Learning with fewer labels in deep learning for plant phenotyping - Chen, Feng (2022)
Deep learning has become an important approach in the research of plant phenotyping. However, training a highly-performing deep learning model generally requires a large amount of labeled data. These training data are normally labeled by human annotators and the labeling process is expensive and time-consuming. In plant phenotyping, precisely labeling the images often needs professional knowledge, for example, knowing the species of a plant or the appearance of an organ, disease or pest, which further increases the costs of annotating the data. Hence, exploring effective training methods that use less labeled data to achieve comparable performance can significantly boost the development of deep learning in plant phenotyping.