Toward Cloud‐Native, Machine Learning Base Detection of Crop Disease With Imaging Spectroscopy
Toward Cloud‐Native, Machine Learning Base Detection of Crop Disease With Imaging Spectroscopy
复制标题
DOI：
10.1029/2022jg007342
复制
发表时间：
2023-05
期刊：
影响因子：
--
通讯作者：
Gloire Rubambiza;Fernando Romero Galvan;R. Pavlick;Hakim Weatherspoon;K. Gold
中科院分区：
文献类型：
--
作者：
Gloire Rubambiza;Fernando Romero Galvan;R. Pavlick;Hakim Weatherspoon;K. Gold
为农业利益相关者开发可操作的早期检测和预警系统对于减少每年2000亿美元的损失和与作物病害相关的环境影响至关重要。农业利益相关者主要依靠劳动密集型，昂贵的侦察和分子检测来检测疾病。光谱图像（SI）可以通过为决策者提供来自机器学习（ML）模型的准确风险地图来改善植物病害管理。然而，训练和部署ML需要大量的计算和存储能力。随着即将到来的地表生物学和地质学卫星提供的全球范围的数据，这一挑战将变得更加严峻。这项工作提出了一个云托管架构，以简化植物疾病检测与SI从美国宇航局的AVIRIS-NG平台，使用葡萄藤叶相关病毒复合体3（GLRaV-3）作为模型系统。在这里，我们展示了一个处理SI以生成植物疾病检测模型的管道，并证明了基于云的疾病检测系统的基本原理可以轻松地适应模型改进和数据模式的转变。我们的目标是通过一个考虑到农业利益相关者的需求和价值观而设计的平台，将来自SI的见解提供给他们。这项工作的主要成果是一个创新的，响应式的系统基础，可以使农业利益相关者能够做出数据驱动的植物病害管理决策，同时作为其他人追求使用启发的农业应用程序开发的框架，以确保社会影响和可重复性，同时保护利益相关者的隐私。
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.