ArticleData in brief2023
Comprehensive dataset of annotated rice panicle image from Bangladesh.
Article in Data in brief, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- BDFlower: Growth stage flower image dataset for precision agriculture and floriculture.Data in brief · 2026Article
- An integrated edge AI prototype for smart agriculture: real-time pest detection, physical trapping, and multi-node deployment analysis under field uncertainty.Frontiers in plant science · 2026Article
- Smartphone image dataset for machine learning-based monitoring and analysis of mango growth stages.Data in brief · 2025Article
- PaddyVarietyBD: Classifying paddy variations of Bangladesh with a novel image dataset.Data in brief · 2025Article
- FabricSpotDefect: An annotated dataset for identifying spot defects in different fabric types.Data in brief · 2024Article
- One to All: Toward a Unified Model for Counting Cereal Crop Heads Based on Few-Shot Learning.Plant phenomics (Washington, D.C.) · 2024Article
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Authors and funding
10 authors.
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Abstract
Bangladesh's economy is primarily driven by the agriculture sector. Rice is one of the staple food of Bangladesh. The count of panicles per unit area serves as a widely used indicator for estimating rice yield, facilitating breeding efforts, and conducting phenotypic analysis. By calculating the number of panicles within a given area, researchers and farmers can assess crop density, plant health, and prospective production. The conventional method of estimating rice yields in Bangladesh is time-consuming, inaccurate, and inefficient. To address the challenge of detecting rice panicles, this article provides a comprehensive dataset of annotated rice panicle images from Bangladesh. Data collection was done by a drone equipped with a 4 K resolution camera, and it took place on April 25, 2023, in Bonkhoria Gazipur, Bangladesh. During the day, the drone captured the rice field from various heights and perspectives. After employing various image processing techniques for curation and annotation, the dataset was generated using images extracted from drone video clips, which were then annotated with information regarding rice panicles. The dataset is the largest publicly accessible collection of rice panicle images from Bangladesh, consisting of 2193 original images and 5701 augmented images.
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