Semantic Scholar Open Access 2016 896 sitasi

Machine Learning for High-Throughput Stress Phenotyping in Plants.

Arti Singh B. Ganapathysubramanian Ashutosh Kumar Singh S. Sarkar

Lihat Sumber DOI

Abstrak

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.

Topik & Kata Kunci

Medicine Biology

Penulis (4)

Arti Singh

B. Ganapathysubramanian

Ashutosh Kumar Singh

S. Sarkar

Format Sitasi

APA MLA BibTeX

Singh, A., Ganapathysubramanian, B., Singh, A.K., Sarkar, S. (2016). Machine Learning for High-Throughput Stress Phenotyping in Plants.. https://doi.org/10.1016/j.tplants.2015.10.015

Akses Cepat

Lihat di Sumber doi.org/10.1016/j.tplants.2015.10.015

Informasi Jurnal

Tahun Terbit: 2016
Bahasa: en
Total Sitasi: 896×
Sumber Database: Semantic Scholar
DOI: 10.1016/j.tplants.2015.10.015
Akses: Open Access ✓