Real-Time Identification of Rice Weeds by UAV Low-Altitude Remote Sensing Based on Improved Semantic Segmentation Model

Real-Time Identification of Rice Weeds by UAV Low-Altitude Remote Sensing Based on Improved Semantic Segmentation Model

Real-time analysis of UAV low-altitude remote sensing images at airborne terminals facilitates the timely monitoring of weeds in the farmland. Aiming at the real-time identification of rice weeds by UAV low-altitude remote sensing, two improved identification models, MobileNetV2-UNet and FFB-BiSeNet...

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Autores principales: Yubin Lan, Kanghua Huang, Chang Yang, Luocheng Lei, Jiahang Ye, Jianling Zhang, Wen Zeng, Yali Zhang, Jizhong Deng
Formato: article
Lenguaje:EN
Publicado: MDPI AG 2021
Materias:
low-altitude remote sensing
semantic segmentation model
real-time
rice weeds
target spraying
Science
Q
Acceso en línea:https://doaj.org/article/3545b7338c78431a8b9dc465e394d63c
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