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Updated: Jul 5, 2025
Measuring Stolons and Rhizomes of Turfgrasses Using a Digital Image Analysis System
Published on: February 19, 2019
Semi-supervised learning methods for weed detection in turf
Teng Liu1, Danlan Zhai1, Feiyu He2
1Peking University Institute of Advanced Agricultural Sciences / Shandong Laboratory of Advanced Agricultural Sciences at Weifang, Weifang, China.
Background:
Accurate weed detection is a prerequisite for precise automatic precision herbicide application. Previous research has adopted the laborious and time-consuming approach of manually labeling and processing large image data sets to develop deep neural networks for weed detection. This research introduces a novel semi-supervised learning (SSL) approach for detecting weeds in turf. The performance of SSL was compared with that of ResNet50, a fully supervised learning (FSL) method, in detecting and differentiating sub-images containing weeds from those containing only turfgrass.
Results:
Compared with ResNet50, the evaluated SSL methods, Π-model, Mean Teacher, and FixMatch, increased the classification accuracy by 2.8%, 0.7%, and 3.9%, respectively, when only 100 labeled images per class were utilized. FixMatch was the most efficient and reliable model, as it exhibited higher accuracy (≥0.9530) and F1 scores (≥0.951) with fewer labeled data (50 per class) in the validation and testing data sets than the other neural networks evaluated.
Conclusion:
These results reveal that the SSL deep neural networks are capable of being highly accurate while requiring fewer labeled training images, thus being more time- and labor-efficient than the FSL method. © 2024 Society of Chemical Industry.
