Label-Efficient Learning in Agriculture: A Comprehensive Review

• URL: http://arxiv.org/abs/2305.14691v1
• Date: Wed, 24 May 2023 03:53:20 GMT
• Title: Label-Efficient Learning in Agriculture: A Comprehensive Review
• Authors: Jiajia Li, Dong Chen, Xinda Qi, Zhaojian Li, Yanbo Huang, Daniel Morris, Xiaobo Tan
• Abstract summary: Authors develop a principled taxonomy to organize label-efficient ML/DL methods according to the degree of supervision. A systematic review of various agricultural applications exploiting these label-efficient algorithms is presented.
• Score: 15.117639286963604
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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 can be accessed at https://github.com/DongChen06/Label-efficient-in-Agriculture.

Related papers

• Application of Machine Learning in Agriculture: Recent Trends and Future Research Avenues [6.0460261046732455]
  Food production is a vital global concern and the potential for an agritech revolution through artificial intelligence (AI) remains largely unexplored. This paper presents a comprehensive review focused on the application of machine learning (ML) in agriculture, aiming to explore its transformative potential in farming practices and efficiency enhancement.
  arXiv  Detail & Related papers  (2024-05-23T17:53:31Z)
• Self-Supervised Backbone Framework for Diverse Agricultural Vision Tasks [0.3683202928838613]
  Computer vision in agriculture is game-changing to transform farming into a data-driven, precise, and sustainable industry. Deep learning has empowered agriculture vision to analyze vast, complex visual data, but heavily rely on the availability of large annotated datasets. We propose a lightweight framework utilizing SimCLR, a contrastive learning approach, to pre-train a ResNet-50 backbone on a large, unannotated dataset of real-world agriculture field images.
  arXiv  Detail & Related papers  (2024-03-22T14:46:51Z)
• Performance Evaluation of Semi-supervised Learning Frameworks for Multi-Class Weed Detection [15.828967396019143]
  Effective weed control plays a crucial role in optimizing crop yield and enhancing agricultural product quality. Recent advances in precision weed management enabled by ML and DL provide a sustainable alternative. Semi-supervised learning methods, especially semi-supervised learning, have gained increased attention in the broader domain of computer vision.
  arXiv  Detail & Related papers  (2024-03-06T00:59:51Z)
• Data-Centric Digital Agriculture: A Perspective [23.566985362242498]
  Digital agriculture is rapidly evolving to meet increasing global demand for food, feed, fiber, and fuel. Machine learning research in digital agriculture has predominantly focused on model-centric approaches. To fully realize the potential of digital agriculture, it is crucial to have a comprehensive understanding of the role of data in the field.
  arXiv  Detail & Related papers  (2023-12-06T11:38:26Z)
• Machine Learning Meets Advanced Robotic Manipulation [48.6221343014126]
  The paper reviews cutting edge technologies and recent trends on machine learning methods applied to real-world manipulation tasks. The rest of the paper is devoted to ML applications in different domains such as industry, healthcare, agriculture, space, military, and search and rescue.
  arXiv  Detail & Related papers  (2023-09-22T01:06:32Z)
• Benchmarking Automated Machine Learning Methods for Price Forecasting Applications [58.720142291102135]
  We show the possibility of substituting manually created ML pipelines with automated machine learning (AutoML) solutions. Based on the CRISP-DM process, we split the manual ML pipeline into a machine learning and non-machine learning part. We show in a case study for the industrial use case of price forecasting, that domain knowledge combined with AutoML can weaken the dependence on ML experts.
  arXiv  Detail & Related papers  (2023-04-28T10:27:38Z)
• A Survey on Extreme Multi-label Learning [72.8751573611815]
  Multi-label learning has attracted significant attention from both academic and industry field in recent decades. It is infeasible to directly adapt them to extremely large label space because of the compute and memory overhead. eXtreme Multi-label Learning (XML) is becoming an important task and many effective approaches are proposed.
  arXiv  Detail & Related papers  (2022-10-08T08:31:34Z)
• Learning crop type mapping from regional label proportions in large-scale SAR and optical imagery [9.303156731091532]
  This study proposes an online deep clustering method using crop label proportions as priors to learn a sample-level classifier. We evaluate the method using two large datasets from two different agricultural regions in Brazil.
  arXiv  Detail & Related papers  (2022-08-24T15:23:26Z)
• Understanding the World Through Action [91.3755431537592]
  I will argue that a general, principled, and powerful framework for utilizing unlabeled data can be derived from reinforcement learning. I will discuss how such a procedure is more closely aligned with potential downstream tasks.
  arXiv  Detail & Related papers  (2021-10-24T22:33:52Z)
• Just Label What You Need: Fine-Grained Active Selection for Perception and Prediction through Partially Labeled Scenes [78.23907801786827]
  We introduce generalizations that ensure that our approach is both cost-aware and allows for fine-grained selection of examples through partially labeled scenes. Our experiments on a real-world, large-scale self-driving dataset suggest that fine-grained selection can improve the performance across perception, prediction, and downstream planning tasks.
  arXiv  Detail & Related papers  (2021-04-08T17:57:41Z)
• A Survey of Deep Active Learning [54.376820959917005]
  Active learning (AL) attempts to maximize the performance gain of the model by marking the fewest samples. Deep learning (DL) is greedy for data and requires a large amount of data supply to optimize massive parameters. Deep active learning (DAL) has emerged.
  arXiv  Detail & Related papers  (2020-08-30T04:28:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.