Improved weed segmentation in UAV imagery of sorghum fields with a combined deblurring segmentation model.

ABSTRACT:

Background

Efficient and site-specific weed management is a critical step in many agricultural tasks. Image captures from drones and modern machine learning based computer vision methods can be used to assess weed infestation in agricultural fields more efficiently. However, the image quality of the captures can be affected by several factors, including motion blur. Image captures can be blurred because the drone moves during the image capturing process, e.g. due to wind pressure or camera settings. These influences complicate the annotation of training and test samples and can also lead to reduced predictive power in segmentation and classification tasks.

Results

In this study, we propose DeBlurWeedSeg, a combined deblurring and segmentation model for weed and crop segmenta

SUBMITTER: Genze N 

PROVIDER: S-EPMC10463442 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature