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

• Published in: Plants (Basel) Vol. 11; no. 7; p. 970
• Main Authors: Rico-Chávez, Amanda Kim, Franco, Jesus Alejandro, Fernandez-Jaramillo, Arturo Alfonso, Contreras-Medina, Luis Miguel, Guevara-González, Ramón Gerardo, Hernandez-Escobedo, Quetzalcoatl
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 02.04.2022; MDPI
• Subjects: agricultural engineering; Agriculture; Artificial intelligence; Biology; Biosynthesis; Cellular stress response; crop improvement; Crop production; Crops; Deep learning; Defensive behavior; eustress; Food plants; Food production; Gene expression; Genomes; Genomics; Hormesis; intelligent algorithms; Learning algorithms; Machine learning; Metabolites; Physiological responses; Physiology; Plant sciences; Plant stress; Productivity; Protein biosynthesis; Review; eustress; agricultural engineering; crop improvement; intelligent algorithms
• ISSN: 2223-7747; 2223-7747
• DOI: 10.3390/plants11070970

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.