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bioRxiv · 10.1101/2022.12.15.520316

Towards Cloud-Native, Machine Learning Based Detection of Crop Disease with Imaging Spectroscopy

Rubambiza, G.·Romero Galvan, F. E.·Pavlick, R. P.·Weatherspoon, H.·Gold, K.
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Abstract

Developing actionable early detection and warning systems for agricultural stakeholders is crucial to reduce the annual $200B USD losses and environmental impacts associated with crop diseases. Agricultural stakeholders primarily rely on labor-intensive, expensive scouting and molecular testing to detect disease. Spectroscopic imagery (SI) can improve plant disease management by offering decision-makers accurate risk maps derived from Machine Learning (ML) models. However, training and deploying ML requires significant computation and storage capabilities. This challenge will become even greater as global scale data from the forthcoming Surface Biology & Geology (SBG) satellite becomes available. This work presents a cloud-hosted architecture to streamline plant disease detection with SI from NASAs AVIRIS-NG platform, using grapevine leafroll associated virus complex 3 (GLRaV-3) as a model system. Here, we showcase a pipeline for processing SI to produce plant disease detection models and demonstrate that the underlying principles of a cloud-based disease detection system easily accommodate model improvements and shifting data modalities. Our goal is to make the insights derived from SI available to agricultural stakeholders via a platform designed with their needs and values in mind. The key outcome of this work is an innovative, responsive system foundation that can empower agricultural stakeholders to make data-driven plant disease management decisions, while serving as a framework for others pursuing use-inspired application development for agriculture to follow that ensures social impact and reproducibility while preserving stakeholder privacy. Key PointsO_LICloud-based plant disease detection system, easily accommodates newly developed and/or improved models, as well as diverse data modalities. C_LIO_LIEmpower agricultural stakeholders to use hyperspectral data for decision support while preserving stakeholder data privacy. C_LIO_LIOutline framework for researchers interested in designing geospatial/remote sensing applications for agricultural stakeholders to follow. C_LI

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Rubambiza, G., Romero Galvan, F. E., Pavlick, R. P., Weatherspoon, H., Gold, K.. 2022-12-19. Towards Cloud-Native, Machine Learning Based Detection of Crop Disease with Imaging Spectroscopy. https://doi.org/10.1101/2022.12.15.520316

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