Unsupervised Domain Adaptation For Plant Organ Counting

• URL: http://arxiv.org/abs/2009.01081v1
• Date: Wed, 2 Sep 2020 13:57:09 GMT
• Title: Unsupervised Domain Adaptation For Plant Organ Counting
• Authors: Tewodros Ayalew, Jordan Ubbens, Ian Stavness
• Abstract summary: Counting plant organs for image-based plant phenotyping falls within this category. In this paper, we propose a domain-adrial learning approach for domain adaptation of density map estimation.
• Score: 12.424350934766704
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Supervised learning is often used to count objects in images, but for counting small, densely located objects, the required image annotations are burdensome to collect. Counting plant organs for image-based plant phenotyping falls within this category. Object counting in plant images is further challenged by having plant image datasets with significant domain shift due to different experimental conditions, e.g. applying an annotated dataset of indoor plant images for use on outdoor images, or on a different plant species. In this paper, we propose a domain-adversarial learning approach for domain adaptation of density map estimation for the purposes of object counting. The approach does not assume perfectly aligned distributions between the source and target datasets, which makes it more broadly applicable within general object counting and plant organ counting tasks. Evaluation on two diverse object counting tasks (wheat spikelets, leaves) demonstrates consistent performance on the target datasets across different classes of domain shift: from indoor-to-outdoor images and from species-to-species adaptation.

Related papers

• Improving Data Efficiency for Plant Cover Prediction with Label Interpolation and Monte-Carlo Cropping [7.993547048820065]
  The plant community composition is an essential indicator of environmental changes and is usually analyzed in ecological field studies. We introduce an approach to interpolate the sparse labels in the collected vegetation plot time series down to the intermediate dense and unlabeled images. We also introduce a new method we call Monte-Carlo Cropping to deal with high-resolution images efficiently.
  arXiv  Detail & Related papers  (2023-07-17T15:17:39Z)
• Inside Out: Transforming Images of Lab-Grown Plants for Machine Learning Applications in Agriculture [0.0]
  We employ a contrastive unpaired translation (CUT) generative adversarial network (GAN) to translate indoor plant images to appear as field images. While we train our network to translate an image containing only a single plant, we show that our method is easily extendable to produce multiple-plant field images. We also use our synthetic multi-plant images to train several YoloV5 nano object detection models to perform the task of plant detection.
  arXiv  Detail & Related papers  (2022-11-05T20:51:45Z)
• LEAD: Self-Supervised Landmark Estimation by Aligning Distributions of Feature Similarity [49.84167231111667]
  Existing works in self-supervised landmark detection are based on learning dense (pixel-level) feature representations from an image. We introduce an approach to enhance the learning of dense equivariant representations in a self-supervised fashion. We show that having such a prior in the feature extractor helps in landmark detection, even under drastically limited number of annotations.
  arXiv  Detail & Related papers  (2022-04-06T17:48:18Z)
• Self-Supervised Leaf Segmentation under Complex Lighting Conditions [14.290827361756108]
  Leaf segmentation is an essential prerequisite task in image-based plant phenotyping. We present a self-supervised leaf segmentation framework consisting of a self-supervised semantic segmentation model, a color-based leaf segmentation algorithm, and a self-supervised color correction model. Experimental results on datasets of different plant species demonstrate the potential of the proposed self-supervised framework.
  arXiv  Detail & Related papers  (2022-03-29T22:59:02Z)
• A Simple and Effective Use of Object-Centric Images for Long-Tailed Object Detection [56.82077636126353]
  We take advantage of object-centric images to improve object detection in scene-centric images. We present a simple yet surprisingly effective framework to do so. Our approach can improve the object detection (and instance segmentation) accuracy of rare objects by 50% (and 33%) relatively.
  arXiv  Detail & Related papers  (2021-02-17T17:27:21Z)
• Instance Localization for Self-supervised Detection Pretraining [68.24102560821623]
  We propose a new self-supervised pretext task, called instance localization. We show that integration of bounding boxes into pretraining promotes better task alignment and architecture alignment for transfer learning. Experimental results demonstrate that our approach yields state-of-the-art transfer learning results for object detection.
  arXiv  Detail & Related papers  (2021-02-16T17:58:57Z)
• DoFE: Domain-oriented Feature Embedding for Generalizable Fundus Image Segmentation on Unseen Datasets [96.92018649136217]
  We present a novel Domain-oriented Feature Embedding (DoFE) framework to improve the generalization ability of CNNs on unseen target domains. Our DoFE framework dynamically enriches the image features with additional domain prior knowledge learned from multi-source domains. Our framework generates satisfying segmentation results on unseen datasets and surpasses other domain generalization and network regularization methods.
  arXiv  Detail & Related papers  (2020-10-13T07:28:39Z)
• AutoCount: Unsupervised Segmentation and Counting of Organs in Field Images [11.976801748296403]
  We propose a fully unsupervised technique for counting dense objects such as plant organs. We use a convolutional network-based unsupervised segmentation method followed by two post-hoc optimization steps. The proposed technique is shown to provide competitive counting performance on a range of organ counting tasks in sorghum (S. bicolor) and wheat (T. aestivum) with no dataset-dependent tuning or modifications.
  arXiv  Detail & Related papers  (2020-07-17T18:27:47Z)
• Two-View Fine-grained Classification of Plant Species [66.75915278733197]
  We propose a novel method based on a two-view leaf image representation and a hierarchical classification strategy for fine-grained recognition of plant species. A deep metric based on Siamese convolutional neural networks is used to reduce the dependence on a large number of training samples and make the method scalable to new plant species.
  arXiv  Detail & Related papers  (2020-05-18T21:57:47Z)
• Deep Transfer Learning For Plant Center Localization [19.322420819302263]
  This paper investigates methods that estimate plant locations for a field-based crop using RGB aerial images captured using Unmanned Aerial Vehicles (UAVs) Deep learning approaches provide promising capability for locating plants observed in RGB images, but they require large quantities of labeled data (ground truth) for training. We propose a method for estimating plant centers by transferring an existing model to a new scenario using limited ground truth data.
  arXiv  Detail & Related papers  (2020-04-29T06:29:49Z)
• Phase Consistent Ecological Domain Adaptation [76.75730500201536]
  We focus on the task of semantic segmentation, where annotated synthetic data are aplenty, but annotating real data is laborious. The first criterion, inspired by visual psychophysics, is that the map between the two image domains be phase-preserving. The second criterion aims to leverage ecological statistics, or regularities in the scene which are manifest in any image of it, regardless of the characteristics of the illuminant or the imaging sensor.
  arXiv  Detail & Related papers  (2020-04-10T06:58:03Z)

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.