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用端到端的多目标深度学习框架估计苗生长特征
Ziran Ye1, Xiangfeng Tan1, Mengdi Dai1
1Institute of Digital Agriculture, Zhejiang Academy of Agricultural Sciences, Hangzhou, China.
Frontiers in plant science
|June 19, 2023
概括
使用混合卷积神经网络 (CNN) 的新方法从数字图像中准确估计了大米苗的生长特征. 这种方法提供了一个快速的,非破坏性的工具,用于监测控制环境中的苗木发展.
科学领域:
- 农业科学 农业科学
- 计算机科学 计算机科学
- 植物科学 植物科学
背景情况:
- 在中国,越来越多地使用大米苗工厂,需要手工选择和田间移植.
- 通过高度和生物质等特征来精确量化大米苗的生长是至关重要的.
- 目前基于图像的植物表型化方法需要改进,以提高控制环境中的速度,稳定性和成本效益.
研究的目的:
- 开发和评估一种使用数字图像估计苗生长特征的新方法.
- 在受控植物工厂环境中解决现有表型化技术的局限性.
- 为提供一种快速,稳固和低成本的解决方案,用于对苗发展的非破坏性监测.
主要方法:
- 开发了一个利用混合卷积神经网络 (CNN) 的端到端框架.
- 该模型处理了彩色图像,缩放因子和图像采集距离作为输入.
- 进行图像细分,直接预测拍摄高度 (SH) 和拍摄新鲜重量 (SFW).
主要成果:
- 混合CNNs模型实现了高精度,R平方值为SH的0.980和SFW的0.717.
- 标准化根平均平方误差 (NRMSE) 值为SH的2.64%和SFW的17.23%.
- 拟议的模型表现优于传统的随机森林 (RF) 和回归CNN (RCNN) 模型.
结论:
- 混合CNN有效地学习数字图像和苗生长特征之间的关系.
- 这种方法提供了一个方便和灵活的工具,用于对苗木生长进行非破坏性监测.
- 该方法在控制环境下优化大米苗种生产方面显示出显著的前景.
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