Semi-Self-Supervised Domain Adaptation: Developing Deep Learning Models with Limited Annotated Data for Wheat Head Segmentation

• URL: http://arxiv.org/abs/2405.07157v1
• Date: Sun, 12 May 2024 04:35:49 GMT
• Title: Semi-Self-Supervised Domain Adaptation: Developing Deep Learning Models with Limited Annotated Data for Wheat Head Segmentation
• Authors: Alireza Ghanbari, Gholamhassan Shirdel, Farhad Maleki,
• Abstract summary: We introduce a semi-self-supervised domain adaptation technique based on deep convolutional neural networks with a probabilistic diffusion process. We develop a two-branch convolutional encoder-decoder model architecture that uses both synthesized image-mask pairs and unannotated images. The proposed model achieved a Dice score of 80.7% on an internal test dataset and a Dice score of 64.8% on an external test set.
• Score: 0.10923877073891444
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Precision agriculture involves the application of advanced technologies to improve agricultural productivity, efficiency, and profitability while minimizing waste and environmental impact. Deep learning approaches enable automated decision-making for many visual tasks. However, in the agricultural domain, variability in growth stages and environmental conditions, such as weather and lighting, presents significant challenges to developing deep learning-based techniques that generalize across different conditions. The resource-intensive nature of creating extensive annotated datasets that capture these variabilities further hinders the widespread adoption of these approaches. To tackle these issues, we introduce a semi-self-supervised domain adaptation technique based on deep convolutional neural networks with a probabilistic diffusion process, requiring minimal manual data annotation. Using only three manually annotated images and a selection of video clips from wheat fields, we generated a large-scale computationally annotated dataset of image-mask pairs and a large dataset of unannotated images extracted from video frames. We developed a two-branch convolutional encoder-decoder model architecture that uses both synthesized image-mask pairs and unannotated images, enabling effective adaptation to real images. The proposed model achieved a Dice score of 80.7\% on an internal test dataset and a Dice score of 64.8\% on an external test set, composed of images from five countries and spanning 18 domains, indicating its potential to develop generalizable solutions that could encourage the wider adoption of advanced technologies in agriculture.

Related papers

• Self-supervised Vision Transformer are Scalable Generative Models for Domain Generalization [0.13108652488669734]
  We propose a novel generative method for domain generalization in histopathology images. Our method employs a generative, self-supervised Vision Transformer to dynamically extract characteristics of image patches. Experiments conducted on two distinct histopathology datasets demonstrate the effectiveness of our proposed approach.
  arXiv  Detail & Related papers  (2024-07-03T08:20:27Z)
• Robust Disaster Assessment from Aerial Imagery Using Text-to-Image Synthetic Data [66.49494950674402]
  We leverage emerging text-to-image generative models in creating large-scale synthetic supervision for the task of damage assessment from aerial images. We build an efficient and easily scalable pipeline to generate thousands of post-disaster images from low-resource domains. We validate the strength of our proposed framework under cross-geography domain transfer setting from xBD and SKAI images in both single-source and multi-source settings.
  arXiv  Detail & Related papers  (2024-05-22T16:07:05Z)
• Generating Diverse Agricultural Data for Vision-Based Farming Applications [74.79409721178489]
  This model is capable of simulating distinct growth stages of plants, diverse soil conditions, and randomized field arrangements under varying lighting conditions. Our dataset includes 12,000 images with semantic labels, offering a comprehensive resource for computer vision tasks in precision agriculture.
  arXiv  Detail & Related papers  (2024-03-27T08:42:47Z)
• Deep Domain Adaptation: A Sim2Real Neural Approach for Improving Eye-Tracking Systems [80.62854148838359]
  Eye image segmentation is a critical step in eye tracking that has great influence over the final gaze estimate. We use dimensionality-reduction techniques to measure the overlap between the target eye images and synthetic training data. Our methods result in robust, improved performance when tackling the discrepancy between simulation and real-world data samples.
  arXiv  Detail & Related papers  (2024-03-23T22:32:06Z)
• Pre-training Contextualized World Models with In-the-wild Videos for Reinforcement Learning [54.67880602409801]
  In this paper, we study the problem of pre-training world models with abundant in-the-wild videos for efficient learning of visual control tasks. We introduce Contextualized World Models (ContextWM) that explicitly separate context and dynamics modeling. Our experiments show that in-the-wild video pre-training equipped with ContextWM can significantly improve the sample efficiency of model-based reinforcement learning.
  arXiv  Detail & Related papers  (2023-05-29T14:29:12Z)
• Extended Agriculture-Vision: An Extension of a Large Aerial Image Dataset for Agricultural Pattern Analysis [11.133807938044804]
  We release an improved version of the Agriculture-Vision dataset (Chiu et al., 2020b) We extend this dataset with the release of 3600 large, high-resolution (10cm/pixel), full-field, red-green-blue and near-infrared images for pre-training. We demonstrate the usefulness of this data by benchmarking different contrastive learning approaches on both downstream classification and semantic segmentation tasks.
  arXiv  Detail & Related papers  (2023-03-04T17:35:24Z)
• Enlisting 3D Crop Models and GANs for More Data Efficient and Generalizable Fruit Detection [0.0]
  We propose a method that generates agricultural images from a synthetic 3D crop model domain into real world crop domains. The method uses a semantically constrained GAN (generative adversarial network) to preserve the fruit position and geometry. Incremental training experiments in vineyard grape detection tasks show that the images generated from our method can significantly speed the domain process.
  arXiv  Detail & Related papers  (2021-08-30T16:11:59Z)
• Potato Crop Stress Identification in Aerial Images using Deep Learning-based Object Detection [60.83360138070649]
  The paper presents an approach for analyzing aerial images of a potato crop using deep neural networks. The main objective is to demonstrate automated spatial recognition of a healthy versus stressed crop at a plant level. Experimental validation demonstrated the ability for distinguishing healthy and stressed plants in field images, achieving an average Dice coefficient of 0.74.
  arXiv  Detail & Related papers  (2021-06-14T21:57:40Z)
• A Robust Illumination-Invariant Camera System for Agricultural Applications [7.349727826230863]
  Object detection and semantic segmentation are two of the most widely adopted deep learning algorithms in agricultural applications. We present a high throughput robust active lighting-based camera system that generates consistent images in all lighting conditions. On average, deep nets for object detection trained on consistent data required nearly four times less data to achieve similar level of accuracy.
  arXiv  Detail & Related papers  (2021-01-06T18:50:53Z)
• Learning Deformable Image Registration from Optimization: Perspective, Modules, Bilevel Training and Beyond [62.730497582218284]
  We develop a new deep learning based framework to optimize a diffeomorphic model via multi-scale propagation. We conduct two groups of image registration experiments on 3D volume datasets including image-to-atlas registration on brain MRI data and image-to-image registration on liver CT data.
  arXiv  Detail & Related papers  (2020-04-30T03:23:45Z)

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.