Latent Dirichlet Allocation Uncovers Spectral Characteristics of Drought Stressed Plants

TL;DR

This paper introduces a scalable, data-driven method using latent Dirichlet allocation to identify spectral characteristics of drought-stressed plants from hyper-spectral imaging data, enhancing understanding and management.

Contribution

It presents the first unsupervised, large-scale spectral index discovery approach using an online variational Bayes LDA with a convolved Dirichlet regularizer, scalable to massive datasets.

Findings

Spectral topics align with plant physiological knowledge.

Method is faster than existing LDA approaches.

Provides an objective tool for plant drought stress analysis.

Abstract

Understanding the adaptation process of plants to drought stress is essential in improving management practices, breeding strategies as well as engineering viable crops for a sustainable agriculture in the coming decades. Hyper-spectral imaging provides a particularly promising approach to gain such understanding since it allows to discover non-destructively spectral characteristics of plants governed primarily by scattering and absorption characteristics of the leaf internal structure and biochemical constituents. Several drought stress indices have been derived using hyper-spectral imaging. However, they are typically based on few hyper-spectral images only, rely on interpretations of experts, and consider few wavelengths only. In this study, we present the first data-driven approach to discovering spectral drought stress indices, treating it as an unsupervised labeling problem at massive scale. To make use of short range dependencies of spectral wavelengths, we develop an online variational Bayes algorithm for latent Dirichlet allocation with convolved Dirichlet regularizer. This approach scales to massive datasets and, hence, provides a more objective complement to plant physiological practices. The spectral topics found conform to plant physiological knowledge and can be computed in a fraction of the time compared to existing LDA approaches.