Abstract: The past decade has witnessed many great successes of machine learning (ML) and deep learning
(DL) applications in agricultural systems, including weed control, plant disease diagnosis, agricultural
robotics, and precision livestock management. Despite tremendous progresses, one downside of
such ML/DL models is that they generally rely on large-scale labeled datasets for training, and
the performance of such models is strongly influenced by the size and quality of available labeled
data samples. In addition, collecting, processing, and labeling such large-scale datasets is extremely
costly and time-consuming, partially due to the rising cost in human labor. Therefore, developing
label-efficient ML/DL methods for agricultural applications has received significant interests among
researchers and practitioners. In fact, there are more than 50 papers on developing and applying
deep-learning-based label-efficient techniques to address various agricultural problems since 2016,
which motivates the authors to provide a timely and comprehensive review of recent label-efficient
ML/DL methods in agricultural applications. To this end, we first develop a principled taxonomy
to organize these methods according to the degree of supervision, including weak supervision (i.e.,
active learning and semi-/weakly- supervised learning), and no supervision (i.e., un-/self- supervised
learning), supplemented by representative state-of-the-art label-efficient ML/DL methods. In addition,
a systematic review of various agricultural applications exploiting these label-efficient algorithms,
such as precision agriculture, plant phenotyping, and postharvest quality assessment, is presented.
Finally, we discuss the current problems and challenges, as well as future research directions. A well-
classified paper list that will be actively updated can be accessed at https://github.com/DongChen06/Label-efficient-in-Agriculture.
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