A survey of datasets for computer vision in agriculture
- URL: http://arxiv.org/abs/2502.16950v1
- Date: Mon, 24 Feb 2025 08:27:36 GMT
- Title: A survey of datasets for computer vision in agriculture
- Authors: Nico Heider, Lorenz Gunreben, Sebastian Zürner, Martin Schieck,
- Abstract summary: This paper provides a large number of high-quality datasets of images taken on fields.<n>Overall, we find 45 datasets, which are listed in this paper as well as in an online catalog on the project website.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: In agricultural research, there has been a recent surge in the amount of Computer Vision (CV) focused work. But unlike general CV research, large high-quality public datasets are sparsely available. This can be partially attributed to the high variability between different agricultural tasks, crops and environments as well as the complexity of data collection, but it is also influenced by the reticence to publish datasets by many authors. This, as well as the lack of a widely used agricultural data repository, are impactful factors that hinder research in applied CV for agriculture as well as the usage of agricultural data in general-purpose CV research. In this survey, we provide a large number of high-quality datasets of images taken on fields. Overall, we find 45 datasets, which are listed in this paper as well as in an online catalog on the project website: https://smartfarminglab.github.io/field_dataset_survey/.
Related papers
- Agri-LLaVA: Knowledge-Infused Large Multimodal Assistant on Agricultural Pests and Diseases [49.782064512495495]
We construct the first multimodal instruction-following dataset in the agricultural domain.<n>This dataset covers over 221 types of pests and diseases with approximately 400,000 data entries.<n>We propose a knowledge-infused training method to develop Agri-LLaVA, an agricultural multimodal conversation system.
arXiv Detail & Related papers (2024-12-03T04:34:23Z) - 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) - Self-Supervised Backbone Framework for Diverse Agricultural Vision Tasks [0.3683202928838613]
Computer vision in agriculture is game-changing to transform farming into a data-driven, precise, and sustainable industry.
Deep learning has empowered agriculture vision to analyze vast, complex visual data, but heavily rely on the availability of large annotated datasets.
We propose a lightweight framework utilizing SimCLR, a contrastive learning approach, to pre-train a ResNet-50 backbone on a large, unannotated dataset of real-world agriculture field images.
arXiv Detail & Related papers (2024-03-22T14:46:51Z) - HarvestNet: A Dataset for Detecting Smallholder Farming Activity Using
Harvest Piles and Remote Sensing [50.4506590177605]
HarvestNet is a dataset for mapping the presence of farms in the Ethiopian regions of Tigray and Amhara during 2020-2023.
We introduce a new approach based on the detection of harvest piles characteristic of many smallholder systems.
We conclude that remote sensing of harvest piles can contribute to more timely and accurate cropland assessments in food insecure regions.
arXiv Detail & Related papers (2023-08-23T11:03:28Z) - PhenoBench -- A Large Dataset and Benchmarks for Semantic Image Interpretation in the Agricultural Domain [29.395926321984565]
We present an annotated dataset and benchmarks for the semantic interpretation of real agricultural fields.
Our dataset recorded with a UAV provides high-quality, pixel-wise annotations of crops and weeds, but also crop leaf instances at the same time.
We provide benchmarks for various tasks on a hidden test set comprised of different fields.
arXiv Detail & Related papers (2023-06-07T16:04:08Z) - Agricultural Plant Cataloging and Establishment of a Data Framework from
UAV-based Crop Images by Computer Vision [4.0382342610484425]
We present a hands-on workflow for the automatized temporal and spatial identification and individualization of crop images from UAVs.
The presented approach improves analysis and interpretation of UAV data in agriculture significantly.
arXiv Detail & Related papers (2022-01-08T21:14:07Z) - Retiring Adult: New Datasets for Fair Machine Learning [47.27417042497261]
UCI Adult has served as the basis for the development and comparison of many algorithmic fairness interventions.
We reconstruct a superset of the UCI Adult data from available US Census sources and reveal idiosyncrasies of the UCI Adult dataset that limit its external validity.
Our primary contribution is a suite of new datasets that extend the existing data ecosystem for research on fair machine learning.
arXiv Detail & Related papers (2021-08-10T19:19:41Z) - Pollen13K: A Large Scale Microscope Pollen Grain Image Dataset [63.05335933454068]
This work presents the first large-scale pollen grain image dataset, including more than 13 thousands objects.
The paper focuses on the employed data acquisition steps, which include aerobiological sampling, microscope image acquisition, object detection, segmentation and labelling.
arXiv Detail & Related papers (2020-07-09T10:33:31Z) - Crop Knowledge Discovery Based on Agricultural Big Data Integration [2.597676155371155]
Agricultural data can be generated through various sources, such as: Internet of Thing (IoT), sensors, satellites, weather stations, robots, farm equipment, agricultural laboratories, farmers, government agencies and agribusinesses.
We propose an agricultural data integration method using a constellation schema which is designed to be flexible enough to incorporate other datasets and big data models.
arXiv Detail & Related papers (2020-03-11T00:13:17Z) - Data Warehouse and Decision Support on Integrated Crop Big Data [0.0]
We designed and implemented a continental level agricultural data warehouse (ADW)
ADW is characterised by its (1) flexible schema; (2) data integration from real agricultural multi datasets; (3) data science and business intelligent support; (4) high performance; (5) high storage; (6) security; (7) governance and monitoring; (8) consistency, availability and partition tolerant; (9) cloud compatibility.
arXiv Detail & Related papers (2020-03-10T00:10:22Z) - Agriculture-Vision: A Large Aerial Image Database for Agricultural
Pattern Analysis [110.30849704592592]
We present Agriculture-Vision: a large-scale aerial farmland image dataset for semantic segmentation of agricultural patterns.
Each image consists of RGB and Near-infrared (NIR) channels with resolution as high as 10 cm per pixel.
We annotate nine types of field anomaly patterns that are most important to farmers.
arXiv Detail & Related papers (2020-01-05T20:19:33Z)
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.