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Found 4 papers, 1 papers with code
Utilizing unsupervised learning to improve sward content prediction and herbage mass estimation
Sward species composition estimation is a tedious one.
Unsupervised domain adaptation and super resolution on drone images for autonomous dry herbage biomass estimation
In this context, deep learning algorithms offer a tempting alternative to the usual means of sward composition estimation, which involves the destructive process of cutting a sample from the herbage field and sorting by hand all plant species in the herbage.
Semi-supervised dry herbage mass estimation using automatic data and synthetic images
Deep learning for computer vision is a powerful tool in this context as it can accurately estimate the dry biomass of a herbage parcel using images of the grass canopy taken using a portable device.
Extracting Pasture Phenotype and Biomass Percentages using Weakly Supervised Multi-target Deep Learning on a Small Dataset
In this paper, we demonstrate that applying data augmentation and transfer learning is effective in predicting multi-target biomass percentages of different plant species, even with a small training dataset.
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