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Understanding the adaptation process of plants to drought stress is essentialin improving management practices, breeding strategies as well as engineeringviable crops for a sustainable agriculture in the coming decades.Hyper-spectral imaging provides a particularly promising approach to gain suchunderstanding since it allows to discover non-destructively spectralcharacteristics of plants governed primarily by scattering and absorptioncharacteristics 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 oninterpretations of experts, and consider few wavelengths only. In this study,we present the first data-driven approach to discovering spectral droughtstress indices, treating it as an unsupervised labeling problem at massivescale. To make use of short range dependencies of spectral wavelengths, wedevelop an online variational Bayes algorithm for latent Dirichlet allocationwith convolved Dirichlet regularizer. This approach scales to massive datasetsand, hence, provides a more objective complement to plant physiologicalpractices. The spectral topics found conform to plant physiological knowledgeand can be computed in a fraction of the time compared to existing LDAapproaches.

Latent Dirichlet Allocation Uncovers Spectral Characteristics of Drought Stressed Plants