4th Edition Of Plant Science and Molecular Biology World Conference 2027

AI in plant science and machine learning in agriculture are transforming how plant data is analysed, and this session at PMBWC 2027 explores their growing role in research and breeding. Plant research now produces large amounts of data from genomes, gene expression studies, field trials, imaging and weather records. Artificial intelligence and machine learning help researchers analyse this data and turn it into useful predictions about plant traits, crop performance and responses to climate. Machine learning models can find patterns in data that are difficult to detect with traditional statistics. They are being used to predict yield from genetic markers, identify diseases from leaf images, estimate crop growth from drone and satellite data, and forecast how varieties will perform in different environments. As these tools become easier to use, they are becoming part of everyday plant research and breeding.

This session includes genomic prediction, image-based phenotyping, deep learning, protein structure prediction and crop growth and climate models. Both the development of new methods and their application in plant science and breeding are covered, along with issues of data quality and model accuracy.

Key Topics Covered

• Genomic prediction and machine learning models
• Deep learning for image-based plant phenotyping
• AI tools for plant disease detection
• Crop growth, yield and climate prediction models
• Protein structure prediction for plant genes
• Data quality, model accuracy and open data in plant research