From a vision of AI in plant science to the reality of Pest-ID in global agriculture

AMES, Iowa (October 14, 2025) - A 2016 research paper explored a vision for using emerging tools in machine learning – a branch of artificial intelligence – to help plant scientists study stress in plants.

Advancements in imaging technologies at the time “resulted in a deluge of high-resolution images and sensor data of plants,” wrote the paper’s four co-authors, all faculty members at Iowa State University who are working to turn their early vision for AI in agriculture to in-the-fields reality. 

However – wrote Arti Singh, Baskar Ganapathysubramanian, Asheesh Singh and Soumik Sarkar, long-standing members of Iowa State’s “Soynomics” team that has studied how computing tools can improve agriculture – “extracting patterns and features from this large corpus of data requires the use of machine learning tools.”

Their paper in the scientific journal Trends in Plant Science went on to explain their ideas for using different machine learning tools for studying plant identification, classification, quantification and prediction.

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Originally published by Iowa State University in October 2025.