Vol. 8 No. 07 (2026): Volume 08 Issue 07

Mastitis remains one of the most prevalent and economically significant diseases affecting dairy cattle worldwide, resulting in substantial reductions in milk production, deterioration of milk quality, increased veterinary expenditures, premature culling, and diminished animal welfare. Although conventional diagnostic techniques such as somatic cell count analysis, bacterial culture, and clinical examination remain widely adopted, these approaches often identify the disease after physiological damage has already occurred. The increasing availability of precision livestock farming technologies, automatic milking systems, wearable sensors, and continuous physiological monitoring has created unprecedented opportunities for developing intelligent predictive systems capable of identifying mastitis before the appearance of obvious clinical symptoms. Recent advances in artificial intelligence have demonstrated considerable success in disease prediction through machine learning algorithms; however, existing systems frequently rely on isolated predictive models that inadequately exploit multidimensional relationships among cows, environmental variables, production characteristics, and historical disease patterns. This study proposes a predictive analytics architecture integrating machine learning and collaborative filtering to improve the accuracy and reliability of mastitis detection in dairy cows. The proposed conceptual architecture combines heterogeneous sensor data, historical herd records, behavioral monitoring, milk quality parameters, and collaborative similarity learning to generate individualized disease-risk assessments. Machine learning algorithms perform nonlinear pattern recognition, while collaborative filtering captures latent similarities among animals exhibiting comparable physiological and production characteristics. Together, these complementary mechanisms enhance prediction robustness and reduce false-positive and false-negative classifications. The paper synthesizes recent developments in machine learning, automated milking systems, risk assessment methodologies, ensemble learning, deep learning, and sensor-based monitoring exclusively from the provided literature. A comprehensive methodological framework is developed to explain data acquisition, preprocessing, feature engineering, collaborative similarity modeling, predictive analytics, validation strategies, and deployment considerations. The study further evaluates the theoretical advantages, implementation challenges, and practical implications of integrating collaborative intelligence into livestock disease prediction. The proposed architecture contributes a generalized decision-support framework capable of supporting veterinarians, dairy producers, and precision livestock management systems in achieving earlier intervention, improved herd health, optimized resource utilization, and enhanced sustainability within modern dairy farming.

This study investigated the effects of seed coating and the application of macro- and micronutrient fertilizers on the key biometric characteristics of spring wheat (Triticum aestivum L.) grown on irrigated meadow alluvial soils in the Nukus district of the Republic of Karakalpakstan. The effects of the treatments on plant height, length of the uppermost internode, spike length, and the number of spikelets per spike were evaluated. The results showed that seed coating combined with the application of macro- and micronutrient fertilizers before sowing significantly improved the biometric characteristics of the plants. In particular, the treated variants exhibited greater plant height, longer uppermost internodes, increased spike length, and a higher number of spikelets per spike compared with the untreated control. The findings demonstrate that pre-sowing seed treatment together with the rational application of macro- and micronutrient fertilizers is an effective agronomic practice for promoting plant growth, improving yield components, and enhancing the productivity of spring wheat under the soil and climatic conditions of the Republic of Karakalpakstan.