  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?
