rice fields in the Mekong Delta region using a mobile phone. Data source location Provinces in the Mekong Delta Latitude: 10.063363, Longitude: 105.594339 Data accessibility Repository name: Bacterial Grain Rot Dataset Caused by Burkholderia Glumae Bacteria Data identification number: 10.5281/zenodo.10805462 Direct URL to data: https://zenodo.org/records/10805462 Guidance on retrieving this dataset: Individuals may obtain the dataset by downloading it from the provided link and then unzipping the files for use. Related research article Quach, Luyl-Da, et al. “Evaluating the Effectiveness of YOLO Models in Different Sized Object Detection and Feature-Based Classification of Small Objects
Open resource ↗zenodo · 10.5281/zenodo.10805462 · lines:1-57Unverified paper record
Grain rot dataset caused by Burkholderia Glumae Bacteria.
Data in brief · 16 Mar 2024 · 10.1016/j.dib.2024.110334
Abstract
The Burkholderia glumae bacterium causes bacterial grain rot in rice, posing significant threats to the crop's yield, particularly thriving during the rice flowering and grain filling stages. This disease is especially evident in rice grains before harvest, presenting challenges in the detection and classification of rice panicles. Firstly, diseased grains may mix with healthy ones, complicating their separation. Secondly, the size of grains on a panicle varies from small to large, which can be problematic when detected using object detection methods. Thirdly, disease classification can be conducted by evaluating the extent of infection on rice panicles to assess its impact on yield. Finally, the challenges in detection, classification, and preprocessing for disease identification and management necessitate the adoption of diverse approaches in machine learning and deep learning to develop optimal methods and support smart agriculture.
Plant phenotyping relevance
イネ穂・粒の病徴を画像で検出・分類するデータセットであり、植物の病害状態を推定するフェノタイピング用途が中心です。
titleGrain rot dataset caused by Burkholderia Glumae Bacteria.
abstractdisease classification can be conducted by evaluating the extent of infection on rice panicles to assess its impact on yield.
abstractthe challenges in detection, classification, and preprocessing for disease identification and management necessitate the adoption of diverse approaches in machine learning and deep learning to develop optimal methods
Code and data availability
This Data in Brief article describes its own publicly deposited rice grain rot image dataset (1528 annotated images, YOLO format) on Zenodo, with explicit direct URL and DOI, qualifying as a paper-specific public phenotyping image dataset.
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