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...

Whakaahuatanga katoa

Ngā taipitopito rārangi puna kōrero

Ngā kaituhi matua:	Brandon J. Weihs, Zhou Tang, Somshubhra Roy, Zezhong Tian, Deborah Jo Heuschele, Zhiwu Zhang, Cranos Williams, Zhou Zhang, Garett Heineck, Swayamjit Saha, Zhanyou Xu
Hōputu:	Artigo
Reo:	Inglês
I whakaputaina:	Elsevier 2025-12-01
Rangatū:	Smart Agricultural Technology
Ngā marau:	YOLO Computer vision Alfalfa Image analysis Pixel Stem hollowness
Urunga tuihono:	http://www.sciencedirect.com/science/article/pii/S277237552500509X
Ngā Tūtohu:	Tāpirihia he Tūtohu Kāore He Tūtohu, Me noho koe te mea tuatahi ki te tūtohu i tēnei pūkete!