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Semi-supervised learning methods for weed detection in turf
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DOI：10.1002/ps.7959.png)
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Abstract
En 中文
BACKGROUNDAccurate 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.RESULTSCompared with ResNet50, the evaluated SSL methods, pi-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.CONCLUSIONThese 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. (c) 2024 Society of Chemical Industry. The semi-supervised learning method FixMatch excelled in detecting and differentiating weeds in turf with only 50 labeled images per class and an abundance of unlabeled data, which increased the classification accuracy (>= 0.9530) over the fully supervised learning method ResNet50.image
Keywords:
deep learning
semi-supervised learning
FixMatch
weed detection
precision herbicide application
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