4.7 Review

Thermal Imaging for Plant Stress Detection and Phenotyping

期刊

REMOTE SENSING
卷 13, 期 1, 页码 -

出版社

MDPI
DOI: 10.3390/rs13010068

关键词

Remote sensing; proximal sensing; thermography; plant phenotyping

资金

  1. Junta de Andalucia [P12-AGR-0370]
  2. Ministerio de Ciencia, Innovacion y Universidades (MCIU) by Agencia Estatal de Investigacion (AEI)
  3. European Regional Development Fund (ERDF) [RTI2018-094652-B-I00]

向作者/读者索取更多资源

In recent years, significant efforts have been made to develop new methods for optimizing stress detection in crop fields, with plant phenotyping based on imaging techniques becoming an essential tool in agriculture. Thermal imaging, particularly leaf temperature, is a valuable indicator of plant physiological status responsive to both biotic and abiotic stressors. When combined with other imaging sensors and data-mining techniques, thermography plays a crucial role in achieving more automated, precise, and sustainable agriculture.
In the last few years, large efforts have been made to develop new methods to optimize stress detection in crop fields. Thus, plant phenotyping based on imaging techniques has become an essential tool in agriculture. In particular, leaf temperature is a valuable indicator of the physiological status of plants, responding to both biotic and abiotic stressors. Often combined with other imaging sensors and data-mining techniques, thermography is crucial in the implementation of a more automatized, precise and sustainable agriculture. However, thermal data need some corrections related to the environmental and measuring conditions in order to achieve a correct interpretation of the data. This review focuses on the state of the art of thermography applied to the detection of biotic stress. The work will also revise the most important abiotic stress factors affecting the measurements as well as practical issues that need to be considered in order to implement this technique, particularly at the field scale.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据