A Rapidly Deployable Classification System using Visual Data for the Application of Precision Weed Management

2018-01-25 · David Hall, Feras Dayoub, Tristan Perez, Chris McCool

In this work we demonstrate a rapidly deployable weed classification system that uses visual data to enable autonomous precision weeding without making prior assumptions about which weed species are present in a given field. Previous work in this area relies on having prior knowledge of the weed species present in the field. This assumption cannot always hold true for every field, and thus limits the use of weed classification systems based on this assumption. In this work, we obviate this assumption and introduce a rapidly deployable approach able to operate on any field without any weed species assumptions prior to deployment. We present a three stage pipeline for the implementation of our weed classification system consisting of initial field surveillance, offline processing and selective labelling, and automated precision weeding. The key characteristic of our approach is the combination of plant clustering and selective labelling which is what enables our system to operate without prior weed species knowledge. Testing using field data we are able to label 12.3 times fewer images than traditional full labelling whilst reducing classification accuracy by only 14%.

📄 PDF Abstract BibTeX arXiv:1801.08613

Code (0)

등록된 구현이 없습니다.

Tasks

ClassificationClusteringGeneral Classification Management

AssetGen: Deployable 3D Asset Generation at Interactive Speed

2026-05-22 · Dilin Wang, Xiaoyu Xiang, Kihyuk Sohn, Tom Monnier 외 arxiv

While 3D generation is progressing rapidly, recent work has often focused on obtaining high-resolution assets, leaving user experience and deployability as afterthoughts. We present AssetGen, a 3D generator that focuses …

3D Generation

Contrastive meta-domain adaptation for robust skin lesion classification across clinical and acquisition conditions

2026-02-23 · Rodrigo Mota, Kelvin Cunha, Emanoel dos Santos, Fábio Papais 외 arxiv

Deep learning models for dermatological image analysis remain sensitive to acquisition variability and domain-specific visual characteristics, leading to performance degradation when deployed in clinical settings. We inv…

Skin Lesion Classification Domain Adaptation

Evaluating Pretrained models for Deployable Lifelong Learning

2023-11-22 · Kiran Lekkala, Eshan Bhargava, Yunhao Ge, Laurent Itti

We create a novel benchmark for evaluating a Deployable Lifelong Learning system for Visual Reinforcement Learning (RL) that is pretrained on a curated dataset, and propose a novel Scalable Lifelong Learning system capab…

Atari Games class-incremental learning Class Incremental Learning Continual Learning+4

Artificial Intelligence-Defined 5G Radio Access Networks

2018-11-21 · Miao Yao, Munawwar Sohul, Vuk Marojevic, Jeffrey H. Reed

Massive multiple-input multiple-output antenna systems, millimeter wave communications, and ultra-dense networks have been widely perceived as the three key enablers that facilitate the development and deployment of 5G s…

VIVID-Med: LLM-Supervised Structured Pretraining for Deployable Medical ViTs

2026-03-10 · Xiyao Wang, Xiaoyu Tan, Yang Dai, Yuxuan Fu 외 arxiv

Vision-language pretraining has driven significant progress in medical image analysis. However, current methods typically supervise visual encoders using one-hot labels or free-form text, neither of which effectively cap…

Lung Nodule Classification Structured Prediction