Editorial: Spectroscopy, imaging and machine learning for crop stress

Abstract

Crop stress poses a huge challenge to food security, necessitating innovative approaches for early detection, monitoring, and management of stress. In recent years, the integration of spectroscopy, imaging, and machine learning techniques has emerged as a promising avenue for detecting various types of crop stress. This editorial work introduces recent publications within the field included in the research topic "Spectroscopy, Imaging, and Machine Learning for Crop Stress." By exploring these cutting-edge research findings, we can gain valuable insights into the application of these technologies to enhance agricultural resilience and productivity. The combination of spectroscopy, imaging, and machine learning has a high potential for improving crop stress analysis and management. By utilizing these technologies, we can enhance our understanding of crop stress dynamics, develop precise and targeted stress detection methods, and improve decision-making processes for farmers.Ongoing research, technological advancements, and collaborative efforts are necessary to unlock the full potential of spectroscopy, imaging, and machine learning in mitigating crop stress and ensuring global food security.

Plant phenotyping relevance

作物ストレスの検出・モニタリングに用いる分光法、イメージング、機械学習を扱う研究テーマのエディトリアルであり、植物ストレス状態の表現型取得・解析手法を中心に概説している。

Code and data availability

This is an editorial summarizing a Research Topic; it contains no paper-specific datasets, images, code, models, or supplements with availability statements. No qualifying assets and no allowed URLs are present.

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