PSIX-13 Enhancing livestock health monitoring: AI driven approaches for anemia detection in small ruminants.
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».