ected for 19 and 15 consecutive weeks respectively. Data source location The dataset is in two major groups: screenhouse and open field experiment, collected for 19 and 15 consecutive weeks respectively. Data accessibility Repository name: Harvard Dataverse Data identification number: doi: 10.7910/DVN/R0KL7R Direct URL to data: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/R0KL7R Related research article Godliver Owomugisha, Ephraim Nuwamanya, John A. Quinn, Michael Biehl, and Ernest Mwebaze. 2020. Early detection of plant diseases using spectral data. In Proceedings of the 3rd International Conference on Applications of Intelligent Systems (APPIS 2020). Associatio
Open resource ↗Harvard Dataverse · doi:10.7910/DVN/R0KL7R · lines:1-54Unverified paper record
A labeled spectral dataset with cassava disease occurrences using virus titre determination protocol.
Data in brief · 9 Jul 2023 · 10.1016/j.dib.2023.109387
Abstract
In 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 screenhouse and open field, respectively, until disease symptoms were visibly seen by the human eye.
Plant phenotyping relevance
カッサバ病害の症状・病態を対象に、スペクトルと画像を収集した再利用可能なデータセットを構築し、収集プロトコル、専門家による病害スコア、地上真値を記述しているため、植物表現型取得が中心である。
abstractwe present a novel dataset composed of spectral data and images of cassava crops with and without diseases.
abstractTogether with the description of the dataset, we describe the protocol to collect such data in a controlled environment and in an open field
abstractagricultural experts provided a disease score per plant leaf from 1 to 5, 1 representing healthy and 5 severely diseased.
Code and data availability
The paper is a Data in Brief article describing a publicly deposited cassava spectral/leaf-image dataset with biochemical and expert-score labels, hosted on Harvard Dataverse with an explicit DOI and direct URL. This is the paper's own phenotyping data (spectra, leaf images, RT-PCR ground truth, expert scores), so it's
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