Edge computing and RGB phenotyping enables machine learning-driven salinity monitoring in peppermint
论文结合边缘计算与RGB表型分析监测薄荷盐分，方法新颖，数据可靠，但时效性稍差。
本研究提出一种集成的表型分析框架,结合RGB成像、机器学习以及边缘计算功能的无线传感器网络,用于准确检测薄荷在0/100/200 mM NaCl浓度下的三种盐分胁迫程度。在10天内每天采集侧视图和正视图图像,采用RGB颜色信息+直方图颜色信息两种互补特征提取方法。正视图图像仅使用两个特征即可通过Fine KNN模型实现高达98.30%的分类精度;紧凑型配置下宽神经网络(内存占用仅0.010 MB)实现近乎完美的100%分类精度;SHAP可解释性分析显示盐分胁迫的典型特征为植被绿色度下降、色调变化增加以及冠层收缩。
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