ShrimpFarming Dataset
Description
About:
The
ShrimpFarming dataset documents the behavior of shrimp around a feeding plate
in an aquaculture tank, to be used for extracting various indicators about the
monitored shrimp and their behavior.
The
images from the dataset were acquired at RiaSearch, Aveiro, Portugal, on five
different days, at different times of the day and under varying illumination
conditions. The image acquisition was manually triggered by pressing a button
at the moment the feeding pellets were introduced in the tank. A Raspberry Pi
Cam Module 3 WIDE was used for image acquisition, which was positioned
approximately 95 cm above the surface of the tank.
The
ShrimpFarming dataset captures shrimp interactions across two distinct
conditions: (i) Feed, which includes images taken during active feeding periods
when pellets are present in the tank, and (ii) NoFeed, which captures shrimp
behavior outside of feeding times, with no pellets in the feeding plate.
The
dataset contains one capture of the “NoFeed” condition and six captures during
feeding, taken in a span of five different days. Three captures occurred around
11 a.m. in both sunny and cloudy conditions, two around 2 p.m., and one at 5
p.m., experiencing a mix of sun and clouds. Among these six captures, three use
pellets of type A and the remaining three use pellets of type B, which vary in
both color and shape.
The
ShrimpFarming dataset includes two types of annotations: (i) anatomy, which
differentiates between shrimp based on their positioning and orientation,
allowing for more detailed analysis of shrimp posture and shrimp measurements,
and (ii) activity, which provides general annotations for all shrimp without
distinguishing individual positions. In both cases annotations for the feeding
plate position are included.
The
duration of each capture session was 20 minutes long, but two distinct timing
protocols were followed: (i) images were captured every 4 seconds; and (ii) the
captures follow a structured image acquisition protocol designed to emphasize
the initial moments of feeding activity. In the latter case, images were
captured every 4 seconds for the first 2 minutes, every 8 seconds for the next
2 minutes, and then every minute for the remaining 16 minutes, offering a
detailed view of shrimp behavior throughout the feeding period.
ShrimpFarming
Dataset Organization:
· The root folder, named
"ShrimpFarming dataset", contains two subfolders: (i)
"Feed", corresponding to the captures that occurred during feeding
(include pellets); (ii) “NoFeed”, which includes the captures that did not
happen in feeding period (do not include pellets).
· These two subfolders contain
subfolders called “type_X”, where "type" corresponds to if the
capture is “Feed” or “NoFeed” and "X" is the number (identifier) of
the capture.
· Inside each “type_X” folder are
three subfolders and a metadata file: (i) “images”, which contains the images
of the corresponding “typeX” capture; (ii) “activity_labels”, which contains
all the activity annotations of those images; (iii) “anatomy_labels”, that has
the anatomy annotations of the images; and (iv) “metadata_typeX”, which is a
.yaml file that contains information regarding the “type_X” capture.
· Inside the “images” folder are the
images of the corresponding capture that follow the naming convention:
“typeX_IMGYYYY”, where “typeX” regards the capture type and the capture
identifier, and “YYYY” is the sequential number of the image from the capture
"X".
· The “activity_labels” and
“anatomy_labels” folders contain the annotations of the images in YOLO format.
Each image has a corresponding .txt annotation file, in each of these two
folders, whose names are the same as the images: “typeX_IMGYYYY”.
· The “metadata_typeX.yaml” files
provide specific information about each capture session, such as date, time,
capture duration, capture intervals protocol, number of shrimps in the tank,
water temperature, shrimp breed and the pellets used (A or B).
Highlights:
· The IST EURECOM Light Field Face
Database is the first database to include the raw light field images, sample 2D
rendered images and the corresponding depth maps along with a rich collection
of metadata, including the location of a set of facial landmarks.
License agreement:
· To use the database please fill in
the license agreement and send a scanned copy
of the signed form by e-mail.
Download:
· The database can be downloaded from
the link below.
· A password for decrypting the compressed
Zip file will be provided after receiving the duly signed license agreement
(see above).
How to reference the database:
· B. Correia; O. Pacheco; R.J.M. Rocha; P.L. Correia; “Image-Based Shrimp Aquaculture Monitoring”. Sensors 2025, Vol. 25, No. 248. https://doi.org/10.3390/s25010248
Contacts:
Feedback is
welcome. Please provide your comments and suggestions by e-mail.