Image Processing in AI-Driven Phenomics
AI-driven image processing methods are crucial for analyzing complex biological images, enabling faster and more accurate extraction of phenotypic data. In this section, we explore AI algorithms that automate the identification and quantification of morphological features in biological specimens. Learn how deep learning and computer vision techniques are being applied to enhance image analysis and automate workflows.
From segmentation to feature extraction, AI tools are accelerating the process of analyzing large datasets, providing greater insight into phenotypic variation.
Citation:
Y. He, J.M. Mulqueeney, E.C. Watt, A. Salili-James, N.S. Barber, M. Camaiti,
E.S.E. Hunt, O. Kippax-Chui, A. Knapp, A. Lanzetti, G. Rangel-de Lázaro,
J.K. McMinn, J. Minus, A.V. Mohan, L.E. Roberts, D. Adhami,
E. Grisan, Q. Gu, V. Herridge, S. Poon, T. West, and A. Goswami. (2024).
Opportunities and challenges in applying AI to evolutionary morphology.
Published in Integrative Organismal Biology, obae036.
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