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香LSD:香叶图像数据集用于使用机器学习对香叶疾病的分类

Shifat E Arman¹, Md Abdullahil Baki Bhuiyan², Hasan Muhammad Abdullah³

¹Department of Robotics and Mechatronics Engineering, University of Dhaka, Dhaka-1000, Bangladesh.

|October 12, 2023

概括

一个关于香叶斑病的新数据集被创建,以帮助农民早期发现西加托卡,科尔达纳和佩斯塔洛提奥普西斯. 这个香叶斑病 (BananaLSD) 数据集有助于开发用于疾病识别的机器学习模型.

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计算机视觉

背景情况:

香种植面临着巨大的经济损失,原因是叶子斑点疾病,如西加托卡,科尔达纳和Pestalotiopsis.
早期发现这些疾病对于有效管理和产量保存至关重要.

研究的目的:

引入香叶斑病 (BananaLSD) 数据集,这是一个用于机器学习的全面图像集合.
促进开发用于识别关键香叶斑点疾病的自动化系统.

主要方法:

收集了937张香叶的图像,这些香叶受到西加托卡,科尔达纳和佩斯塔洛提奥普西斯的影响.
增加了数据集,产生了额外的1600张图像.
使用智能手机摄像机在各种现实场景条件下获取的图像.

主要成果:

香LSD数据集共包含2537个图像 (937个原始图像 + 1600个增强图像).
该数据集捕捉了不同环境和照明条件下的疾病变异.
该数据集被用来开发BananaSqueezeNet模型.

结论:

香LSD数据集为训练机器学习模型用于早期发现香疾病提供了宝贵的资源.
它可以显著帮助农民识别植物疾病,从而减少作物损失.
该数据集鼓励进一步研究农业计算机视觉和疾病诊断.

关键词:

香的叶子香的叶子计算机视觉计算机视觉计算机视觉深度学习是一种深度学习.疾病检测检测疾病检测图像的分类图像的分类.机器学习机器学习植物病理学植物病理学

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