A Rappatoir report on AAKTP project: Data-enabled innovation and ioT application for enhancing broiler productivity in southwestern Nigeria

Keynote speakers and Attendees
Prof. Saidu Oseni (KB Supervisor/Co-PI). Prof. Kamran Munir (KB Supervisor/Co-PI). (Virtual). Prof. I. O. Dudusola (HOD Animal Sciences). Mr. Olamide Akintaro (MD Taro Agric). Mr. Hameed Bashiru (Doctoral Candidate). Lawal Rasheed (Project Associate). Ajayi Ayobami (Project Technician). Fayele Moses Olukoya. Olutomilayo Amazing-Grace. Luqman Abdul Fatai. Adegboyega Aduragbemi Samson. Adefuwa Solomon Oluwole. Salam Muizat Adedoyin. Luqman Olanrewaju Oladosu. Gabriel Abraham Chidiebere. Omilaju Adebowale Muhammed. Bamimore Roqeeb Adedeji. Olawumi Patience Ifeoluwa. Olehi Ifeanyi Precious. Ayodele Adebayo Samuel. Adenike Sanusi.

Project Vision
 A new strategy for exploring data-enabled innovations (DEI), to amplify the business capabilities for higher productivity and profitability by reducing feed wastage, chicken mortality and stunted growth, whilst improving chicken welfare.

Project Aims & Objectives
 Leveraging data-driven technologies and IoT applications to improve the productivity of broiler chickens in Nigeria’s poultry industry.

Expected Goals and Outcomes of the Retreat
A clear understanding and alignment among team members regarding the project’s purpose, objectives and anticipated outcomes. Generation of innovative ideas and strategies leveraging IoT and data-enabled innovations for broiler productivity enhancement. Establishment of a collaborative framework engaging cross-disciplinary interactions and idea sharing. Identified skill enhancement areas and a plan for upskilling team members in relevant domains. A detailed project plan outlining steps, responsibilities, timelines, and resources necessary for successful implementation.

Speaker 1(Prof Kamran Munir): Imperatives of digitalization and digital technologies in Animal sciences)

In summary he outlined the following that should be Harnessed in Animal sciences and transform into digital technology. Mechanized milking (in the UK 60-70% mechanized milking process has been adopted in farms) RFID tags which is used as a timer to know an animal milking process time, monitoring system like sensors. Vetenary diagnostics:king OAU should be a hub where diagnostics through AI are implemented.

Speaker 2 ( Prof. S.O. Oseni): Data-driven technologies especially AI-enabled IoT applications to improved livestock (Broiler) productivity 
 Outlined the following informations: The use of digital technologies for the enhancement of welfare and productivity of poultry business Data- enabled digital technologies Revenue generation Enhanced profitability sensors ( 968-003 hydrogen Sulphide sensor , Adafruit Esp32 board etc.)
Speaker 3(MD Taro Agric): Challenges of broiler chicken industry in Nigeria: the role of DEI
In africa nigeria is the 2nd largest (broiler)production after south Africa. broiler poultry is worth 1.6 trillion naira in market value in Nigeria. Quote from Albert Einstein " insanity is doing the same thing the same way and expecting a different result" Data must ttranslate into wellbeing,y result- maximization and feed intake versus feed wastage profit optimisation in place of profit maximization.

Speaker 4( Mr. Faleye o. Moses): IOT in Data monitoring system and Architecture 
Why Esp32 in all monitoring? It is very cheap It is very powerful compared to hertz mega which runs on 16-20 mega hertz - 3 processors called dual core processor which encourages multitasking Sensor Integration converting from voltage level to sensor level using I2C and it's benefits are; - speed - connectivity security measures using encryption softwares challenges faced : some of the devices where not coded for hence it had to be coded for from scratch conclusion: gathering more data through sensors.

Speaker 5(Bashiru Hameed): Application of machine learning in genomics- based breeding programs for broiler chickens 
Marker assisted selection only takes place in one loci and that seems to be a disadvantage because we need to evaluate a larger amount of loci putting MAS at a disadvantage zone. Machine learning algorithms (GWAS &LP) 36,44 and 88NPs were selected by Adaboost, Random Forest and Decision tree - Machine learning algorithms Have proved more accurate in it's prediction.

Speaker 6(lawal Rasheed): Operationalisation of the AAKTP DEI project 
 Integrated sensor network - captures real-time data collection on the farm and centralized storage Ai - driven support Project outcome: data techniques to improve and make better driven aspect and technologies. AI - driven support aids in proactive decision making for optimal broiler management in ; Feed optimisation,Health monitoring, precision livestock farming, predictive Analysis and resource optimisation.

Presenter 1(Adegboyega Aduragbemi Samson): Machine learning application in growth and health prediction of broiler chicken 
By definition machine learning involves training of computer program to learn from data and improve it's performance overtime. Real-time monitoring technologies to track growth & health status of broiler chicken are being deployed. Acoustics AI machine transcroption machine to track the status of health and predition of growth in Broiler chicken.
Presenter 2( Adefuwa Solomon Oluwole): Sensor technology for poultry production 
 Digital technologies are here to stay because it has prospects that drives systems in different sector of livestock farming . Sensor types are 3 mainly ; physical state sensors,ent chemical sensors and remote sensors sensors functionality is based on infrared rAYS and RGB indicators.
Presenter 3(Salam Muizat Adedoyin): Development of transfer function for weight prediction of live broiler chicken using Machine learning
The following were outlined; Monitoring using MAPE,Relative mean square error (RMSE), Simple Regression error(SRE) on broiler chicken live weight. Vision editing using chan-vese model (removing the head,then the tail, 6 body measures were calculated)

Presenter 4(Luqman Olanrewaju Oladosu): The behavior of commercial broiler in response to a mobile robot
 A mobile robot that could move amongst a flock of birds must be safe guarded in order to preven miscalculation or miscaliberation of it's reading guarded or measurements . Other concepts were; startle behavior of the poultry birds, birds contact with robot , blocking bird's.

Presenter 5 (Omilaju Adebowale Muhammed): Lameness prediction in Broiler chicken using a machine learning technique
The following were outlined; Gait scoring by obtaining Gait parameters Using sensors GS0 - sound of birds/Birds sound GS1 - Less mobility accessment than sound of birds using Gait scoring to evaluate the behavioral condition of broiler chickens.

Presenter 6( Bamimore Roqeeb Adedeji): Use of Artificial neural network to estimate production of variables of Broiler breeders in the production phase
ANN are used in crucial estimation of mortality and Morbidity Ratio,feed consumption,weight gain and egg production . Best practices for implementing ANN: in utilisation of variables. ANN is most applicable in Experimental unit of birds hence highly applicable in large scale production.

Presenter 7(Olawumi Patience Ifeoluwa): Computer -Assisted image analysis to quantify Daily growth rates of Broiler chicken using deep learning approach
Traditional method has declined in it's efficiency and ability to quantify Daily growth rates. Daily learning improves; Enhancement of Animal welfare,better decision making and reducing costs. CNN (central neural network) deals specifically with the behavioral status of an animal ( Broiler chickens).

Presenter 8( olehi Ifeanyi precious): Broiler weight estimation based on machine vision and artificial neural network (ANN)
Chan-vese model is usedd in machine vision editing of chicken (broiler); removing the head, then tail and then 6 body segment giving a complete dissect And weight of each Portion. Monitoring daily Weight gain and feed consumption.
Presenter 9(Gabriel Abraham chidiebere):Current loop-mediated isothermal amplification (LAMP) technologies for the detection of poultry pathogens
Emphasizes on the of LAMP through Ai to detect and diagnose pathogenic infection
   Importance of LAMP ASSAYS : LAMP primer,florescent detectors, detection limits
   Comparison between the traditional practice using serology and culture florescent as to digital technologies using  smartphone Ai  LAMP ASSAYS softwares for diagnostics of viral, bacterial and fungus pathogen in poultry animals.

Pep talk by Adenike Sanusi.
We were encouraged by Mrs adenike sanusi that we should take our project seriously,apply our mind and endeavour to not be shallow minded in this project supervison under PROF. S.O. Oseni because it holds immense rewards.
Conclusion
The conclusion was undertaken by PROF.S.O. oseni who emphasized that the AAKTP was a project that can change the course of agriculture (including Animal breeding and genetics) through Data-driven technologies especially AI-enabled IoT applications to improve livestock productivity

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