1.Introduction
The project The design of the Insight pipeline for behavioral animal science & animal breeding was offered to Luis Roma Barge as his graduation project, as a part of the Software Technology Professional Doctorate in Engineering program. This program is offered by the Department of Mathematics and Computer Science of the Eindhoven University of Technology in the context of the 4TU.School for Technological Design, Stan Ackerman’s Institute as part of the IMAGEN project.
1.1 The Need for Studying Animal Group Behaviour and Genetics
In the past decades, the human population has grown exponentially. The number of people living on Earth doubled from roughly 2 billion in 1930 to 4 billion in 1974 and doubled again with 7.8 billion in 2020 [4] [5]. The projected human population in 2050 is 10 to 12 billion [6] . With so many mouths to feed, food production must equally grow to satisfy human demand.
Animal livestock farms pose a challenge in keeping up with this increasing demand (horizontal growth).
These facilities are already densely populated, with the average farm having thousands of animals. In addition to the ever-increasing amount of food and water needed to feed the animals, space is also a problem. Even in the farms with the best conditions, these animals live in crowded spaces with several animals per square meter. This lack of space negatively affects the animals; they cannot establish the social hierarchies found in the wild [7], which leads to animals suffering stress and harmful behavior appearing.
In the case of chickens, they may be pecking at each other [8], causing injuries and suffering on the victim. In rare situations, chickens may opt to group against a wall or a corner, stacking on top of other chickens causing suffocation and even death. In the case of the pigs, they will bite each others’ tails [9], causing injuries that can produce infections. All these cases impact the health of the animals, the quality of the products, and the economy of the farm. In the past, these problems were fixed with harmful solutions such as removing chickens’ beaks or cutting off pigs’ tails. Current efforts aim to improve animals’ health in a less harmful way.
If the number of animals on a farm cannot be increased by increasing their living space, the solution is to improve the quality of the animals (vertical growth). For this, multiple worldwide known companies such as Topigs Norsvin [10] or Hendrix Genetics [11] research the way of producing (breeding) animals that respond better to living in large groups by studying their genes. One of the main objectives of these companies is to link phenotype information (the genetics of the animal) with the behavior of the animal.
Unfortunately, analyzing the behavior of a single animal within a group of thousands is difficult and tedious.
1.2 The NWO IMAGEN program
The IMAGEN program is a collaboration of Wageningen University and Research [12], Eindhoven University of Technology [13], and Utrecht University [14], together with Hendrix Genetics, Topigs Norsvin, and several other stakeholders. The program is funded by NWO-TTW.
The goal of IMAGEN is to analyze animal behavior in large groups. IMAGEN usesdata obtained from multiple types of sensors to better understand animal social behavior and link it to individual genes. In particular, IMAGEN uses Computer Vision (CV) [15] techniques to automatically identify and track individual animals in large groups. This allows to build up enough data for doing large scale to do statistics and analysis.
The program has three working packages. These are the following:
• WP1 – Generic sensing, AI, and data science technologies. This package focuses on devel-oping the generic elements of sensing, AI, and data technologies for the automated detection of traits in groups of livestock. WP1 will last for four years.
• WP2 – Behavioral interaction in pigs. This package focuses on the behavioral interactions in pigs, including tail-biting, mounting, aggression, and social-support behaviors. WP2 will last for five years, exceeding WP1 by one year.
• WP3 – Behavioral interaction in laying hens. This package focuses on the behavioral inter-actions in laying hens, including pecking behavior, smothering, and collective dustbathing.
Similar to WP3, it will last for five years.
The animals are provided by Topigs Norsvin (pigs) and Hendrix Genetics (chickens), for their interest in improving the genetics of their animals (see Section 3.3 ). These animals are recorded and analyzed by researchers from Topigs Norvin, Utrecht University, and Wageningen University. Additionally, Eindhoven University of Technology uses the data to produce AI algorithms that aim to identify and track animals and detect specific behavior.
This PDEng project belongs to WP1 but it had a tight collaboration with members of WP2 and WP3.
1.3 Insight
This PDEng project’s contribution to IMAGEN is the design and creation of the “Insight” data pipeline.
This pipeline will be used by all of the members of IMAGEN for behavioral research. Additionally, this pipeline will be integrated into a platform that stores the raw and annotated data and produces analytics from the AI-generated data.
Insight is the data pipeline part of the processing layer of the Discovery Informatics Platform. Insight takes the raw AI-generated data (i.e., files containing bounding boxes of detected and tracked ani-mals) and produces understandable data. This data is generated in the form of reports, analytics, plots, and graphs; It can be further analyzed by behavioral experts. The main goal is to aid animal research-ers in conducting their behavioral and genetic research.
1.4 Goal and Objectives of the Project
The goal of this project is to design a software pipeline capable of transforming complex animal data into useful Insight in order to improve animal welfare. To achieve this goal, we must meet the follow-ing objectives:
•
Design layers that facilitate the storing and sharing of raw data and results (e.g., cataloging and archiving a video stream.)•
Design layers that allow automated AI services to analyze the raw data to a stream of low-level events (e.g identifying an animal and detecting its position or movement.)•
Design pipeline(s) that allow the user to obtain high-level behavioral knowledge from AI-ana-lyzed data (e.g identifying which animals are sociable or aggressive towards which other ani-mals.)1.5 Report Outline
The remaining of the report is organized in the following way. Chapter 2 identifies and describes the project stakeholders. Chapter 3 analyzes the domain and describes the problems to be solved in this
3 project. Chapter 4 is the requirements elicitation. Chapters 5 describes Insight design and implementa-tion. Chapter 6 contains the expected results of applying Insight. Chapter 7 contains the conclusions of this project. Finally, Chapter 8 describes the way this project was managed.
5