Harnessing machine learning approach for phenotyping and deciphering the plants biotic and abiotic stresses responsive molecular mechanisms

Panahi, Bahman; Ghavidel, Bentolhoda; Shahgoli, Pouya

doi:10.30473/cb.2024.70615.1955

Harnessing machine learning approach for phenotyping and deciphering the plants biotic and abiotic stresses responsive molecular mechanisms

Document Type : Review

Authors

¹ Department of Genomics, Branch for Northwest & West region, Agricultural Biotechnology Research Institute of Iran (ABRII), Agricultural Research, Education and Extension Organization (AREEO), Tabriz, Iran.

² Department of Biotechnology, Faculty of Agriculture, Azarbaijan Shahid Madani University, Tabriz, Iran

³ Faculty of Information Technology and Computer Engineering, Azarbaijan Shahid Madani University, Tabriz, Iran

10.30473/cb.2024.70615.1955

Abstract

Machine learning plays a crucial role in identifying specific stressors that impact plant species and provides a comprehensive understanding of the challenges plants face in natural environments. The use of machine learning algorithms has significantly enhanced our ability to classify and differentiate the types of stress. There are two main methodologies in machine learning: supervised learning and unsupervised learning. In supervised learning, the model is trained using input-output data pairs, while unsupervised learning involves training the model without access to output labels. Unsupervised learning is primarily used for data exploration and dimension reduction. This detailed classification helps us better understand the distinct characteristics associated with different stressors and provides a more nuanced view of the plant stress landscape. Machine learning also enables the quantitative assessment of stress intensity and extent, allowing for an accurate evaluation of its impact on plant health and productivity. This quantitative approach helps researchers measure the true extent of stressors and their effects on the overall health of plant ecosystems. By employing advanced algorithms, machine learning can make predictions about future occurrences of stress and their potential consequences on plant ecosystems. This foresight strengthens preventive measures for sustainable agricultural practices, as researchers and practitioners can anticipate and mitigate potential threats to plant health. The purpose of this review is to provide a comprehensive understanding of the applications and concepts of machine learning in uncovering the complexity of plant stress phenotyping and elucidating the involved molecular mechanisms.

Keywords

Main Subjects

Bioinformatics

References

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