用数字图像和机器学习方法估计甘的气营养含量.
Hui You1, Muchen Zhou1, Junxiang Zhang2
1College of Mechanics, Guangxi University, 100 East University Road, Nanning, 530004, Guangxi, China.
Scientific reports
|September 11, 2023
概括
这项研究表明,将甘叶的颜色和纹理特征与机器学习模型相结合,可以准确地估计含量. 这种数字图像分析提供了一种快速,非破坏性的方法来优化肥料管理.
科学领域:
- 农业科学 农业科学
- 植物生理学 植物生理学
- 数字图像处理 数字图像处理
背景情况:
- 农作物营养状况,特别是 (N),对于产量至关重要,可以从叶子特征中推断出来.
- 优化化肥管理对于可持续农业和环境保护至关重要.
研究的目的:
- 开发和验证机器学习模型,用数字图像分析来估计甘叶的含量.
- 根据颜色特征,纹理特征和集成特征来比较模型的性能.
主要方法:
- 采集了使用数码相机在耕作和延长阶段采集的甘叶图像.
- 使用数字图像处理提取的颜色特征 (CF) 和纹理特征 (TF).
- 应用主要组件分析 (PCA) 来减少特征维度.
- 开发了使用多重线性回归 (MLR),随机森林 (RF) 和堆叠融合模型 (SFM) 的N含量估计模型.
主要成果:
- 整合色彩和纹理主要组件特征 (C-T-PCA) 的模型表现优于单一特征模型.
- SFM实现了最高的精度,R2值为0.9264 (废料) 和0.9111 (延长).
- 与其他模型相比,SFM在准确性方面取得了显著的改进.
结论:
- 基于C-T-PCA的SFM框架有效地提高了模型性能,防干扰和概括能力.
- 数字图像处理与机器学习相结合,提供了一种快速而非破坏性的方法来评估作物营养.
- 这种方法有助于在甘中优化肥管理.
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