  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 can be accessed at
https://github.com/DongChen06/Label-efficient-in-Agriculture.
