No more laborious stem counting: AI-powered computer vision enables identification and quantification of solid and hollow alfalfa stems at the pixel level

Traditional alfalfa stem phenotyping is labor-intensive and susceptible to bias from subjective ratings. Computer vision and machine learning present a promising solution for objectively assessing stem morphology. This study proposed an AI-driven image analysis to replace manual phenotyping methods...

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Auteurs principaux:	Brandon J. Weihs, Zhou Tang, Somshubhra Roy, Zezhong Tian, Deborah Jo Heuschele, Zhiwu Zhang, Cranos Williams, Zhou Zhang, Garett Heineck, Swayamjit Saha, Zhanyou Xu
Format:	Artigo
Langue:	Inglês
Publié:	Elsevier 2025-12-01
Collection:	Smart Agricultural Technology
Sujets:	YOLO Computer vision Alfalfa Image analysis Pixel Stem hollowness
Accès en ligne:	http://www.sciencedirect.com/science/article/pii/S277237552500509X
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