Label-efficient learning in agriculture: A comprehensive review

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. However, a notable limitation of these ML/DL models lies in their reliance on large-scale labeled datasets for training, with their performance closely tied to the quantity and quality of available labeled data. The process of collecting, processing, and labeling such datasets is both expensive and time-consuming, primarily due to escalating labor costs. This challenge has sparked substantial interest among researchers and practitioners in the development of label-efficient ML/DL methods tailored for agricultural applications. 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, a principled taxonomy is first developed 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, the current problems and challenges are discussed, 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.