关于轻量级模型用于大米品种分类的应用及其在边缘计算中的潜力
Muhammad Javed Iqbal1, Muhammad Aasem1, Iftikhar Ahmad2
1Department of Computer Science, University of Engineering and Technology Taxila, Taxila 47050, Pakistan.
Foods (Basel, Switzerland)
|November 14, 2023
概括
这项研究引入了三种有效的深度学习模型来分类大米品种,减少了10%的计算需求,并减少了对像图像分析等任务的专家投入.
科学领域:
- 农业科学 农业科学
- 计算机科学 计算机科学
- 数据科学数据科学数据科学
背景情况:
- 米的分类对于工业和营销应用至关重要.
- 传统的机器学习和深度学习模型存在,但资源密集.
- 现有的深度学习模型通常需要重要的专家监督功能工程.
研究的目的:
- 开发计算效率高的深度学习模型来对大米品种进行分类.
- 尽量减少在预处理和特征工程中需要专家监督的需要.
- 为了实现与现有的最先进型号相匹配的性能,降低了开销.
主要方法:
- 关于三种基于图像的米类分类新型深度学习模型的建议.
- 使用端到端方法的培训模型来简化工作流程.
- 基于准确性和计算开销的模型评估.
主要成果:
- 拟议的模型表现出与现有最先进的方法可比的性能.
- 与当前最好的模型相比,计算开销减少了10%.
- 成功地分类了五种不同的水品种:阿尔博里奥,巴斯马蒂,伊普萨拉,莉花和卡拉卡达格.
结论:
- 开发的深度学习模型为米品种分类提供了有效的替代方案.
- 端到端的培训方法减少了需要广泛的专家干预的必要性.
- 这些模型显示了在边缘和移动设备上部署自主现场应用的潜力.
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