PROJECT

1. LAYER BIRD VACCINATION MONITORING &
DISEASE DETECTION SYSTEM
Project Proposal
Submitted by
CHENAI MAKOKO
N0199598
Proposed Supervisor
Ms. C. Chivasa
A PROPOSAL SUBMITTED TO
The Department of Informatics at the National University of
Science and Technology in partial fulfillment of the requirements
for the degree of
BACHELOR OF SCIENCE (HONS) INFORMATICS
Faculty of Applied Sciences
[14 November 2022]

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CHAPTER 1 - Introduction
1.1 Introduction
Layer poultry farming means raising egg-laying poultry birds for commercial
egg production. Layer chickens are a particular species of hens, which need to
be raised from one day old. They start laying eggs commercially from 18-19
weeks of age. They remain to lay eggs continuously until they are 72-78 weeks.
They can produce about one kg of eggs by consuming about 2.25 kg of food
during their egg-laying period. Several highly egg-productive layer breeds are
available worldwide (https://www.facebook.com/growelagrovet, 2018).
Throughout the bird’s lifetime, it should reach all the target weights set. This
will ensure a healthy, well-developed, long-enduring bird. To produce quality
output farmers should follow vaccination and treatments religiously. Layers are
affected by different diseases which may be caused by viruses, bacteria, and
fungi. To avoid losses farmers should know when a bird needs treatment and
how? Should they fail, they can contact a vet.
Farmers need a dependable assistant to conduct their day-to-day work. Small-
scale farmers might not have enough labor at their disposal and are at a
disadvantage when it comes to record-keeping, monitoring, and keeping track
of vaccinations, treatments, and overall bird health. Many of them miss crucial
steps in the process because of work overload. Most available applications cater
to large commercial organizations with automated systems including poultry
machinery that feeds certain data into the system.
Layer birds’ management usually refers to the husbandry practices or
production techniques that help maximize production efficiency. Farmers can
have an application at hand that allows them to track progress and get warnings
or recommendations in this case disease prediction or detection, treatment, and
vaccination reminders. Sound management practices are very essential to
optimize production, and this includes detecting any diseases before things get
out of hand. Developing a System that is exclusively for the small-scale layer

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poultry farmer helps in not only record-keeping but also has a decision-making
tool or recommender. This software application covers certain aspects of Layer
Farms. A person with basic knowledge can easily use this software.
1.2 Background
Over the years, farming of layer birds has been on the rise in Zimbabwe. This
has especially proven the need to keep track of farming records and automate
systems so that decisions are made easily with a glance at the dashboard. The
need also dictates that recommendations be made to the farmer from the data
they provide. This study will focus on these aspects and how best they help the
layer farmer effectively.
Layer farmers face many challenges, devastating diseases being the major ones.
Poultry is highly infected with coccidiosis, salmonella, and Newcastle diseases
amongst others, which cause a high mortality rate of livestock. Some of the
disorders, for instance, Salmonella are zoonotic, meaning they can even spread
to humans hence affecting the health of the community. In addition to diseases,
farmers lack access to reliable sources of information on poultry due to several
extension officers, distant locations for consultations,s and a lack of awareness
and recommended animal husbandry practices (Lwoga et al., 2010). They
instead rely on word of mouth from friends and their ways and tradition.
Farmers need solutions to the problems to increase productivity; the use of
vaccines is the common countermeasure at hand though does not apply to all
diseases. The recent use of deep learning in disease detection motivates the need
to contribute to robust diagnostics (Albarqouniet al., 2016)
1.3 Problem Description
Coccidiosis, Salmonella, and Newcastle are some of the common poultry
diseases that curtail poultry production if they are not detected early. In most
third-world countries, these diseases are not detected early due to limited access
to agricultural support services by poultry farmers (Machuve et al., 2022).
A system is needed to run a small-scale layer farm adequately with the rising
demand for poultry. To avoid losses, farmers can always prevent the causes i.e.,
diseases. Small-scale farmers may not always have access to a vet at all times.

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Improved management and disease control can have a substantial impact on
household economies. Reduced losses will ensure that more birds could be
successfully reared and, assuming the extra birds can be properly fed, this will
allow more eggs to be collected and consumed or sold as a regular source of
income. Treatment of the sicknesses after late identification results in a high
mortality rate for the birds (Wong et al., 2017). Therefore, this study aims at
developing a model for the early detection of poultry diseases using deep
learning for early detection of the diseases.
1.4 Aim
To develop a model for early detection of layer bird diseases for layer poultry
farmers.
1.5 Objectives
● To develop a model for early diagnostics of layer bird diseases
● To allow farmers to enter symptoms of layer birds for detecting disease.
● To recommend steps to farmers upon disease identification.
1.6 Project justification
Several cases of layer disease, mainly in Zimbabwe, go undiagnosed due to poor
veterinary support in remote areas. In this context, a centralized system is
needed for effective monitoring and analysis of the layer birds. The farmers are
challenged with inadequate biosecurity measures and limited access to poultry
health services compared to the large-scale commercial poultry system
(Hemalatha et al., 2014). A web-based poultry disease diagnostic system is a
central platform to store the history and predict the possible disease based on
the current symptoms experienced by a bird to ensure a faster and more accurate
diagnosis. Early disease prediction can help the users determine the severity of
the disease and take quick action(Liakos et al. 2018). Improvement in layer
farming practices shall mitigate the effects caused by the diseases, as various
studies indicate that the most efficient way to manage poultry diseases is via
early detection and treatment (Yazdanbakhsh et al., 2017). The target group of

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this study is small-scale layer farmers who use deep litter or semi-commercial
production systems (Wong et al., 2017). Only a small proportion of the diseases
that affect layers can be controlled with vaccination and studies show that in
very few countries in Africa, 28% use models to solve different problems
(Brooks-Pollock et al., 2015). Therefore, there is a need for tools with efficient
and effective methods for diagnostics of diseases that will lead to better yield
and an increase in production.
1.7 Project Scope
The study will be useful to farmers, as the outcome is a tool for the early
detection of diseases affecting layers, contributing to robust diagnostic
measures. This tool will enable farmers to overcome the loss incurred due to
late diagnosis of the disorders affecting layer birds. In the scientific body of
knowledge, the study will contribute to developing a model based on a deep
learning approach that can be deployed on web platforms that will be easily used
by small-scale farmers, extension officers, and other stakeholders.
Rapid detection and diagnostic technologies allow for responses to be made
sooner when the disease is detected, decreasing further bird transmission and
associated costs. Additionally, systems of rapid disease detection produce data
that can be utilized in decision support systems that can predict when and where
the disease is likely to emerge in poultry. (Astill et al., 2018).
In this research, we will be focusing on tracking vaccinations, monitoring
treatments, and data visualization for easy decision-making. We will not be
focusing on other aspects of the whole layer farming process that includes
weighing or focusing on lighting or egg production. In layer farming processing,
several other aspects will not be our focus in this research. The above-mentioned
aspects of the process will be our focus so that we define how captured data will
be used and how the system will use it to the farmer’s advantage. The goals
specific to this project will be defined.

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1.8 Project Report overview
● Chapter 1 - Introduction
Introduction of the project and its concepts.
● Chapter 2 - Literature Review
Reviews of previous similar work done by other researchers.
● Chapter 3 - Methodology
An explanation of research and software design methodologies that were used
to come up with the solution is explained.
● Chapter 4 - System Analysis and Design
This chapter gives an analysis of the system. “what?”, “how?”, “who, when?”
and “how?” a detailed design of how the system will be achieved is given in the
form of a selected design technique.
● Chapter 5 - Implementation System
converting the design into a system and putting that system through various tests
before deployment.
● Chapter 6 - Conclusion
It concludes the work done by giving an analysis of the results. It also
recommends how the solution can be improved in the future.

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References
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● Astill, J., Dara, R.A., Fraser, E.D.G. and Sharif, S. (2018). Detecting and
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