and Cassava Mosaic Disease, as well as from healthy plants. Together, we also captured leaf imagery data that corresponds to the spectral information. In our experiments, biochemical data is collected and taken as the ground truth. Finally, agricultural experts provided a disease score per plant leaf from 1 to 5, 1 representing healthy and 5 severely diseased. 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. Keywords: Spectral data protocol, Cassava diseases, Crop diagnosis, Smart agriculture, Early disease detection
dc.description.sponsorshipUniversity of Twente; National Crops Resources Research Institute; Makerere University; Busitema University
dc.identifier.citationOwomugisha, G., . . . [et al]. (2023). A labeled spectral dataset with cassava disease occurrences using virus titre determination protocol. Data in Brief, 109387.
dc.identifier.urihttps://doi.org/10.60682/bbsp-6k29
dc.language.isoen
dc.publisherElsevier
dc.subjectResearch Subject Categories::TECHNOLOGY::Information technology::Computer engineering
dc.titleA labeled spectral dataset with cassava disease occurrences using virus titre determination protocol
dc.typeArticle
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