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基于深度学习的子品种分类:使用定制的叶子图像数据集进行比较研究

Yonis Gulzar¹, Zeynep Ünal², Tefide Kızıldeniz²

¹Department of Management Information Systems, College of Business Administration, King Faisal University, Al-Ahsa, 31982, Saudi Arabia.

|December 9, 2024

概括

深度学习模型使用图像数据准确地分类草品种. 转移学习显著提高了准确性,DenseNet121和EfficientNetB3在植物分类方面取得了近乎完美的结果.

科学领域:

农业科学农业科学
计算机科学计算机科学
机器学习机器学习

背景情况:

深度学习模型提高了植物分类的准确性和效率.
准确的植物品种识别对于农业应用至关重要.

研究的目的:

使用深度学习技术对草植物品种进行分类.
为了比较各种最先进的深度学习模型对的分类的性能.

主要方法:

创建了一个自定义的数据集,包含三个子品种 (Bilensoy-80,Diana,Nimet) 的1,214张图像.
评估了几种深度学习模型 (MobileNetV3,InceptionV3,Xception,VGG19,DenseNet121,ResNet101,EfficientNetB3),并对这些模型进行了评估.
用各种超参数测试模型,包括学习率,批量大小和学配置.

主要成果:

转移学习通常会导致的分类测试准确度更高.
通过转移学习,DenseNet121实现了1.0000的准确性.
EfficientNetB3通过从头开始和转移学习方法实现了0.9945的准确性.

结论:

转移学习显著提高了模型在植物分类任务中的性能.
深度学习模型,特别是DenseNet121和EfficientNetB3,显示了准确识别草品种的巨大潜力.
开发的数据集是未来植物分类研究的宝贵资源.

关键词:

阿尔法尔法植物植物的植物人工智能的人工智能对比深度学习模型评估图像的分类图像的分类.模型评估模型评估植物分类植物分类转移学习学习转移学习

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