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 this https URL

本研究提出了一种利用计算机视觉中的深度学习算法，通过少量已标注数据及近似标注数据来监测牧草干草生物量的低监督方法，其包括合成数据生成算法、自动标注数据处理过程和强健的回归网络。该方法在爱尔兰采集的牧草质量估计数据集上进行了设计，还对丹麦发布的草艹生物量估计数据集报告了最先进的结果。