Computer vision is becoming a core technology in PLF, enabling non-contact monitoring, phenotyping, and decision support at both animal and group levels. This umbrella review synthesised review-level evidence from 112 reviews on cattle, pig, and poultry systems, covering visual applications for identification, phenotyping, health, behaviour, locomotion, body condition, growth, reproduction, mortality, and resource use. To preserve the review as the bibliographic unit while capturing multi-topic evidence, multi-label coding generated 328 review-domain assignments across six PLF computer vision domains. Health/stress and posture/activity were the most frequently reviewed areas; however, these patterns reflect review coverage rather than evidence strength. Benchmarking reporting averaged 11.09 out of 14 criteria, corresponding to 79.2%, but validation-critical items, including class balance, annotation protocols, validation splits, and farm or site reporting, remained less complete. Methodological confidence was limited: AMSTAR 2 classified 95 reviews as critically low and 17 as moderate, while ROBIS classified 78 as high, 17 as unclear, and 17 as low risk of bias. The corpus-level candidate-reference overlap estimate was 0.122% and is retained only as a sensitivity estimate, not as formal primary-study CCA or evidence of independence. Review-derived performance and readiness tiers identified a mean descriptive performance-to-deployment gap of 0.63 across 328 assignments. Overall, reported model performance continues to exceed documented farm-deployment evidence, highlighting the need for stronger external validation, transparent benchmarking, workflow integration, economic assessment, and user-readiness evidence before routine deployment claims can be supported.
Agentic artificial intelligence is emerging as an extension of Precision Livestock Farming by linking perception, reasoning, planning, and bounded action within human-supervised livestock-housing workflows. This review synthesizes 90 publications on agentic AI, multi-agent systems, retrieval-augmented generation, large language models, foundation models, robotics, digital twins, simulation, computer vision, cyber-physical control, and related enabling technologies for livestock-housing management. We propose a Perception–Reasoning–Action–Safety (PRAS) loop and an Agentic Livestock Housing Readiness Scale to classify systems from passive monitoring and advisory decision support to supervised, safety-constrained closed-loop operation. A staged benchmarking perspective is also used to integrate algorithmic performance, biological relevance, safety, auditability, economic feasibility, and human–AI interaction. Current evidence is strongest for perception, advisory reasoning, natural-language data access, welfare-risk interpretation, and simulation-supported decision support, whereas robust barn-wide autonomous control remains largely unvalidated. Technology categories were coded non-exclusively; therefore, publication frequencies indicate representation within the selected corpus rather than effectiveness, evidence strength, or deployment readiness. Across species, dairy cattle provide the most developed evidence base, poultry studies mainly address environmental comfort and nutrition support, swine systems emphasize simulation-based precision feeding, and small-ruminant evidence remains concentrated in advisory tools and contextual embodied monitoring. Overall, agentic AI in livestock housing is currently more mature as an orchestration, explanation, and decision-support layer than as an autonomous control technology. By distinguishing direct housing applications, semi-agentic prototypes, and enabling technologies, this review clarifies the gap between current evidence and deployable autonomy. Progress towards higher readiness will require cross-farm validation, biological plausibility, source-grounding audits, safety assurance, interoperability, economic assessment, transparent benchmarking, and explicit human oversight.
This study aimed to investigate Triumpheta cordifolia A. Rich. gum extract as a clarifying agent in sorghum wort brewing. The biological materials used in this study included the Safrari sorghum cultivar and the bark of T. cordifolia. We determined the key physicochemical characteristics, such as turbidity, pH, Brix (soluble solids), as well as color and polyphenol content of Safrari sorghum wort. The response surface methodology employing a Box-Behnken experimental design with three factors was used to investigate the impact of adding T. cordifolia gum extract to Safrari sorghum wort during its clarification. The physicochemical properties of Safrari sorghum demonstrated satisfactory results for malting. The sorghum was malted and subsequently brewed, and the resulting wort was clarified according to the experimental design. The design factors included the volume of the gum extract (from 1.5 to 3.5 mL) for a fixed wort volume of 200 mL, the stirring speed (from 30 to 160 rpm), and the settling time (from 10 to 60 min). The responses were analyzed during 15 trials and included turbidity, pH, Brix, color, and polyphenol content. These responses were subjected to mathematical and statistical modeling. All obtained models were validated against several criteria, including the correlation coefficient (R2 > 0.90), the absolute average deviation (absolute average deviation < 0.3), as well as the bias and accuracy factors (Af andBfbetween 0.75 and 1.25). The optimal results suggested that the gum extracted from T. cordifolia could be effectively used as a clarifying agent in the brewing industry.
Aquaculture is developing in many countries to meet the rising protein demand associated with a rapidly growing human population. However, intensification of production brings with it challenges such as deteriorating water quality and increasing disease outbreaks. These conditions lead to an increased risk of infectious diseases and led to higher mortality rates, which causes economic losses. Lactococcosis, particularly that caused by the Gram-positive bacterium Lactococcus garvieae, is one of the most common of these problems and can cause significant losses in various fish species. The intensive use of antibiotics for control purposes creates additional risks in terms of antimicrobial resistance, environmental pollution, and food safety. Therefore, natural feed additives have gained importance in recent years. Certain additives that support immune responses and increase disease resistance in fish have become prominent. Among these, phytobiotics, probiotics, prebiotics, and synbiotics are the most well-known and effective. Their widespread availability, lower cost, and environmental safety make these additives considered an alternative approach to aquaculture. Studies show that these additives strengthen both innate and adaptive immune responses, reduce infection severity, and reduce mortality associated with L. garvieae infections. However, there are still gaps in knowledge regarding how these substances regulate mechanisms such as the immune system, inflammatory processes, antioxidant defenses, and interactions with pathogens. This review aims to clarify these mechanisms by bringing together scientific data obtained in recent years. It also discusses how the information obtained can contribute to the development of safer feed additive strategies and the development of new vaccine approaches. This aims to support the establishment of a more sustainable production structure in the aquaculture sector.
Abstract We study gravitational quasinormal modes of the Hayward spacetime, a regular black-hole geometry that also admits an interpretation as an effective quantum-corrected solution within asymptotically safe gravity. Using both the higher-order WKB method supplemented with Padé approximants and time-domain integration with Prony analysis, we obtain accurate spectra for axial perturbations and explore the impact of the quantum parameter $$\gamma $$ γ . We find that increasing $$\gamma $$ γ systematically raises the oscillation frequencies while reducing the damping rates, making the ringdown longer lived. For the first overtone, the effect of $$\gamma $$ γ is noticeably stronger than for the fundamental mode, providing an indication of the so-called “outburst of overtones” previously observed for test fields, and pointing to the particular sensitivity of subdominant modes to near-horizon quantum corrections. In addition, the analytic approximation for quasinormal modes are obtained in the form of expansion beyond the eikonal limit.
