During 1st Gulf Poultry Science Conference, held on November 25-26 in Abu Dhabi, UAE along with VIV MEA 2025, I got an opportunity to interact with Mr. Ali Murtaza Solangi, Founder and CEO, Poulta Inc., USA (www.poulta.com) and we had good discussions on how artificial intelligence and precision agriculture are transforming one of the world’s most critical food sectors and why India represents the next frontier for this technological revolution. From Vision to Reality: The Middle East Success Story – Poulta’s with head office in USA and branch offices in Canada and Netherlands, credibility in transforming large scale poultry operations was solidified earlier this year when the company announced a strategic partnership with Tanmiah Food Company, one of the Middle East’s largest poultry producers. This collaboration represents the region’s largest digital transformation initiative in the poultry sector, targeting efficiency improvements of 25% and waste reduction of 30%.

What we’ve demonstrated in the Gulf region is that Artificial Intelligence (AI) isn’t a distant future-it’s delivering measurable results today, said Mr. Ali Murtaza Solangi, CEO of Poulta. “We’re seeing mortality rates drop from 5-7% to under 3%, feed conversion ratios improve from 1.9 to 1.6, and production cycles optimize by 20-30%. These aren’t projections; they’re real outcomes happening right now in facilities across the region.”

During my discussions, Mr. Solangi said, India will be the next chapter in Poultry Intelligence. India’s poultry industry, valued at over $30 billion and growing at 8% annually, faces unique challenges that mirror those Poulta has successfully addressed in the Gulf: climate extremes, resource optimization pressures, food safety concerns, and the need for rapid scaling to meet surging protein demand from a growing middle class.

AI isn’t the future—it’s delivering measurable results today.”—

– Mr. Ali Murtaza Solangi, Founder and CEO, Poulta Inc.

Ms. Atifa Asghar, Senior Advisor for Sales and Marketing at Poulta Inc, emphasized the Indian opportunity: “India is not just another market—it’s the ultimate proving ground for agricultural technology. The Indian poultry needs exactly what Poulta Inc. offers: intelligence that turns data into impact. Our platform provides real-time visibility across the entire value chain, from breeder farms to processing facilities, enabling predictive decision-making that was impossible just five years ago.”
The Connected Ecosystem Advantage – According to company, Poulta’s solution operates as the “digital nervous system” for poultry operations, integrating IoT sensors, computer vision, predictive analytics, and blockchain traceability into a unified platform. The system monitors critical parameters— temperature, humidity, bird behavior, feed consumption and uses machine learning to predict biosecurity threats, optimize feeding schedules, and prevent disease outbreaks before they occur. For Indian producers facing increasing pressure to improve sustainability while maintaining profitability, Poulta’s platform delivers on multiple fronts: reducing antibiotic usage through early disease detection, minimizing resource waste through precision feeding, enhancing food safety through end-to-end traceability, and improving animal welfare through continuous monitoring.

Aligning with India’s Agricultural Transformation – India’s focus on digital agriculture, evidenced by initiatives like the National Digital Livestock Mission and increasing adoption of precision farming technologies, creates an ideal environment for Poulta’s expansion. The company’s approach aligns perfectly with India’s goals: food security, employment generation through technology-enabled agriculture, export competitiveness, and sustainable production practices.

The message is clear: the future of poultry farming is data-driven, predictive, and sustainable. Poulta Inc. has proven the model works at scale in one of the world’s most demanding environments. India, with its vast production capacity and appetite for innovation, represents the next chapter in this transformation, one where traditional farming wisdom meets cutting-edge intelligence to feed billions while building resilient, profitable, and environmentally responsible food systems. The question for Indian poultry stakeholders isn’t whether to adopt precision agriculture, it’s whether they can afford to delay while competitors gain the intelligence advantage.

Dr.Partha P. Biswas M.Sc.,Ph.D.,F.Z.S.,F.Z.S.I.
Former Associate Professor & H.O.D., Dept. of Zoology, R.K. Mission V.C.College, Kolkata 700118, W.Bengal.
Senior Consultant-Poultry, Vet & Aqua inputs In West Bengal
Medivin Pharmaceuticals, Hyderabad 500070.Telengana.

Poultry meat production, which continues to be the largest category of total meat output, is predicted to expand dramatically in the coming years. As a result, there is an ongoing need to develop strategies to boost poultry production efficiency while improving animal quality and welfare. Artificial intelligence (AI) might help the poultry sector solve issues such as environmental impact, animal welfare, and production efficiency. Birds have natural behaviors, are healthy, and are emotionally stable. One of the most serious problems in today’s large-scale chicken business is behavioural anomalies, which can appear as increased aggression, lameness, cannibalism, or feather plucking and result in financial losses. Animal behaviour, feeding practises, and environmental factors must all be regularly observed. Furthermore, in order to save money, contemporary chicken farms try to minimise the number of employees while keeping big bird flocks. In this regard, AI has the potential to minimise expenses.

Precision Livestock Farming (PLF)
Precision Livestock Farming (PLF) uses AI-based tools to monitor and regulate poultry production. PLF equipment allow for the unattended collection of commonly recognised data on housing conditions and poultry animals in real time. It comprises automating animal monitoring in order to maximise their production/reproduction, health, welfare, and environmental impact. This allows for accurate real-time data collecting from sensors or other poultry farm equipments. This expedites management choices while minimising financial losses in the long term.

AI in Farm Management
Big Data and AI compliment one other well. AI requires massive amounts of data to learn and enhance decision-making processes, whereas big data analytics uses AI to improve data analysis. Big Data (data sets that are too huge or complicated to be handled by typical data-processing application software and are stored in cloud locations) can play an important role in improving farm management practices. AI technology enables poultry producers to gain real-time insight into their operations.
They may then use this data to change the temperature, lighting, humidity, and other environmental parameters in their chicken coops. Farmers may enhance animal health and increase operational production by utilising AI to optimise situation. The gadget can measure distress sounds emitted by birds in a shed. A new study found that correctly classified distress sounds may be linked to other noises with 97% accuracy. The goal of technology is not to count distress calls, but rather to create surroundings in which hens may live with less stress.

AI in Keeping Track of Feed and Water Usage
In terms of quality assurance, one of the most crucial things to check is nutrition trends. Anomalies in this region are the first warning sign that something is wrong with the birds’ health or conduct. The AI system, in conjunction with sensors and weighing or measuring equipment, may be able to provide early alarms when there is a deviation from the norm and examine the causes of the deviation by gathering data in real time.It could also find correlations between environmental changes and feeding and water consumption behaviours. Based on their results, livestock producers make decisions on practises that may improve animal welfare. Simultaneously, it enables them to track flocks or particular birds who consume food above the norm and identify the reasons for it.

AI in Monitoring Chicken Voclizations
There are two sorts of vocalisations in poultry birds: those generated by individuals for herd identification and those made inside the same animal to track and assess individual animal health.
Poultry producers may now obtain real-time insights into their farm operations thanks to AI technology. They may then use this data to tweak the temperature, lighting, humidity, and other environmental conditions in their chicken homes. Farmers may enhance animal health and increase operational production by utilising AI to optimise situations. Using auditory signals recorded in poultry homes, researchers demonstrated the ability to detect diseases like laryngotracheitis and infectious bronchitis, as well as the bird’s response to temperature and ammonia stress.

AI & It’s Use Identification of Faeces
Faeces are yet another important sign of avian wellbeing. The automated system can use computer vision to analyse its qualities, or it can validate the collected samples by screening the analytical findings for any irregularities in the poultry chicken microbiome, such as the presence of undesirable bacteria.

AI in Disease Management and Control
AI systems coupled to cameras with computer vision may identify any irregularities in bird postures and link them to specific health or behavioural disorders automatically. AI technology can help to speed up illness diagnosis by detecting unusual poultry behaviour like as cannibalism & huddling. Cutting-edge systems may even interpret “chicken chatter” and identify speech changes that are illness precursors. Disease-induced symptoms can also be diagnosed immediately by observing bird behaviours
using AI and real-time data. AI anticipates potential illness outbreaks based on previous data.A new quick detection biosensor for avian flu has been created by researchers.

AI in Improving Farm Egg Handling
Automation can now do egg grading, identify living embryos, regulate incubation settings, and forecast hatching likelihood. This type of technology can also give an effective solution, such as lowering the occurrence of floor eggs. As a consequence, cracks would be reduced, cleaner eggs would be collected, and overall hatchability would improve.

The ground-breaking research of a Senior Study Fellow at the University of Southern Queensland (UniSQ) may lead to important developments in Australian chicken production.For her work on automated chicken monitoring, Dr. Cheryl McCarthy, a mechatronic engineer at the University of Southern Queensland’s Centre for Agricultural Engineering, has been named the 2023 Agri Futures Australia Researcher of the Year. She has successfully tracked a flock of hens’ weight and movement using simply a camera and image analysis, eliminating the need for farmers to handle their flock manually. Dr. McCarthy was able to successfully develop the algorithm that determined the weight of a sample of the flock automatically.

AI in Recognising Breeding Seasons and Tracking Reproduction
AI for detecting breeding times and monitoring reproduction.
Another method for increasing breeding efficiency is to monitor breeding times.
To identify the best breeding period, the machine learning algorithm (these are a precise set of instructions that perform defined operations in either hardware or software-based processes) takes into account flock age, heat times of observed female birds, and other variables that affect fertility (such as environmental conditions, stress, and so on). Monitoring breeding times aids in increasing breeding efficiency. To identify the best breeding period, the machine learning algorithm takes into account flock age, heat times of observed female birds, and other variables that affect fertility (such as environmental conditions, stress, and so on).

AI in Indian Poultry Sector
Despite the fact that digital transformation is becoming increasingly common, many large farms in our country are still cautious to accept new technologies. This might be due to a lack of tools, information, or anxiety about a new change. To remain competitive in this rapidly changing industry, poultry business house must reevaluate their strategy and embrace innovations that can have a significant impact on customer experience and employee engagement.

Conclusions
The current poultry production strategy will be replaced within a decade by a highly intelligent, automated, and data-driven AI system. There is minimal debate over the use of artificial intelligence technology in the poultry sector. AI will revolutionise the poultry business in the next few years with increasing efficiency and accuracy at all levels. Many farms have begun to examine and use AI at various stages of the value chain in recent years. Because there are a number of issues that cannot be addressed without the assistance of robots and robotics & artificial intelligence has enormous promise in the poultry sector. The use of current technology will improve the overall efficiency of the production process.

Fig.1.i Farming (also known as smart farming) or the newest Precision Livestock Farming (PLF) technology, keeps poultry farm and livestock in the best possible condition. In iFarming, measurements on and around the animal are integrated with automatic, ongoing monitoring of every environmental aspect in the home. Production is profitable and sustainable as a result.

Fig 2. AgriFutures Australia and the University of Southern Queensland are looking for technology partners to commercialize cutting-edge video analysis tools to improve on-farm decision-making and better detect growth and welfare indicators of poultry flocks.

Fig.3 The normal and abnormal faeces from typical digestive diseases in chickens.

Fig.4.A mobile-based automated method for identifying and classifying chicken diseases is presented in this study. To identify the presence of the three most prevalent poultry diseases—Salmonella, Coccidiosis, and New Castle Disease—the system analyses the appearance of chicken droppings or faeces. The system is being developed through the collection and pre-processing of image datasets, the creation of augmented images, the segmentation of regions of interest, the training and testing of an image classification deep learning model, and the creation of a mobile app interface. The summary of the study’s steps.