An Interpretable Neural Network for Vegetation Phenotyping with Visualization of Trait-Based Spectral Features

TL;DR

This paper introduces an interpretable neural network trained on spectral data for plant species identification, revealing how spectral traits are learned and visualized, achieving around 90% accuracy and offering insights into vegetation traits.

Contribution

The work presents a novel interpretable neural network model that visualizes spectral trait indicators for plant identification, enhancing explainability over traditional black-box models.

Findings

Neurons learn spectral indicators for chemical and physiological traits.

The network achieves approximately 90% accuracy on test data.

Visualization reveals traits like chlorophyll composition and illumination response.

Abstract

Plant phenotyping is the assessment of a plant's traits and plant identification is the process of determining the category such as genus and species. In this paper we present an interpretable neural network trained on the UPWINS spectral library which contains spectra with rich metadata across variation in species, health, growth stage, annual variation, and environmental conditions for 13 selected indicator species and natural common background species. We show that the neurons in the network learn spectral indicators for chemical and physiological traits through visualization of the network weights, and we show how these traits are combined by the network for species identification with an accuracy around 90% on a test set. While neural networks are often perceived as `black box' classifiers, our work shows that they can be in fact more explainable and informative than other machine learning methods. We show that the neurons learn fundamental traits about the vegetation, for example the composition of different types of chlorophyll present which indicates species as well as response to illumination conditions. There is clear excess training capacity in our network, and we expect that as the UPWINS spectral library continues to grow the approach in this paper will provide further foundational insights in understanding plant traits. This provides a methodology for designing and interpreting neural networks on spectral data in general, and provides a framework for using neural networks with hyperspectral imagery for understanding vegetation that is extendable to other domains.