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IDBGL:用于疾病检测和分类的黑色 (Vigna mungo) 叶的独特图像数据集

Md Mehedi Hasan Shoib¹, Shahnewaz Saeem¹, Afia Benta Aziz Tonima¹

¹Multidisciplinary Action Research Lab,Department of Computer Science and Engineering, Daffodil International University, Daffodil Smart City, Birulia, Dhaka 1216, Bangladesh.

|February 24, 2025

概括

这项研究引入了来自孟加拉国的黑色 (Vigna mungo) 叶片图像的新数据集,用于训练用于早期疾病检测的深度学习模型. 这将有助于改善作物管理,提高农民的产量.

科学领域:

农业科学农业科学
计算机科学计算机科学
植物病理学植物病理学

背景情况:

黑 (Vigna mungo) 是孟加拉国重要的豆类作物,对于营养和收入至关重要.
叶病显著影响黑色的产量,需要有效的检测方法.
当前的疾病识别挑战阻碍了及时的作物管理,增加了产量损失.

研究的目的:

来自孟加拉国的Black Gram叶片图像开发一个独特的数据集.
为了促进深度学习模型的创建,用于自动早期检测和黑色格拉姆叶病的分类.
用先进的计算工具支持研究人员,农民和农业利益相关者.

主要方法:

收集了来自孟加拉国Sirajganj和Solonga地区的4,038张黑色叶的图像.
将图像分为五个类别:健康,Cercospora叶子斑,昆虫,叶子纹和黄色马赛克.
为培训和验证深度学习模型建立了一个新的数据集.

主要成果:

创建了一个包含4038张图像的综合数据集,代表了各种各样的黑色格兰叶状况.
数据集包括健康样本和四个常见疾病类别.
该资源可用于开发用于疾病识别的自动化系统.

结论:

创建的黑色格拉姆叶病数据集是农业研究的宝贵资源.
在这个数据集上训练的深度学习模型可以帮助早期发现和管理疾病.
该项目旨在通过技术进步和提高利益相关者的意识来加强黑色种植.

关键词:

深度学习是一种深度学习.疾病检测检测疾病检测机器学习是机器学习.维格纳果 (Vigna mungo) 是一种植物.

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