一种混合机器视觉和手工制造的功能融合基于细粒小米分类的方法
B Baisakh1,2, Prabira Kumar Sethy3, Richa Gupta4
1Department of Computer Science and Engineering, SUIIT, Sambalpur University, Sambalpur, 768019, Odisha, India.
Scientific reports
|December 16, 2025
概括
准确识别气候适应性小米,如巴尼亚德,小米和普罗索小米,至关重要. 这项研究引入了混合框架,结合了手工制作的功能和深度学习,实现了小米分类的96%以上准确性.
科学领域:
- 农业科学 农业科学
- 计算机视觉 计算机视觉
- 数据科学数据科学数据科学
背景情况:
- 千叶是全球粮食安全的重要气候适应性,营养丰富的作物.
- 由于有限的注释数据集,视觉上相似的小米品种 (Barnyard,Little,Proso) 的准确分类具有挑战性.
研究的目的:
- 开发和评估用于准确识别小米品种的新型混合框架.
- 解决现有的方法在分类视觉上相似的千叶的局限性.
主要方法:
- 开发了一种定制的手工特征提取工艺.
- 提出了两个混合框架: 1) 堆叠集团机器学习 (SVM,RF,KNN与后勤回归元学习器) 和 2) 双向深度学习 (将手工制作的功能与VGG19融合).
主要成果:
- 堆叠组合模型实现了97%的分类准确度.
- 双向深度学习模型实现了96.7%的准确性,0.967的F1得分和0.995的ROC-AUC.
- 两种模型都显示了培训和推断时间之间的优秀权衡.
结论:
- 结合手工特征和深度学习的混合方法对于小米分类是有效的.
- 这些框架为识别气候适应性谷物提供了强大的解决方案,支持粮食和营养安全.
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