Toward Cloud‐Native, Machine Learning Base Detection of Crop Disease With Imaging Spectroscopy

Abstract

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 satellite becomes available. This work presents a cloud‐hosted architecture to streamline plant disease detection with SI from NASA’s 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.

Plant phenotyping relevance

画像分光と機械学習によるブドウ病害状態の検出パイプラインおよびクラウド基盤が研究の中心であり、植物病害表現型の取得・推定手法として適格。

Code and data availability

植物フェノタイピング解析を再現する公開資産であることを、入力本文と直接リンクから確認できなかったため保留しました。

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