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通过使用深度学习和图像处理来评估果的质量特征

Nhan H Nguyen¹, Joseph Michaud², Rene Mogollon^1,3

¹Department of Horticulture Washington State University Pullman WA USA.

|October 10, 2024

概括

开源工具Granny使用机器学习和图像处理来客观地评估果的质量,减少评级者偏见并改善果和梨的数据分辨率.

科学领域:

园艺科学园艺科学
农业技术农业技术
计算机视觉计算机视觉

背景情况:

果的质量评估对于收获时间和储存监测至关重要.
传统的视觉评估是主观的,容易产生评价者偏见,缺乏客观标准.
在不同实验室和实验中确保质量评级的一致性是一项挑战.

研究的目的:

介绍"奶奶",一个用于客观地评估果质量的新工具.
解决传统视觉评估方法的局限性,包括评级者偏差和低分辨率.
为了提供与已建立的质量评级标准的向后兼容性.

主要方法:

使用机器学习和图像处理算法进行水果质量分析.
自动评估粉含量,皮肤缺陷和皮肤颜色.
设计用于客观和高分辨率的水果质量数据收集.

主要成果:

奶奶可以有效地减少水果质量评估中的评级者偏见.
该工具提供了更好的分辨率来分析果实属性,如粉含量和皮肤特征.
提供与现有标准兼容的评级,确保研究的连续性.

结论:

奶奶提高了果质量评估的客观性和分辨率.
通过将水果质量数据与omics和环境因素联系起来,促进整合性分析.
奶奶的开源可用性促进了果实科学的更广泛采用和研究进步.

关键词:

机器学习是机器学习.这种果实是果果.预测特征预测特征

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