一个全面的马拉巴菜数据集,用于疾病分类
Mushfiqur Rahman1, Md Al Mamun1
1Department of Computer Science and Engineering, Daffodil International University, Dhaka, Bangladesh.
Data in brief
|April 25, 2025
概括
这项研究开发了机器视觉算法来检测马拉巴菜中的疾病,达到94%的准确性. 这有助于提高作物管理和粮食安全这一重要的蔬菜.
科学领域:
- 农业科学 农业科学
- 计算机视觉 计算机视觉
- 植物病理学 植物病理学
背景情况:
- 马拉巴拉菜是一种有价值的作物,面临着诸如炭病和虫感染等疾病威胁.
- 有限的研究存在于马拉巴菜的自动疾病检测.
- 机器视觉的进步为作物中早期疾病的识别提供了潜力.
研究的目的:
- 开发和评估机器视觉算法,以准确检测马拉巴菜中的疾病.
- 为了解决这个特定作物的自动疾病识别的研究缺口.
- 为精准农业和有效的作物管理提供工具.
主要方法:
- 利用了健康和生病的马拉巴拉西班牙菜图像的全面数据集.
- 经过培训和验证的深度学习模型,包括ResNet50.
- 采用各种图像处理技术和标准化的评估指标.
主要成果:
- 开发了Malabar菜的可靠疾病检测模型.
- 在疾病识别方面,获得了94%的整体测试准确度.
- 证明了机器视觉在检测特定疾病方面的有效性.
结论:
- 开发的模型为农民和专家提供了有效的工具来检测马拉巴菜疾病.
- 这项研究通过减少产量损失,有助于农业可持续性和粮食安全.
- 加强疾病检测支持精准农业,并加强农业社区.
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