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改进的深度信念网络用于估计果质量指数和分级:基于计算机视觉的中性学方法
Mukesh Kumar Tripathi1, Shivendra2
1Department of Computer Science & Engineering, Vardhaman College of Engineering, Hyderabad, Telangana India.
概括
本研究介绍了一种新的机器学习系统,用于使用中微镜图像处理进行自动化果质量分级. 该系统准确预测可溶性固体总量 (TSS),以改进水果分类.
科学领域:
- 农业科学 农业科学
- 计算机科学 计算机科学
- 图像处理 图像处理
背景情况:
- 准确的果质量评估对于水果行业至关重要.
- 传统的分级方法可能是主观的,劳动密集型的.
- 开发自动化系统可以提高水果分类的效率和客观性.
研究的目的:
- 引入一种新的机器学习算法,用于自动化果质量估计和分级.
- 开发一个能够预测总溶性固体 (TSS) 的系统,用于分级.
- 与传统方法相比,评估拟议系统的有效性.
主要方法:
- 图像预处理包括消除噪音和对比度增强 (CLAHE).
- 使用基于几何平均值的方法将图像转化为中性学域.
- 使用改进的深度信念网络 (IDBN) 进行特征提取和质量分级.
- 考虑到SSC,坚固性和TAC的TSS预测.
主要成果:
- 拟议的系统证明了有效的自动化果分类.
- 中性变换增强了对图像进行分级的分析.
- IDBN模型在各种冷却条件下准确地预测TSS.
- 该系统的性能与传统分级技术相比得到了验证.
结论:
- 开发的机器学习系统为自动化果质量分级提供了强大的解决方案.
- 与IDBN结合的中性图像处理显示了农业应用的巨大潜力.
- 这种方法为手工水果分类提供了一个客观而有效的替代方案.
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