PSIX-13 Enhancing livestock health monitoring: AI driven approaches for anemia detection in small ruminants.

Abstract Because it affects economic productivity, food security around the world, and the well-being of animals, livestock health monitoring is an important part of sustainable agriculture. Blood tests and FAMACHA® scoring, which are traditional ways of detecting anemia, have been extensively employed in the management of parasite diseases, especially Haemonchus contortus in small ruminants. While effective, these methods present limitations such as subjectivity, inter-observer variability, and labor-intensive procedures, particularly in large-scale and resource-limited farming systems. Big Data analytics approaches such as Artificial Intelligence (AI) and Machine Learning (ML) methodologies, particularly Natural Language Processing (NLP), are establishing themselves as important instruments for automating, standardizing, and enhancing anemia diagnosis via multi-sensor data integration. This work performed a systematic literature review (SLR) to evaluate the efficacy of AI-driven methodologies, including NLP, deep learning, and classification models (CNNs, SVMs, BPNNs), in improving anemia detection. A structured search across databases (Web of Science, PubMed, Scopus, Google Scholar) identified key advancements in AI-powered FAMACHA® scoring, RF wave-based real-time health monitoring, and BIA applications in parasite detection. Analysis of 1,928 research nodes and 2,897 citation links revealed increasing interest in AI-driven livestock diagnostics, with NLP techniques emerging as a key tool for extracting insights from unstructured veterinary data and scientific literature. Machine learning models have also transformed FAMACHA® scoring by removing human subjectivity. Convolutional neural networks (CNNs) trained on eye mucosa images achieved 92.1% classification accuracy, surpassing traditional FAMACHA® assessments. AI-assisted scoring eliminates observer bias, enhances disease prediction, and enables automated decision-support systems for anemia detection. Similarly, RF-based ultra-wideband radar and RFID sensors allow remote, real-time health monitoring, offering new avenues for precision livestock management. Comparative keyword analysis highlighted 120 mentions of RF waves, 88 mentions of FAMACHA®, and 15 mentions of BIA, confirming that RF-based anemia detection has the most significant research investment. However, NLP remains an underutilized tool in livestock health analytics despite its potential to convert unstructured veterinary data into actionable insights. While promising, BIA, RF-based sensing, and NLP-driven AI models face adoption challenges. Environmental variables, including temperature, humidity, and breed-specific differences, influence BIA and RF signal precision, requiring regular calibration. Moreover, the economic viability and accessibility of AI-driven monitoring systems continue to be issues in commercial cattle management. Future research ought to concentrate on the integration of NLP with multi-sensor AI models, adaptive deep learning algorithms, and mobile veterinary applications to improve scalability, cost, and accessibility in animal health monitoring. Integrating AI-driven NLP with FAMACHA®, RF, and BIA can transform animal health monitoring into a precision-based, automated, and scalable diagnostic solution. These innovations will enhance sustainability, animal welfare, and economic productivity in response to increasing global food demand.