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Review

Machine Learning for Plant Stress Modeling: A Perspective towards Hormesis Management

by
Amanda Kim Rico-Chávez
1,
Jesus Alejandro Franco
2,
Arturo Alfonso Fernandez-Jaramillo
3,
Luis Miguel Contreras-Medina
1,
Ramón Gerardo Guevara-González
1,* and
Quetzalcoatl Hernandez-Escobedo
2,*
1
Unidad de Ingeniería en Biosistemas, Facultad de Ingeniería Campus Amazcala, Universidad Autónoma de Querétaro, Carretera Chichimequillas, s/n km 1, El Marqués CP 76265, Mexico
2
Escuela Nacional de Estudios Superiores Unidad Juriquilla, UNAM, Querétaro CP 76230, Mexico
3
Unidad Académica de Ingeniería Biomédica, Universidad Politécnica de Sinaloa, Carretera Municipal Libre Mazatlán Higueras km 3, Col. Genaro Estrada, Mazatlán CP 82199, Mexico
*
Authors to whom correspondence should be addressed.
Plants 2022, 11(7), 970; https://doi.org/10.3390/plants11070970
Submission received: 7 March 2022 / Revised: 28 March 2022 / Accepted: 31 March 2022 / Published: 2 April 2022
(This article belongs to the Special Issue Plant Computational Biology)
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Abstract

Plant stress is one of the most significant factors affecting plant fitness and, consequently, food production. However, plant stress may also be profitable since it behaves hormetically; at low doses, it stimulates positive traits in crops, such as the synthesis of specialized metabolites and additional stress tolerance. The controlled exposure of crops to low doses of stressors is therefore called hormesis management, and it is a promising method to increase crop productivity and quality. Nevertheless, hormesis management has severe limitations derived from the complexity of plant physiological responses to stress. Many technological advances assist plant stress science in overcoming such limitations, which results in extensive datasets originating from the multiple layers of the plant defensive response. For that reason, artificial intelligence tools, particularly Machine Learning (ML) and Deep Learning (DL), have become crucial for processing and interpreting data to accurately model plant stress responses such as genomic variation, gene and protein expression, and metabolite biosynthesis. In this review, we discuss the most recent ML and DL applications in plant stress science, focusing on their potential for improving the development of hormesis management protocols.
Keywords: eustress; crop improvement; intelligent algorithms; agricultural engineering eustress; crop improvement; intelligent algorithms; agricultural engineering

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MDPI and ACS Style

Rico-Chávez, A.K.; Franco, J.A.; Fernandez-Jaramillo, A.A.; Contreras-Medina, L.M.; Guevara-González, R.G.; Hernandez-Escobedo, Q. Machine Learning for Plant Stress Modeling: A Perspective towards Hormesis Management. Plants 2022, 11, 970. https://doi.org/10.3390/plants11070970

AMA Style

Rico-Chávez AK, Franco JA, Fernandez-Jaramillo AA, Contreras-Medina LM, Guevara-González RG, Hernandez-Escobedo Q. Machine Learning for Plant Stress Modeling: A Perspective towards Hormesis Management. Plants. 2022; 11(7):970. https://doi.org/10.3390/plants11070970

Chicago/Turabian Style

Rico-Chávez, Amanda Kim, Jesus Alejandro Franco, Arturo Alfonso Fernandez-Jaramillo, Luis Miguel Contreras-Medina, Ramón Gerardo Guevara-González, and Quetzalcoatl Hernandez-Escobedo. 2022. "Machine Learning for Plant Stress Modeling: A Perspective towards Hormesis Management" Plants 11, no. 7: 970. https://doi.org/10.3390/plants11070970

APA Style

Rico-Chávez, A. K., Franco, J. A., Fernandez-Jaramillo, A. A., Contreras-Medina, L. M., Guevara-González, R. G., & Hernandez-Escobedo, Q. (2022). Machine Learning for Plant Stress Modeling: A Perspective towards Hormesis Management. Plants, 11(7), 970. https://doi.org/10.3390/plants11070970

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