Domain Generalization for Crop Segmentation with Standardized Ensemble Knowledge Distillation

• URL: http://arxiv.org/abs/2304.01029v3
• Date: Sun, 14 Apr 2024 14:53:32 GMT
• Title: Domain Generalization for Crop Segmentation with Standardized Ensemble Knowledge Distillation
• Authors: Simone Angarano, Mauro Martini, Alessandro Navone, Marcello Chiaberge,
• Abstract summary: Service robots need a real-time perception system that understands their surroundings and identifies their targets in the wild. Existing methods, however, often fall short in generalizing to new crops and environmental conditions. We propose a novel approach to enhance domain generalization using knowledge distillation.
• Score: 42.39035033967183
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: In recent years, precision agriculture has gradually oriented farming closer to automation processes to support all the activities related to field management. Service robotics plays a predominant role in this evolution by deploying autonomous agents that can navigate fields while performing tasks such as monitoring, spraying, and harvesting without human intervention. To execute these precise actions, mobile robots need a real-time perception system that understands their surroundings and identifies their targets in the wild. Existing methods, however, often fall short in generalizing to new crops and environmental conditions. This limit is critical for practical applications where labeled samples are rarely available. In this paper, we investigate the problem of crop segmentation and propose a novel approach to enhance domain generalization using knowledge distillation. In the proposed framework, we transfer knowledge from a standardized ensemble of models individually trained on source domains to a student model that can adapt to unseen realistic scenarios. To support the proposed method, we present a synthetic multi-domain dataset for crop segmentation containing plants of variegate species and covering different terrain styles, weather conditions, and light scenarios for more than 70,000 samples. We demonstrate significant improvements in performance over state-of-the-art methods and superior sim-to-real generalization. Our approach provides a promising solution for domain generalization in crop segmentation and has the potential to enhance a wide variety of agriculture applications.

Related papers

• Learning to Generalize Unseen Domains via Multi-Source Meta Learning for Text Classification [71.08024880298613]
  We study the multi-source Domain Generalization of text classification. We propose a framework to use multiple seen domains to train a model that can achieve high accuracy in an unseen domain.
  arXiv  Detail & Related papers  (2024-09-20T07:46:21Z)
• 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)
• Memory-Efficient Prompt Tuning for Incremental Histopathology Classification [69.46798702300042]
  We present a memory-efficient prompt tuning framework to cultivate model generalization potential in economical memory cost. We have extensively evaluated our framework with two histopathology tasks, i.e., breast cancer metastasis classification and epithelium-stroma tissue classification.
  arXiv  Detail & Related papers  (2024-01-22T03:24:45Z)
• Towards Full-scene Domain Generalization in Multi-agent Collaborative Bird's Eye View Segmentation for Connected and Autonomous Driving [49.03947018718156]
  We propose a unified domain generalization framework to be utilized during the training and inference stages of collaborative perception. We also introduce an intra-system domain alignment mechanism to reduce or potentially eliminate the domain discrepancy among connected and autonomous vehicles.
  arXiv  Detail & Related papers  (2023-11-28T12:52:49Z)
• Improved Crop and Weed Detection with Diverse Data Ensemble Learning [18.490612639895893]
  Modern agriculture heavily relies on Site-Specific Farm Management practices, necessitating accurate detection, localization, and quantification of crops and weeds in the field. Existing methods augment and synthesize agricultural data to account for uncontrolled field conditions. We propose utilizing data specific to other crops and weeds for our specific target problem.
  arXiv  Detail & Related papers  (2023-10-02T10:05:30Z)
• DeepG2P: Fusing Multi-Modal Data to Improve Crop Production [1.7406327893433846]
  We present a Natural Language Processing-based neural network architecture to process the G, E and M inputs and their interactions. We show that by modeling DNA as natural language, our approach performs better than previous approaches when tested for new environments.
  arXiv  Detail & Related papers  (2022-11-11T03:32:44Z)
• Waypoint Generation in Row-based Crops with Deep Learning and Contrastive Clustering [1.2599533416395767]
  We propose a learning-based approach to tackle waypoint generation for planning a navigation path for row-based crops. We present a novel methodology for waypoint clustering based on a contrastive loss, able to project the points to a separable latent space. The proposed deep neural network can simultaneously predict the waypoint position and cluster assignment with two specialized heads in a single forward pass.
  arXiv  Detail & Related papers  (2022-06-23T11:21:04Z)
• Unsupervised Domain Generalization for Person Re-identification: A Domain-specific Adaptive Framework [50.88463458896428]
  Domain generalization (DG) has attracted much attention in person re-identification (ReID) recently. Existing methods usually need the source domains to be labeled, which could be a significant burden for practical ReID tasks. We propose a simple and efficient domain-specific adaptive framework, and realize it with an adaptive normalization module.
  arXiv  Detail & Related papers  (2021-11-30T02:35:51Z)
• DeepWay: a Deep Learning Waypoint Estimator for Global Path Generation [1.3764085113103222]
  The presented research proposes a feature learning fully convolutional model capable of estimating waypoints given an occupancy grid map. In particular, we apply the proposed data-driven methodology to the specific case of row-based crops with the general objective to generate a global path able to cover the extension of the crop completely.
  arXiv  Detail & Related papers  (2020-10-30T15:27:42Z)

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.