[论文解读] Image based Crop Monitoring Technologies in Protected Horticulture: A Review

Namal Jayasuriya, Yi Guo+2 more|arXiv (Cornell University)|Jan 25, 2024

Smart Agriculture and AI被引用 5

一句话总结

本论文回顾在保护性温室园艺中使用的基于图像的作物监测和表型平台，详细介绍相机技术、平台类型和性状提取，并指出在半受控环境中对高大、垂直支撑作物的差距。

ABSTRACT

Future food security is a major concern of the 21st century with the growing global population and climate changes. In addressing these challenges, protected cropping ensures food production year-round and increases crop production per land area by controlling environment conditions. Maintaining the growth and health of crops in these facilities is essential to ensure optimum food production. However, this is a laborious work and is currently done manually. Image-based non-destructive plant phenotyping is an emerging research area that reduces the skilled labour cost while enhancing the monitoring of crop growth, health, and identifying phenotype-genotype relations for plant breeding. With the proliferations of protected infrastructures and targeted plants, different technologies and sensor setups are needed for image-based crop monitoring. Conveyor-type plant-to-sensor systems, bench-top or gantry-based systems are commonly found in research facilities focussing on phenotyping of small, relatively short, or movable model plants. This review examines the literature on crop monitoring and phenotyping platforms in both field and protected facilities and explains different camera technologies and their ability to extract different plant traits. The review highlights the future research directions of image-based monitoring of commercial scale protected crops where crops can be relatively tall or vertically supported under semi controlled environments, which presents new challenges and is rarely covered in the literature.

研究动机与目标

• 通过全年保护性栽培和改进的作物健康监测，促进并提升粮食安全。
• 总结在保护设施和田间使用的基于图像的非破坏性表型分析方法。
• 确定用于植物性状提取的传感器配置和平台类型（传送带、台式、龙门式）。
• 讨论半受控环境中对高大、垂直支撑作物的挑战与差距。
• 提出面向商业规模应用的未来研究方向。

提出的方法

• 综述田间和保护设施中的作物监测与表型平台的文献。
• 解释相机技术及其用于提取植物性状的能力。
• 描述常见的平台配置（传送带型、台式、龙门式）用于小型或可移动植物。
• 在基于图像的表型分析方面，比较半受控的保护环境与田间条件。
• 突出在保护设施中处理更高作物的未来研究方向。

实验结果

研究问题

• RQ1 在保护性园艺中用于作物表型的基于图像的监测技术有哪些？
• RQ2 哪些相机配置与平台能够在保护环境中提取关键植物性状？
• RQ3 在基于图像的作物监测的背景下，保护设施与田间环境有何不同？
• RQ4 将基于图像的监测扩展到商业高度作物存在哪些差距与未来研究方向？

主要发现

• 在保护环境中评估了不同的相机技术以提取多样的植物性状。
• 在表型研究中，传送带、台式和龙门系统等平台类型常用于小型或可移动植物。
• 在半受控环境中对高大或垂直支撑作物的基于图像的监测方面，文献存在空缺。
• 该综述强调未来应聚焦于商业规模的保护性作物研究，而不仅限于小型模型植物。
• 本文讨论了将基于图像的表型分析适应于更高作物和半受控环境的挑战与机遇。