Technology advances have enabled the development and implementation of remote sensing technologies for plant, weed, pest, and disease identification and management. This provides an opportunity to develop intelligent agricultural systems for precision applications using artificial intelligence and machine learning.
In this Electronic Data Information Source (EDIS) publication, Yiannis Ampatzidis, agricultural and biological engineering assistant professor and precision agriculture researcher, discusses artificial intelligence (AI) and machine learning concepts and explains how AI can be applied in agriculture through object, disease, and pest detection.
Visit the EDIS website to read more from this publication or download a printable PDF version. Read more from the UF/IFAS Agricultural and Biological Engineering department on our blog.
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