Plant phenotyping (Guo et al. 2021; Pieruschka et al. 2019) focuses on studying the diverse traits of plants related to the plants' growth. To be more specific, by accurately measuring the plant's anatomical, ontogenetical, physiological and biochemical properties, it allows identifying the crucial factors of plants' growth in different environments. One commonly used approach is to predict the plant's traits using hyperspectral reflectance (Yendrek et al. 2017; Wang et al. 2021). However, the data distributions of the hyperspectral reflectance data in plant phenotyping might vary in different environments for different plants. That is, it would be computationally expansive to learn the machine learning models separately for one plant in different environments. To solve this problem, we focus on studying the knowledge transferability of modern machine learning models in plant phenotyping. More specifically, this work aims to answer the following questions. (1) How is the performance of conventional machine learning models, e.g., partial least squares regression (PLSR), Gaussian process regression (GPR) and multi-layer perceptron (MLP), affected by the number of annotated samples for plant phenotyping? (2) Whether could the neural network based transfer learning models improve the performance of plant phenotyping? (3) Could the neural network based transfer learning be improved by using infinite-width hidden layers for plant phenotyping?
翻译:植物洞察(Guo et al. 2021; Pieruschka et al. 2019) 侧重于研究植物与植物生长有关的各种特性。 更具体地说,通过精确测量植物的解剖、 原子、 生理和生化特性,可以辨别植物在不同环境中生长的关键因素。 一个常用的方法是使用超光谱反射法预测植物的特性(Yendrek et al. 2017; Wang et al. 2021) 。 然而,植物洞察中超光谱反射数据的数据分布可能在不同植物的隐蔽环境中有所不同? 也就是说,为不同环境中的某个植物分别学习机器学习模型将是广度的。 为了解决这个问题,我们侧重于研究植物洞察中现代机器学习模型的知识可转移性。 更具体地说,这项工作旨在回答下列问题:(1) 常规机器学习模型的性能表现如何,例如,部分平方回归(PLSR), 高频不位的植物洞察过程回归模型(GGPR) 和多层变换的植物网络, 以学习模型为基础,可以改进工厂的不断的变压的变压的变换的网络, 。