Semi-supervised dry herbage mass estimation using automatic data and synthetic images

TL;DR

This paper introduces a semi-supervised approach combining synthetic data, automatic labeling, and a small trusted dataset to accurately estimate dry herbage biomass using computer vision, reducing the need for extensive manual data collection.

Contribution

It presents a novel semi-supervised method with synthetic data generation and automatic labeling for herbage biomass estimation, improving accuracy with less manual effort.

Findings

Achieved state-of-the-art results on grass biomass datasets.

Demonstrated robustness of the regression network with limited trusted labels.

Validated approach on datasets from Ireland and Denmark.

Abstract

Monitoring species-specific dry herbage biomass is an important aspect of pasture-based milk production systems. Being aware of the herbage biomass in the field enables farmers to manage surpluses and deficits in herbage supply, as well as using targeted nitrogen fertilization when necessary. 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. However, the performance of deep learning comes at the cost of an extensive, and in this case destructive, data gathering process. Since accurate species-specific biomass estimation is labor intensive and destructive for the herbage parcel, we propose in this paper to study low supervision approaches to dry biomass estimation using computer vision. Our contributions include: a synthetic data generation algorithm to generate data for a herbage height aware semantic segmentation task, an automatic process to label data using semantic segmentation maps, and a robust regression network trained to predict dry biomass using approximate biomass labels and a small trusted dataset with gold standard labels. We design our approach on a herbage mass estimation dataset collected in Ireland and also report state-of-the-art results on the publicly released Grass-Clover biomass estimation dataset from Denmark. Our code is available at https://git.io/J0L2a