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Machine Learning for High-Throughput Stress Phenotyping in Plants
Trends in Plant Science
  • Arti Singh, Iowa State University
  • Baskar Ganapathysubramanian, Iowa State University
  • Asheesh Kumar Singh, Iowa State University
  • Soumik Sarkar, Iowa State University
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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.

This article is published as Singh, Arti, Baskar Ganapathysubramanian, Asheesh Kumar Singh, and Soumik Sarkar. "Machine learning for high-throughput stress phenotyping in plants." Trends in plant science 21, no. 2 (2016): 110-124. DOI:10.1016/j.tplants.2015.10.015. Posted with permission.

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Arti Singh, Baskar Ganapathysubramanian, Asheesh Kumar Singh and Soumik Sarkar. "Machine Learning for High-Throughput Stress Phenotyping in Plants" Trends in Plant Science Vol. 21 Iss. 2 (2016) p. 110 - 124
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