Poultry farming faces increasing pressure to meet productivity targets while ensuring animal welfare and environmental compliance. Yet many small and medium-sized farms lack affordable, integrated tools for continuous monitoring and decision-making, relying instead on manual, reactive inspections. This paper presents Poultry Farm Intelligence (PoultryFI) - a modular, cost-effective platform that integrates six AI-powered modules: Camera Placement Optimizer, Audio-Visual Monitoring, Analytics & Alerting, Real-Time Egg Counting, Production & Profitability Forecasting, and a Recommendation Module. Camera layouts are first optimized offline using evolutionary algorithms for full poultry house coverage with minimal hardware. The Audio-Visual Monitoring module extracts welfare indicators from synchronized video, audio, and feeding data. Analytics & Alerting produces daily summaries and real-time notifications, while Real-Time Egg Counting uses an edge vision model to automate production tracking. Forecasting models predict egg yield and feed consumption up to 10 days in advance, and the Recommendation Module integrates forecasts with weather data to guide environmental and operational adjustments. This is among the first systems to combine low-cost sensing, edge analytics, and prescriptive AI to continuously monitor flocks, predict production, and optimize performance. Field trials demonstrate 100% egg-count accuracy on Raspberry Pi 5, robust anomaly detection, and reliable short-term forecasting. PoultryFI bridges the gap between isolated pilot tools and scalable, farm-wide intelligence, empowering producers to proactively safeguard welfare and profitability.

Understanding animal vocalizations through multi-source data fusion is crucial for assessing emotional states and enhancing animal welfare in precision livestock farming. This study aims to decode dairy cow contact calls by employing multi-modal data fusion techniques, integrating transcription, semantic analysis, contextual and emotional assessment, and acoustic feature extraction. We utilized the Natural Language Processing model to transcribe audio recordings of cow vocalizations into written form. By fusing multiple acoustic features frequency, duration, and intensity with transcribed textual data, we developed a comprehensive representation of cow vocalizations. Utilizing data fusion within a custom-developed ontology, we categorized vocalizations into high frequency calls associated with distress or arousal, and low frequency calls linked to contentment or calmness. Analyzing the fused multi dimensional data, we identified anxiety related features indicative of emotional distress, including specific frequency measurements and sound spectrum results. Assessing the sentiment and acoustic features of vocalizations from 20 individual cows allowed us to determine differences in calling patterns and emotional states. Employing advanced machine learning algorithms, Random Forest, Support Vector Machine, and Recurrent Neural Networks, we effectively processed and fused multi-source data to classify cow vocalizations. These models were optimized to handle computational demands and data quality challenges inherent in practical farm environments. Our findings demonstrate the effectiveness of multi-source data fusion and intelligent processing techniques in animal welfare monitoring. This study represents a significant advancement in animal welfare assessment, highlighting the role of innovative fusion technologies in understanding and improving the emotional wellbeing of dairy cows.

If an unexpected meow, peculiar pose, or unusual twitch of the whiskers leaves you puzzling over what your cat is trying to tell you, artificial intelligence may soon be able to translate. Scientists are turning to new technology to unpick the meanings behind the vocal and physical cues of a host of animals. "We could use AI to teach us a lot about what animals are trying to say to us," said Daniel Mills, a professor of veterinary behavioural medicine at the University of Lincoln. Previous work, including by Mills, has shown that cats produce a variety of facial expressions when interacting with humans, and this week researchers revealed felines have a range of 276 facial expressions when interacting with other cats. "However, the facial expressions they produce towards humans look different from those produced towards cats," said Dr Brittany Florkiewicz, an assistant professor of psychology at Lyon College in Arkansas who co-authored the new work.

Supported by Rethink Priorities • This is part of a weekly series summarizing the top posts on the EA and LW forums - you can see the full collection here. The first post includes some details on pur…

Smart farming, an increasingly common part of food production, refers broadly to the innovative use of sensors, robotics, and artificial intelligence (AI) to streamline agriculture. Field crop examples of smart farming include monitoring soil health using small sensors, spotting signs of disease in plants via drones, and facilitating connections between smaller-scale farmers through consumer electronic devices. These smart innovations offer potential time savings and crop optimization benefits for farmers and may contribute to the wiser use of resources in food production. Smart farming is also used in animal agriculture in forms such as Precision Livestock Farming (PLF) and Digital Livestock Farming (DLF). PLF uses sensors and small electronics to measure key indicators related to animals' physiology and behavior.

Artificial intelligence (AI) has started focusing on animal welfare including poultry farms in recent times. Farmers can leverage AI for its voice technology with a deep learning tool known as a bird-brained bot to gain information regarding baby chicks and chickens on their farms. AI can help to detect squawking chickens and get them out of distress by enhancing their health or physical conditions. The bird-brained bot is developed for the well-being of squawking chickens by listening to them carefully. The deep learning tool with the integrated voice technology can help to determine their issues and happiness with their squawking patterns. Instagram's New AI is the Common Creep Tech that Happily Invades Your Privacy But Why are Other Nations Worried?

Pager was feverishly moving a joystick as he played his favorite game, Pong, but it was merely a force of habit--the joystick itself was not actually connected to anything. Yet the ball moved from paddle to paddle. He was using his thoughts to play, through use of his direct neural connections from his newly implanted Neuralink device. Pager isn't your typical 9-year-old, though: He is a macaque monkey and will provide valuable information to the company owned by Elon Musk, so it may eventually move forward with human testing for this invasive medical device. In fact, his gaming performance was at a live Neuralink event, in which people were first introduced to a working implanted model.

Facial recognition technology is rapidly becoming ubiquitous, used in everything from security cameras to smartphones. But in the near future, humans may not be the only ones to be digitally captured. Researchers are training forms of artificial intelligence to recognize individual animals by their faces alone -- and even discern their emotional state just by reading their expressions. Much of the research into animal facial expressions has focused on species like dogs and horses. But some of the most cutting-edge work is aimed at an unlikely subject: the farmed hog.

Leading conservationists have emphasized that conservation's priority is the protection of species and populations, not the welfare of individual nonhuman animals (hereafter “animals”) ([ 1 ][1]–[ 3 ][2]). Although individual conservationists often harbor concern for animal welfare, conservation organizations and scientists frequently downplay or ignore the ethical implications of actions they promote that harm individual animals, from culling and sport hunting to the discontinuation of wildlife rescue from oil spills ([ 3 ][2]–[ 5 ][3]). A growing body of scientific evidence should prompt conservation organizations to reconsider their inattention to animal welfare. A wide variety of vertebrate species (and perhaps some invertebrates) are capable of experiencing physical and emotional pain, engaging in substantive relationships, and executing cognitively complex tasks ([ 6 ][4]–[ 8 ][5]), bolstering claims that animal well-being is morally significant and policy-relevant. Addressing animal welfare in conservation would be politically challenging, and given the central role of predation and competition in ecosystems, conservation science cannot altogether avoid difficult decisions; harming animals can be a necessary step toward a worthwhile goal. Despite these trade-offs, conservation organizations face a singular opportunity to reshape conservation into a discipline that promotes both the quantity of species and the quality of animal life. Although humans are exceptional in many ways, the once-popular belief that it is unscientific to ascribe emotions or thoughts to animals is now regarded as inconsistent with evolutionary theory, experimental evidence, and any reasonable burden of proof ([ 9 ][6], [ 10 ][7]). Commonalities in basic neural functioning across vertebrate species, ranging from fish to mammals, suggest similarities in experiential capacities ([ 9 ][6], [ 11 ][8]). Evidence indicates that the thalamocingulate division of the limbic system and the anterior cingulate cortex evolved prior to the radiation of mammals, with all studied mammals sharing seven basic emotional systems including joy, fear, grief, parental nurturance, and playfulness. Deep neurological similarities underpin the extensive use of mammalian models in medical research, including for depression and anxiety. Further, research indicates that convergent evolution of the mammalian cortex and avian pallium has led to similar neural architecture between birds and mammals ([ 12 ][9]), with birds exhibiting similar forms of some affective states, consciousness, and attachment-oriented behaviors. Recent research has also demonstrated that various animal species are cognitively sophisticated, with findings including tool use in diverse taxa; spontaneous insight and innovative behavior; self-recognition and metacognition; collaboration to solve unfamiliar tasks; planning for future events; political strategy; empathetic concern; and the ability to recognize hundreds of human words (see supplementary materials). The accumulating scientific evidence that animals have vibrant inner lives was anticipated by modern philosophers, the field of animal welfare science, and numerous world cultures that have accorded moral relevance to the quality of animal life. Yet with limited exceptions, the most prominent international conservation organizations do not attempt to promote animal welfare in their mission or vision statements or to safeguard animal welfare in their readily available public policies. This contrasts with often robust ethics policies on a range of other social and environmental issues. From one perspective, the omission of animal welfare is befuddling. Conservationists must believe that animals deserve protection from human-induced harm; by combating habitat destruction and poaching, conservation often already promotes wild animal welfare. Officially recognizing the imperative of protecting animals as individuals could broaden conservation's constituency. Whereas the public often finds the value of biodiversity to be abstract and unrelatable, many people are concerned when human actions unnecessarily violate the freedom and well-being of wild animals. Conservation organizations have realized this, often using stories of human-induced suffering of individual animals to generate empathy and raise funds. Yet, owing to the pervasiveness of activities that compromise animal welfare, many conservation organizations could face a variety of political risks and programmatic complications if they were to officially endorse the legitimacy of animal welfare concerns. Conservation organizations often depend on a diverse coalition of political interests, including groups that habitually harm animals. For instance, the U.S. government is one of the largest bilateral sources of funding for international conservation largely because the U.S. Congress's International Conservation Caucus is among the largest bipartisan caucuses in the legislature, with many participants being vocal supporters of recreational hunting and fishing. For conservation organizations to acknowledge that killing animals for recreation might have moral implications ([ 4 ][10]) could complicate these politically important relationships in both halls of power and remote settings globally. There are well-evidenced concerns for how wild animals, especially wide-ranging species like elephants, some cetaceans, and carnivores, fare in captivity, but zoos can also inspire considerable support for wildlife conservation. Finally, conservation organizations and conservationists themselves (like other environmentalists) often regularly purchase factory farm products even though factory farms pose serious concerns about human-induced animal suffering. For conservation organizations, officially acknowledging the moral significance of animal welfare could complicate how many conservationists see themselves and generally cause discontent within their communities. Furthermore, conservation programming takes place in complex socioecological systems that pose practical trade-offs between animal welfare and biodiversity conservation or even human rights. At its extreme, efforts to curtail hunting and fishing in the world's poor rural areas could unjustly harm communities that rely on bushmeat or wild fish for their nutrition and livelihoods. Conservation groups can be seen as elitist, out-of-touch, or culturally oppressive where they oppose the killing of dangerous animals like elephants or traditional practices like subsistence whaling—such conflicts could become more common if conservation organizations consistently prioritize the interests of individual animals. In settings where wildlife tourism is not profitable, prohibiting sport hunting could deprive organizations of funding to protect wildlife from poaching, perversely leading to an increase in the killing of wildlife. Additionally, there are many examples of direct trade-offs between animal welfare and traditional conservation objectives like preventing extinction and maintaining ecosystem function. Invasive mammals—like goats on the Galapagos or feral cats on remote islands—suffer during eradication campaigns, but there may be no other way to secure the future for endangered native species. Programs to cull white-tailed deer similarly might be necessary to ensure the regeneration of forests in the eastern United States. Ecological research and reintroduction programs can also involve duress for the animals involved. Despite challenges posed by these trade-offs, conservation science should adjust its priorities in response to the overwhelming evidence that animals think and feel. Only explicit consideration of animal welfare in decision-making can ensure that conservation organizations do not unnecessarily compromise the well-being of individual animals. As a community, conservation organizations should set in motion three processes to (i) develop consensus principles, (ii) build the evidence base to identify best practices, and (iii) develop advisory institutions that can advance best practices. Each of these should engage diverse voices, including representatives from different cultures, countries with diverse political realities, and researchers and practitioners from both animal welfare science and conservation. The process of developing consensus principles to bring animal welfare concerns into conservation science has already begun, with ideas coming from national regulatory bodies, nongovernmental organizations concerned with wild animal welfare, the World Association of Zoos and Aquariums ([ 13 ][11]), animal welfare experts ([ 14 ][12], [ 15 ][13]), and the burgeoning compassionate conservation movement ([ 3 ][2], [ 4 ][10]). Conservation and animal welfare organizations should collaborate to systematically refine and select practically applicable ethical principles. Given the diverse cultural practices and economic systems that involve harm to animals, prohibitions on animal captivity, killing animals, and eating meat are unlikely to gain consensus support—but that need not prevent constructive discussions on minimizing human-induced suffering of animals, general agreement to minimize suffering during killing, and principles guiding the circumstances when killing animals might be acceptable. Animal welfare principles can alert conservationists to when the harm an activity causes to individual animals outweighs the benefits to biodiversity. Second, international conservation and animal welfare organizations should fund the development of an evidence base for how best to engage with wildlife in a way that minimizes avoidable suffering. Again, scientists have begun this process ([ 4 ][10], [ 13 ][11], [ 14 ][12])—but the evidence compiled must come from more diverse settings and situations and reflect practical limitations and trade-offs faced by conservation organizations in places where even human rights are not adequately realized. In addition to improving conservation practice, such evidence would help animal welfare organizations recognize where the protection of biodiversity, ecological function, and local communities might necessitate harming individual animals. This evidence review process would also highlight areas of research that could help resolve ethical dilemmas posed by conservation programming. International conservation bodies should also work with animal welfare scientists to establish advisory committees that review (voluntarily submitted) conservation project proposals to assess whether they satisfy principles of animal welfare. The process could be modeled as a voluntary version of the Institutional Animal Care and Use Committee that reviews animal research in the United States, working to promote best practices, build precedent, and collect real-life cases that can improve the evidence base. The committees' recommendations should provide a basis for informed debate about the trade-offs between wildlife conservation and animal welfare, helping better define whether the suffering of individual animals might be commensurate with conservation benefits ([ 14 ][12]). Over time, the cumulative experience of these committees should allow conservation organizations to recommend evidence-based animal welfare safeguards that can fit into the broader category of social and environmental safeguards, much like policies striving to minimize carbon emissions or protect human rights in conservation and development. Inevitably, these processes will take time. In the meanwhile, conservation organizations can take two steps toward building a better world for all animals: publicly commit to considering animal welfare in their decisions, and adopt policies against the purchase of factory farm meat where less harmful alternatives are available. Given the implications of factory farming not just for animal welfare but also for climate change and biodiversity, such action would further demonstrate the sincerity of conservation organizations' pursuit of a more just and sustainable planet. [science.sciencemag.org/content/369/6504/629/suppl/DC1][14] 1. [↵][15]1. M. E. Soule , Bioscience 35, 727 (1985). [OpenUrl][16][CrossRef][17][Web of Science][18] 2. 1. P. Kareiva, 2. M. Marvier , Bioscience 62, 962 (2012). [OpenUrl][19][CrossRef][20][Web of Science][21] 3. [↵][22]1. D. Ramp, 2. M. Bekoff , Bioscience 65, 323 (2015). [OpenUrl][23][CrossRef][24] 4. [↵][25]1. A. D. Wallach et al ., Conserv. Biol. 32, 1255 (2018). [OpenUrl][26] 5. [↵][27]1. P. 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[OpenUrl][49][CrossRef][50][PubMed][51][Web of Science][52] 13. [↵][53]1. D. J. Mellor et al ., Caring for Wildlife: The World Zoo and Aquarium Animal Welfare Strategy (WAZA Executive Office, 2015). 14. [↵][54]1. S. Dubois et al ., Conserv. Biol. 31, 753 (2017). [OpenUrl][55] 15. [↵][56]1. J. O. Hampton et al ., Conserv. Biol. 33, 751 (2019). [OpenUrl][57] Acknowledgments: We thank H. Telkänranta, N. Shah, S. Sekar, N. Mohapatra, D. Mistree, M.Malik, A.Lerner, K. Kolappa, S. Kishore, P. Hannam, G. Fricchione, M. Doshi, P. Chanchani, and four anonymous reviewers. This piece reflects the views of the authors and not the official positions of their organizations. 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Irish agtech company Cainthus uses vision technology to improve dairy herd management. Ireland's multi-generations of dairy farmers know a thing or two about raising dairy cows. Its more than 18,000 dairy farmers tend 1.4 million animals and are recognized globally for productivity and quality. So, it's no surprise that an Irish agtech company called Cainthus would invent a way to use artificial intelligence--the same technology developed for terrorist detection of humans--to manage dairy cows. At its simplest, Cainthus' technology has been described as facial recognition for cows, but Cainthus CEO Aidan Connolly explains that it is actually much more.