Affordable crop phenotyping matters

Abstract

ABSTRACT Improving genetic gain and optimizing agronomic practices are fundamental to ensuring global food security. Phenotyping plays a pivotal role in accelerating genetic gain, and recent advancements in high-throughput technologies—particularly those based on remote sensing—have significantly enhanced phenotyping capacities. However, the widespread adoption of these innovations has been constrained by high operational and infrastructure costs. In response, affordable yet accurate phenotyping solutions are emerging, enabling large-scale deployment beyond elite research settings. The integration of artificial intelligence (AI) to process and analyze phenotypic data and the open-source nature of the supporting software, combined with the synergistic power of information and communication technologies, is accelerating the development of scalable and cost-effective phenotyping systems. An example of the application capabilities associated with AI is the development in recent years of smartphone-based phenotypic platforms that process information in the cloud. Additionally, the potential use of satellites as future phenotyping platforms promises to further reduce costs and expand access. These low-cost solutions encompass a broad array of components, including sensors, platforms, data processing pipelines, and decision-support tools, collectively ushering in a new era of accessible phenotyping.

Plant phenotyping relevance

低コスト植物フェノタイピング技術を総説し、センサー、プラットフォーム、データ処理、AI解析、スマートフォンおよび衛星による測定系を中心に扱っているため、方法論レビューとして適格。

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