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BrinjalFruitX:用于机器学习和基于深度学习的布林果疾病识别的现场收集的图像数据集
Abu Kowshir Bitto1, Md Zahid Hasan2, Md Hasan Imam Bijoy1
1Department of Computer Science and Engineering, Daffodil International University, Dhaka 1216, Bangladesh.
Data in brief
|February 20, 2026
概括
为了帮助农业研究,创建了一个关于果疾病的新数据集. 该资源支持早期检测和精准农业,帮助农民减少损失并增加经济回报.
科学领域:
- 农业科学 农业科学
- 植物病理学 植物病理学
- 计算机视觉 计算机视觉
背景情况:
- (Solanum melongena) 是孟加拉国的重要作物,对农民的生计至关重要.
- 布林贾尔非常容易受到水果疾病的影响,导致大量的产量和经济损失.
- 现有的植物病病数据集缺乏对水果病的全面覆盖,特别是对于大.
研究的目的:
- 引入一套全新的,全面的数据集,专门用于果病.
- 解决有关果实特异性植物病理的现有数据集的差距.
- 促进深度学习模型的开发,用于自主检测疾病.
主要方法:
- 收集了1823个高质量,标记的布林果疾病图像.
- 包括五个不同的类别:Phomopsis Blight,射击和水果挖掘机,水果破裂,湿和健康的水果.
- 从孟加拉国各地的真实农场条件获取图像,以确保多样化的代表性.
主要成果:
- 开发了一个独特的数据集,1823年标记的图像,用于 brinjal 果病.
- 该数据集涵盖了五个关键疾病类别和健康样本.
- 通过从各种现实世界农业环境中收集数据,确保数据的稳定性.
结论:
- 该数据集支持植物疾病研究和深度学习模型的培训.
- 能够早期发现疾病,改善作物管理,减少农民的损失.
- 促进精准农业的进步,提高农民的经济回报.
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