Artificial Intelligence in plant biology is transforming the way researchers understand plant systems and manage agricultural practices. By leveraging machine learning and deep learning techniques, AI is capable of analyzing vast amounts of plant data, from genomic sequences to environmental conditions, to uncover complex patterns and relationships that would otherwise be challenging to detect. AI algorithms can help optimize plant breeding programs by predicting desirable traits, such as yield, drought tolerance, or pest resistance, allowing for faster and more precise crop improvement. In addition, AI is playing a crucial role in precision agriculture, where real-time data from sensors, drones, and satellite imagery are analyzed to monitor plant health, soil moisture, and nutrient levels, helping farmers make informed decisions that enhance productivity and sustainability.
AI-driven tools also support disease detection by analyzing visual patterns and identifying early signs of pathogens, reducing the need for chemical pesticides and minimizing crop loss. Moreover, the integration of AI with biotechnology is facilitating the development of genetically modified plants with enhanced resistance to environmental stresses and pests. Artificial Intelligence in plant biology can also contribute to bioinformatics, assisting in the identification of critical genes and pathways responsible for key traits.
Title : Functional medicine and the agronomic engineer: What it is and how to influence in a society after a pandemic
Edgar Omar Rueda Puente, Universidad de Sonora, Mexico
Title : Revealing allelic variations in candidate genes associated with grain yield under salinity stress between two contrasting rice genotypes
Nisha Sulari Kottearachchi, Wayamba University of Sri Lanka, Sri Lanka
Title : Primed for the Future: PGPR and the Promise of Sustainable, Heritable Crop Resilience
Prashant Singh, Banaras Hindu University (BHU), India
Title : Genetic variability, heritability and genetic advance for yield and agronomic traits in winged bean
Ufuoma Lydia Akpojotor, International Institute of Tropical Agriculture, Nigeria
Title : Adaptive strategies of aristida L. Species across ecological zones of Pakistan: linking soil characteristics with morphological and physiological traits
Iram Ijaz, University of Agriculture Faisalabad Pakistan, Pakistan
Title : Exploring the genetic diversity in tannin-rich forages to explain the large intra species variability in tannin content
Selina Sterup Moore, Aarhus University, Denmark