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Found 3 papers, 1 papers with code
Active learning for efficient annotation in precision agriculture: a use-case on crop-weed semantic segmentation
Our results indicated that active learning, especially PowerBALD, yields a higher performance than Random sampling on both datasets.
Group-Conditional Conformal Prediction via Quantile Regression Calibration for Crop and Weed Classification
As deep learning predictive models become an integral part of a large spectrum of precision agricultural systems, a barrier to the adoption of such automated solutions is the lack of user trust in these highly complex, opaque and uncertain models.
Active learning with MaskAL reduces annotation effort for training Mask R-CNN
In our study, MaskAL was compared to a random sampling method on a broccoli dataset with five visually similar classes.
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