Eggs US rose to 1.17 USD/Dozen on October 3, 2025, up 0.83% from the previous day. Over the past month, Eggs US's price has fallen 44.74%, and is down 44.60% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. This dataset includes a chart with historical data for Eggs US.

Here are a few use cases for this project:

1. Quality Control in Poultry Industry: Use the Egg Detection model to inspect eggs on a conveyor belt or in egg cartons, classifying them as Fertile Egg (FE), Defective Egg (DE), or Unfertilized Egg (UF). This ensures only high-quality eggs reach the market and reduces human errors in manual inspection.

2. Egg Sorting for Hatcheries: Implement the model to automatically sort eggs in hatcheries based on their respective classes (FE, DE, UF). This will facilitate accurate separation of fertile eggs for incubation and help improve the efficiency of the breeding process.

3. Smart Agriculture Systems: Integrate the Egg Detection model into advanced agricultural management systems to monitor the health and productivity of poultry farms. This can help farmers take appropriate actions to optimize egg production and maintain healthy livestock.

4. Wildlife Conservation and Ecological Research: Utilize the Egg Detection model to study the reproductive health of various bird species in their natural habitats by analyzing collected images of their eggs. Researchers can use this data to monitor species population dynamics and evaluate the impact of environmental changes.

5. Education and Training: Incorporate the Egg Detection model in educational materials and virtual training tools for poultry farm workers and field researchers. This helps teach novices how to identify different types of eggs and assists in building hands-on skills for inspecting and managing eggs in real-world scenarios.

Pink-Eggs Dataset V1 has been specifically curated for object detection tasks within the environmental industry. Comprising 1261 images, this dataset includes 2518 labeled objects falling under a singular class — eggs. The dataset presents a unique collection of images highlighting pink eggs recognized as belonging to the Pomacea canaliculata species, each accompanied by precise bounding box annotations. Its primary objective is to serve as a valuable resource for researchers, utilizing deep learning techniques to analyze and understand the distribution and proliferation of Pomacea canaliculata species. Furthermore, this dataset supports various investigative endeavors that rely on visual data pertaining to the eggs of Pomacea canaliculata, aiding studies within ecological research and environmental sciences.

Here are a few use cases for this project:

1. Poultry Farm Management: The model can be used by poultry farmers to automate the process of counting the number of eggs produced each day. This will help improve accuracy and reduce time consumption, improving overall productivity.

2. Supermarket Inventory Management: Retail businesses can use the model to monitor egg stocks, helping to ensure they always have enough products available for customers, and also help with efficient restocking plans.

3. Egg Production Quality Control: In egg production factories, this model could help detect eggs on the conveyor belt, assisting in the identification of any broken or defective eggs which need to be removed from the production line.

4. Ecological Studies: The model can be used by ecologists to count the number of eggs in bird nests or other wildlife species, which can provide valuable data for ecological research and conservation efforts.

5. Culinary Industry: Restaurants or large-scale catering services can use this model to maintain accurate counts of eggs used in their kitchen, aiding in precise portion control and cost management.

Egg detection dataset

This is a dataset with images of eggs. The dataset is divided into two classes: white-egg and brown-egg. This is YOLOV5 format dataset. The training and validation images are in the train and val folders respectively. The bounding box annotations are in the related labels folders.

  Goal

This dataset is collected to train a YOLOV5 model to detect different types of eggs (right now the white and brown eggs data are available). A model is trained on… See the full description on the dataset page: https://huggingface.co/datasets/industoai/Egg-Detection.

The "Egg Model" is a synthetic reservoir model consisting of an ensemble of 101 relatively small three-dimensional realizations of a channelized reservoir produced under water flooding conditions with eight water injectors and four producers. This data set represents a "standard version" of the Egg Model which is meant to serve as a standard test case in future publications. We implemented and tested the model in four reservoir simulators: Dynamo/Mores (Shell), Eclipse (Schlumberger), AD-GPRS (Stanford University) and MRST (Sintef). This data set also contains the input files for the various simulators.

Misclassified Egg Size Detection

## Overview

Misclassified Egg Size Detection is a dataset for object detection tasks - it contains Egg annotations for 2,045 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Egg shapes A collection of egg-shaped objects and their boundaries. Creators Vincent Pelletier Jiří Hladůvka Introduction Egg shapes are generalized ellipses with positively weighted foci [1]. They can describe a wide range of natural and artificial objects that deviate from the standard ellipse model, such as eggs, avocados, leaves, and rackets. Fitting egg shapes to these objects is a challenging task that may find many applications in computer vision, image analysis, and pattern recognition. However, there is a lack of publicly available collections of real egg-shaped objects that can be used for developing and testing egg shape-fitting algorithms. This collection aims to fill this gap by providing a diverse set of images, segmentation masks, and boundaries of real egg-shaped objects. Context and methodology The purpose of this collection is to: demonstrate the existence of objects that are better described by egg shapes rather than by ellipses, and serve as a test collection for the development of egg shape-fitting algorithms. Diversity and quality This collection consists of seven datasets: eggs_whole: 1,100 whole-egg photographs and segmentation masks from the Egg-segmentation Dataset [2]. We augmented them with 1,100 boundary-coordinates text files. eggs_boiled: Images of longitudinally halved, hard-boiled eggs found on the internet were manually segmented, yielding 12 boundaries each for the egg whites and yolks. avocados: Images of longitudinally halved avocados were found on the internet. The shells of these avocados were manually segmented, resulting in 6 boundaries. Some of them are slightly deformed. leaves: Leaves from trees and plants were deliberately selected and photographed by authors. The selection criteria included being longitudinally symmetrical, egg-shaped, elongated, and possibly pointed. Manual segmentation excluded the stems and produced 23 boundaries. cells: Palisade cells of Arabidopsis thaliana in a micro-CT cross-section slice from Water's Gateway to Heaven project were manually segmented, resulting in 159, mostly elliptic boundaries. household: 11 spoon heads and 2 toilet seats were photographed and segmented by the authors. rackets: Images of tennis, badminton, and squash racket heads sourced from the internet were segmented, resulting in 12 boundaries. The outer shell of the squash head is noted to be pointed. One of the tennis heads is elliptic. The collection contains a total of 1,129 image+mask pairs and 1,337 boundaries. The images vary in size, resolution, quality, and background. The shapes vary in aspect ratio, eccentricity, curvature, and smoothness. While the collection covers a range of natural and artificial domains, it is by no means complete. It can be expected that the set of real-world egg shapes is much broader. The collection may have some biases and limitations, such as the subjective selection of objects and the manual segmentation of masks. Organization of files This collection contains files of three types: jpg: photographs, png: segmentation masks, and txt: boundary coordinates. Each of the seven datasets has photos and segmentation masks stored in: images/{dataset}/{photo}.jpg - photo images/{dataset}/{photo}.png - segmentation mask Several objects may be segmented from one image. This means one or several boundaries are associated with one image. These boundaries are stored in: boundaries/{dataset}/{photo}_{bID}.txt where {bID} is a 3-digit boundary identifier. Acknowledgments Water's Gateway to Heaven project for providing the leaf scan, Táňa Hladůvková for help with collecting the leaves. Access

Eggs CH fell to 3,018 CNY/T on September 30, 2025, down 0.33% from the previous day. Over the past month, Eggs CH's price has risen 2.37%, but it is still 20.50% lower than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. This dataset includes a chart with historical data for Eggs CH.

Egg Size Detection

## Overview

Egg Size Detection is a dataset for object detection tasks - it contains Objects annotations for 756 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Large white, Grade A chicken eggs, sold in a carton of a dozen. Includes organic, non-organic, cage free, free range, and traditional."

Egg Instance Segmentation

This is a dataset with images of eggs that can be used for egg segmentation purposes. The dataset is divided into two classes: white-egg and brown-egg. This is YOLO format dataset. The training and validation images are in the train and val folders respectively. The polygon annotations specifying the exact boundaries of eggs are in the related labels folders.

  Goal

This dataset is collected to train a YOLO model to segment different types of eggs… See the full description on the dataset page: https://huggingface.co/datasets/industoai/Egg-Instance-Segmentation.

Overview The Central Valley Project Improvement Act (CVPIA) funds habitat improvement work and associated monitoring in the Central Valley of California to increase salmonid populations in furtherance of meeting CVPIA fish doubling goals. This data package contains two datasets for White Sturgeon (Acipenser transmontanus) monitoring in the San Joaquin River (SJR) conducted by the US Fish and Wildlife Service, Lodi Fish and Wildlife Office. The objective of this sampling to was determine if White Sturgeon were spawning in the San Joaquin River and to explore where and when spawning occurred, within areas where adult White Sturgeon were known to congregate during the suspected spawning season. SJR_Egg_WST_Set Data This dataset contains data on egg mat sets used to document White Sturgeon spawning in the San Joaquin River. Sets were made at non-random locations from February to May in 2011-2018. In 2017, additional “blitz” sets were used in areas where eggs were detected. Details about set location, timing, and environmental conditions are included, along with the total number eggs of White Sturgeon and other non-sturgeon eggs. SJR_Egg_WST_Catch Data This dataset contains data specific to eggs found in egg mat nets in the San Joaquin River. Across all years, the diameter of eggs (or groups of eggs) were recorded. In 2011 and 2012, efforts were made to describe the developmental stage of White Sturgeon eggs and estimates of spawning timing were sometimes calculated.

Egg DatasetA tab delimited dataset of insect egg shape and size from the published literature, including additional variables (e.g. volume, aspect ratio) calculated from data in egg_dataset_raw_values.tsv. See publication methods for details on how final values were calculated.egg_dataset.tsvEgg Dataset (Raw Values)A tab delimited dataset of insect egg shape and size from the published literature, reporting the values as recorded (e.g. original published taxonomic name, text description of maximum and minimum egg length). See publication for details and on how data was collected.egg_dataset_raw_values.tsvBibliography Egg Dataset (BibTeX format)A bibtex file of sources cited in the egg dataset. References listed here can be matched to entries in the egg dataset by their unique bibliographic identifier (e.g. Iwata1958).bibliography_egg_dataset.bibBibliography Egg Dataset (PDF format)A formatted bibliography of sources cited in the egg dataset.bibliography_egg_dataset.pdf

A dataset of eggs with annotations for egg and crack classes. A total of 840 images with 740 train and 100 test images.

This release combines the UK egg packing station survey, the UK egg processor survey, the egg laying element of the UK hatcheries survey, together with other DEFRA statistics, Intrastat trade data and EU data. Source agency: Environment, Food and Rural Affairs Designation: National Statistics Language: English Alternative title: Egg Statistics Notice If you require the data in a more accessible format, please contact julie.rumsey@defra.gsi.gov.uk Data users: 1. The information in this notice is used by the UK government and the EU as evidence for assessing market conditions and evaluating agricultural policy. The farmgate price of UK eggs are required quarterly under Regulation EC 1165/2008 (Animal Production). 2. Representatives of the egg and poultry industry are also major users of the data. The data on egg production volumes and egg type are the key sector indicators for the British Egg Industry Council (BEIC) as they reflect the size of the national laying flock. The Home Grown Cereals Authority (HGCA), part of the Agricultural and Horticultural Development Board, rely on egg production data as a good indicator of the commercial layer flock and associated feed demand and hence grain usage by the sector. Our statistics are also often heavily referenced in industry publications such as “Poultry World”. Methodology: 3. Defra runs a quarterly survey of registered UK egg packing stations. It is a voluntary sample survey of 27 respondents that collects information on throughput by production type and prices of graded eggs and sales of ungraded eggs. The response rate is typically 100 per cent and the survey accounts for 75 per cent of eggs packed in the UK. The survey figures are raised up to give UK estimates using information on the number of commercial laying hens, average egg yields, average mortality rates, the proportion of UK eggs that go through packing stations. Throughput by egg type for packing stations not surveyed is calculated using data provided by packing stations responding to the survey. The raised figures are published in this statistics notice and the associated datasets. The figures in this notice therefore represent all Class A eggs passed over a grader in the UK, including seconds. The prices obtained on the survey are weighted according to the volume of eggs packed by each packing station to obtained average prices for the UK. From 2012, prices include any bonus payments paid to producers. The Egg Processor survey is a quarterly survey of all registered egg processors. It is a voluntary survey of 13 respondents run by Defra that collects information on the number of eggs bought by egg processors and the quantity of egg products produced. The response rate is typically 100 per cent . These figures come from HM Revenue and Customs, but are validated and adjusted if necessary prior to publication. The Standard Industrial Classification codes used to produce each table are given in the footnotes below the tables. 4. In tables that show numbers of eggs the units used are 'thousand cases'. There are 360 eggs in one case. 5. The data are subject to a variety of validation checks which identify inconsistencies in the data. All data are cleaned prior to publication. 6. The percentage changes shown are calculated using unrounded figures. Thus any percentage changes calculated using the published (rounded) figures may not equate exactly with the changes shown. Revisions policy: 7. Figures in this dataset are provisional and subject to revision. We will provide information about any revisions we make to previously published information in this dataset, and the associated statistics notice. Revisions could occur for various reasons including : a. if we have not received survey data from respondents we make an estimate based on their previous returns. These estimates will be replaced with actual survey data when it is received. b. survey respondents occasionally supply amended figures for previous periods. c. we may also revise the methodology used to raise the survey data to give UK totals. This quarter there are no revisions to previously published throughput figures

This dataset provides a small set of example inputs and outputs to demonstrate the functionality of the Drosophila melanogaster egg counting tool. It includes three backlit images of eggs laid on agarose, along with their corresponding output files:Inputs: The inputs/ directory contains three raw images (image_1.JPG, image_2.JPG, image_3.JPG) to be analyzed by the tool.Outputs: The outputs/ directory contains results for each input image, including:Annotated Images (_annotated.JPG): These images show the detected egg outlines and segmented agarose areas, along with egg counts per region.Egg Count Spreadsheets (_counts.csv): These CSV files contain numerical counts of detected eggs, segmented by the agarose regions in each image.This dataset is intended for use in the project’s README demo section, allowing users to test and visualize the tool’s capabilities.

This repository contains Electrogastrography signals termed Electrogastrograms (EGG) recorded with surface Ag/AgCl electrodes placed over stomach and pre-processed in 20 healthy individuals (8 Females and 12 Males). The method for EGG recording and pre-processing together with subjects' data can be found in Popović et al. 2019.

For each subject, EGG was recorded from three locations before (fasting state) and after (postprandial state) a commercial oat meal (274 kcal). Two 20 minutes recordings (files) are obtained for each subject - fasting and postprandial.

Naming convention for files: subjects ID _ type of recording (fasting / postprandial).

Sample rate was set at 2 Hz and A/D card had 16 bits resolution. Gain of the amplifier was set at 1000. Overall, file size is 7200 samples (2400 samples for each channel). All signals were filtered with 3rd order band-pass Butterworth filter with cut-off frequencies of 0.03 Hz and 0.25 Hz. In order to avoid phase distortion, zero-phase digital filtering was performed in Matlab R2013a by filtfilt() function. GNU Octave code for analysis of EGG signals with statistical calculations presented in Popović et al. 2019 is also provided (eggAnalysis.m).

For convenient test download and appropriate preview, we provided all signals in .zip and sample signal for ID1 in .txt form.

Dataset contents

EGG-database.zip, data files, text format

eggAnalysis.m, GNU Octave code

README.txt, metadata for data files, text format

ID1_fasting.txt and ID1_postprandial.txt, sample data files for subject ID1, text format

Data files contain numerical values with decimal point according to the following structure

column - CH1* (recorded samples from channel 1)

column - CH2* (recorded samples from channel 2)

column - CH3* (recorded samples from channel 3)

• For exact anatomical locations for EGG channels CH1, CH2, and CH3, please refer to Popović et al. 2019.

If you find these signals useful for your own research or teaching class, please cite both relevant paper and dataset as:

Popović, N.B., Miljković, N. and Popović, M.B., 2019. Simple gastric motility assessment method with a single-channel electrogastrogram. Biomedical Engineering/Biomedizinische Technik, 64(2), pp.177-185, doi: 10.1515/bmt-2017-0218.

Popović, N.B., Miljković, N. and Popović, M.B., 2020. Three-channel surface electrogastrogram (EGG) dataset recorded during fasting and post-prandial states in 20 healthy individuals [Data set]. Zenodo, doi: 10.5281/zenodo.3730617.

DISCLAIMER: The GNU Octave code is provided without any guarantee and it is not intended for medical purposes.

Few estimates of Chinook egg-to-fry survival exist despite the fact that this is thought to be one of the life stages limiting production of many listed Chinook populations. The objective of this project is to estimate egg-to-fry survival for Chinook salmon at a variety of habitat conditions throughout the Yakima & Wenatchee basins. Egg-to-fry survival is estimated in more than 100 artificial redds in nine reaches in the Yakima and three in the Wenatchee River. Egg to fry survival estimates.

These data include egg diameter estimates from Grass Carp (Ctenopharyngodon idella) eggs collected during bongo net sampling in the Huron River (Ohio; 41.3305, -82.5812) on May 23, 2022 near Milan, Ohio. Egg diameters were estimated from photographs using proportional relationships of pixel lengths with a known length scale bar in millimeters. Given that eggs were not perfectly spherical, egg diameters were measured across two perpendicular axes and an average was calculated to represent an estimate of egg diameter (mm).