Guest Editors
Dr. Ioannis Vagelas, Department of Agriculture Crop Production and Rural Development, University of Thessaly, GREECE
Dr. Stefanos Leontopoulos, Hellenic Open Univeristy, GREECE
Summary
The impact of climate change on plant ecosystems is an important area of research. Machine learning provides powerful tools to analyze and predict these effects. Studying the impact of climate change on plants using machine learning involves several steps and methods to understand, predict, and mitigate the effects of changing climate conditions.
These steps include analyzing climate data (such as temperature and precipitation models), plant data (including phenological, growth, and distribution data of plant species), environmental data (such as soil types and land use), and genomic data (involving genetic markers for drought resistance). Machine learning models have been developed to forecast crop yields under various climate conditions, predict changes in the timing of plant phenological events like earlier flowering, and anticipate shifts in the distribution of plant species, pests, and diseases under different climate change scenarios using algorithms. In addition, machine learning helps to monitor plant health, forecast crop yield, and analyze genomic data to identify traits associated with climate resilience. This aids in the development of more resistant plant varieties. By utilizing large datasets and advanced algorithms, researchers can develop predictive models and strategies to mitigate adverse effects and promote resilience in plant ecosystems.
Overall, as climate change presents one of the most significant challenges to humanity, machine learning experts might be interested in how artificial intelligence can be used to analyze large amounts of data and assist in human decision-making.
Keywords
environmental impact, climate change, remote sensing, plant detection and monitoring, plant stress mitigation, adaptation
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