2022
DOI: 10.22541/essoar.167091870.03274332/v1
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Towards Cloud-Native, Machine Learning Based Detection of Crop Disease with Imaging Spectroscopy

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 s… Show more

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