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来自孟加拉国的卜植物叶病疾病分类智能手机图像数据集

Mahamudul Hasan¹, Raiyan Gani¹, Mohammad Rifat Ahmmad Rashid¹

¹Department of Computer Science and Engineering, East West University, Aftabnagar, Dhaka, Bangladesh.

|January 24, 2025

概括

准确识别卜叶病对于健康的作物生长至关重要. 本研究介绍了2801张图像的数据集,用于训练深度学习模型,以精确检测疾病,帮助可持续农业.

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:

卜是营养丰富的根蔬菜,但它们的生长受到各种细菌和真菌叶病的威胁.
传统的疾病识别方法容易出现不准确性,影响作物产量和质量.
早期和精确的疾病检测对于有效的管理和可持续的卜种植至关重要.

研究的目的:

开发一个强大的数据集,用于训练深度学习模型,以准确识别卜叶病.
为了促进精确的疾病诊断,从而最大限度地减少作物损失和提高卜生产.
通过改进疾病管理战略,为可持续的农业实践做出贡献.

主要方法:

一个多样化的数据集的集合,包括来自孟加拉国的2801张卜叶的图像.
包括代表健康叶子和四种常见疾病的图像:黑叶斑,唐尼,跳虫和马赛克.
使用此数据集来训练深度学习模型用于自动疾病识别.

主要成果:

创建了一个包括2801根卜叶图像的综合数据集,涵盖健康和生病样本.
该数据集可以训练深度学习模型,以准确分类卜叶病.
自动化疾病检测系统的潜力,以帮助农民及时干预.

结论:

开发的数据集是推动自动化卜叶病检测的宝贵资源.
通过深度学习准确识别疾病,可以显著减少作物损害,提高产量.
这项工作支持通过技术解决方案提高农业部门的可持续性这一更广泛的目标.

关键词:

农业的挑战农业的挑战数据集收集数据集的收集.疾病识别疾病识别图像分析图像分析叶子疾病叶子疾病卜植物叶病卜植物叶病

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Light Acquisition

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