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Trends Plant Sci. 2016 Feb;21(2):110-124. doi: 10.1016/j.tplants.2015.10.015. Epub 2015 Dec 1.

Machine Learning for High-Throughput Stress Phenotyping in Plants.

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Department of Agronomy, Iowa State University, Ames, IA, USA. Electronic address:
Department of Mechanical Engineering, Iowa State University, Ames, IA, USA.
Department of Agronomy, Iowa State University, Ames, IA, USA.


Advances in automated and high-throughput imaging technologies have resulted in a deluge of high-resolution images and sensor data of plants. However, extracting patterns and features from this large corpus of data requires the use of machine learning (ML) tools to enable data assimilation and feature identification for stress phenotyping. Four stages of the decision cycle in plant stress phenotyping and plant breeding activities where different ML approaches can be deployed are (i) identification, (ii) classification, (iii) quantification, and (iv) prediction (ICQP). We provide here a comprehensive overview and user-friendly taxonomy of ML tools to enable the plant community to correctly and easily apply the appropriate ML tools and best-practice guidelines for various biotic and abiotic stress traits.


Imaging; abiotic stress; biotic stress; high-throughput phenotyping; machine learning; plant breeding

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