Self-supervised learning -- A way to minimize time and effort for precision agriculture?

• URL: http://arxiv.org/abs/2204.02100v1
• Date: Tue, 5 Apr 2022 10:44:07 GMT
• Title: Self-supervised learning -- A way to minimize time and effort for precision agriculture?
• Abstract summary: Self-supervised learning (SSL) could overcome limitation and incorporate existing unlabeled data. A crop type data set was utilized to conduct experiments with SSL and compare it to supervised methods. Our experiments focused on predicting 2018 using SLL without or a few labels to clarify whether new labels should be collected for an unknown year.
• Score: 26.087244189340858
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Machine learning, satellites or local sensors are key factors for a sustainable and resource-saving optimisation of agriculture and proved its values for the management of agricultural land. Up to now, the main focus was on the enlargement of data which were evaluated by means of supervised learning methods. Nevertheless, the need for labels is also a limiting and time-consuming factor, while in contrast, ongoing technological development is already providing an ever-increasing amount of unlabeled data. Self-supervised learning (SSL) could overcome this limitation and incorporate existing unlabeled data. Therefore, a crop type data set was utilized to conduct experiments with SSL and compare it to supervised methods. A unique feature of our data set from 2016 to 2018 was a divergent climatological condition in 2018 that reduced yields and affected the spectral fingerprint of the plants. Our experiments focused on predicting 2018 using SLL without or a few labels to clarify whether new labels should be collected for an unknown year. Despite these challenging conditions, the results showed that SSL contributed to higher accuracies. We believe that the results will encourage further improvements in the field of precision farming, why the SSL framework and data will be published (Marszalek, 2021).

Related papers

• Robust Semi-Supervised Learning in Open Environments [51.741549825533816]
  Semi-supervised learning (SSL) aims to improve performance by exploiting unlabeled data when labels are scarce. It has been reported that exploiting inconsistent unlabeled data causes severe performance degradation. This paper briefly introduces some advances in this line of research, focusing on techniques concerning label, feature, and data distribution inconsistency in SSL.
  arXiv  Detail & Related papers  (2024-12-24T08:13:01Z)
• A Novel Fusion of Optical and Radar Satellite Data for Crop Phenology Estimation using Machine Learning and Cloud Computing [0.0]
  In the era of big Earth observation data ubiquity, attempts have been made to accurately predict crop phenology based on Remote Sensing data. Here, we estimate phenological developments for eight major crops and 13 phenological stages across Germany at 30m scale using a novel framework.
  arXiv  Detail & Related papers  (2024-08-16T13:44:35Z)
• Enhancing crop classification accuracy by synthetic SAR-Optical data generation using deep learning [0.0]
  In agricultural regions, the dominant crops typically consist of one or two specific types, while other crops are scarce. When collecting training samples to create a map of agricultural products, there is an abundance of samples from the dominant crops, forming the majority classes. Our findings demonstrate that the proposed method generates synthetic data with higher quality that can significantly increase the number of samples for minority classes.
  arXiv  Detail & Related papers  (2024-02-03T11:07:50Z)
• A Novel Transformer-Based Self-Supervised Learning Method to Enhance Photoplethysmogram Signal Artifact Detection [0.0699049312989311]
  This study addresses the underutilization of abundant unlabeled data by employing self-supervised learning (SSL) to extract latent features from this data.<n>Our experiments demonstrate that SSL significantly enhances the Transformer model's ability to learn representations.<n>This approach holds promise for broader applications in PICU environments, where annotated data is often limited.
  arXiv  Detail & Related papers  (2024-01-02T04:00:48Z)
• Semi-Supervised Class-Agnostic Motion Prediction with Pseudo Label Regeneration and BEVMix [59.55173022987071]
  We study the potential of semi-supervised learning for class-agnostic motion prediction. Our framework adopts a consistency-based self-training paradigm, enabling the model to learn from unlabeled data. Our method exhibits comparable performance to weakly and some fully supervised methods.
  arXiv  Detail & Related papers  (2023-12-13T09:32:50Z)
• Label-Efficient Learning in Agriculture: A Comprehensive Review [15.117639286963604]
  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.
  arXiv  Detail & Related papers  (2023-05-24T03:53:20Z)
• Complementing Semi-Supervised Learning with Uncertainty Quantification [6.612035830987296]
  We propose a novel unsupervised uncertainty-aware objective that relies on aleatoric and epistemic uncertainty quantification. Our results outperform the state-of-the-art results on complex datasets such as CIFAR-100 and Mini-ImageNet.
  arXiv  Detail & Related papers  (2022-07-22T00:15:02Z)
• Robust Deep Semi-Supervised Learning: A Brief Introduction [63.09703308309176]
  Semi-supervised learning (SSL) aims to improve learning performance by leveraging unlabeled data when labels are insufficient. SSL with deep models has proven to be successful on standard benchmark tasks. However, they are still vulnerable to various robustness threats in real-world applications.
  arXiv  Detail & Related papers  (2022-02-12T04:16:41Z)
• Taming Overconfident Prediction on Unlabeled Data from Hindsight [50.9088560433925]
  Minimizing prediction uncertainty on unlabeled data is a key factor to achieve good performance in semi-supervised learning. This paper proposes a dual mechanism, named ADaptive Sharpening (ADS), which first applies a soft-threshold to adaptively mask out determinate and negligible predictions. ADS significantly improves the state-of-the-art SSL methods by making it a plug-in.
  arXiv  Detail & Related papers  (2021-12-15T15:17:02Z)
• Self-supervised Learning is More Robust to Dataset Imbalance [65.84339596595383]
  We investigate self-supervised learning under dataset imbalance. Off-the-shelf self-supervised representations are already more robust to class imbalance than supervised representations. We devise a re-weighted regularization technique that consistently improves the SSL representation quality on imbalanced datasets.
  arXiv  Detail & Related papers  (2021-10-11T06:29:56Z)
• Semi-supervised Long-tailed Recognition using Alternate Sampling [95.93760490301395]
  Main challenges in long-tailed recognition come from the imbalanced data distribution and sample scarcity in its tail classes. We propose a new recognition setting, namely semi-supervised long-tailed recognition. We demonstrate significant accuracy improvements over other competitive methods on two datasets.
  arXiv  Detail & Related papers  (2021-05-01T00:43:38Z)
• Self-Tuning for Data-Efficient Deep Learning [75.34320911480008]
  Self-Tuning is a novel approach to enable data-efficient deep learning. It unifies the exploration of labeled and unlabeled data and the transfer of a pre-trained model. It outperforms its SSL and TL counterparts on five tasks by sharp margins.
  arXiv  Detail & Related papers  (2021-02-25T14:56:19Z)
• Semi-supervised learning objectives as log-likelihoods in a generative model of data curation [32.45282187405337]
  We formulate SSL objectives as a log-likelihood in a generative model of data curation. We give a proof-of-principle for Bayesian SSL on toy data.
  arXiv  Detail & Related papers  (2020-08-13T13:50:27Z)

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.