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太阳花:太阳花图像的现象分类的一个深度神经网络
Sofia A Bengoa Luoni1, Riccardo Ricci2, Melanie A Corzo3
1Laboratory of Genetics, Wageningen University & Research, 6708 PB Wageningen, The Netherlands.
Plants (Basel, Switzerland)
|July 27, 2024
概括
深度学习使用手机图像准确评估向日的现象阶段. 命名为Sunpheno的resnet50模型通过客观地跟踪叶子衰老来增强产量预测.
科学领域:
- 农业科学 农业科学
- 计算机科学 计算机科学
- 植物生物学 植物生物学
背景情况:
- 叶子衰老对于谷物填充和产量至关重要,需要与植物现象学阶段同步.
- 精确监测现象阶段对于优化作物产量至关重要.
研究的目的:
- 用现场图像评估深度机器学习方法来评估日的现象阶段.
- 开发一个准确和高效的模型来跟踪叶子衰老及其对产量的影响.
主要方法:
- 五个深度机器学习模型使用手机向日图像进行了测试.
- 预先训练的resnet50网络被确定为表现最好的模型.
- 创建了一个数据库,包含5000张专家分类的向日图像.
主要成果:
- 基于resnet50的模型 (Sunpheno) 在评估现象阶段方面表现出卓越的准确性和速度.
- 在日线B481_6和R453之间在衰老过程中观察到明显的现象学差异.
- 生成的模型减少了对叶子衰老的现场评估的主观性.
结论:
- 深度学习,特别是resnet50模型,提供了一个强大的解决方案,用于客观地评估向日的现象学阶段.
- Sunpheno模型可以帮助将衰老参数与作物性能和产量相关联.
- 基于图像的标准化表型化可以改善农业研究和实践中的决策.
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