In the current context, monitoring cattle health is critical for producing abundant milk to satisfy population growth demand and also for attaining sustainability. Traditional methods associated with cattle health must be strengthened in order to overcome the concern of detecting diseases based on the health condition. This problem has moved attention toward digital technologies such as the Internet of Things (IoT), artificial intelligence (AI), cloud computing, edge/fog computing, big data, blockchain, drones, robotics, and augmented reality (AR)/virtual reality (VR), as these technologies have proved for real-time monitoring, intelligent analytics, secure data distribution, and real-time visual experience. The purpose of this study is to examine and discuss many cattle health disorders, as well as to address the fundamental notion of digital technologies, as well as the significance of these technologies for cattle health. Furthermore, the article addressed the different devices that integrated IoT and AI for cattle health monitoring, in which the previous architecture of cattle health monitoring is presented. Based on the review, the article discusses the challenges and suggests recommendations that can be implemented for the future work

This study introduces a robust machine learning framework for predicting hydrochar yield and higher heating value (HHV) using biomass proximate analysis. A curated dataset of 481 samples was assembled, featuring input variables such as fixed carbon, volatile matter, ash content, reaction time, temperature, and water content. Hydrochar yield and HHV served as the target outputs. To enhance data quality, Monte Carlo Outlier Detection (MCOD) was employed to eliminate anomalous entries. Thirteen machine learning algorithms, including convolutional neural networks (CNN), linear regression, decision trees, and advanced ensemble methods (CatBoost, LightGBM, XGBoost) were systematically compared. CatBoost demonstrated superior performance, achieving an R ² of 0.98 and mean squared error (MSE) of 0.05 for HHV prediction, and an R ² of 0.94 with MSE of 0.03 for yield estimation. SHAP analysis identified ash content as the most influential feature for HHV prediction, while temperature, water content, and fixed carbon were key drivers of yield. These results validate the effectiveness of gradient boosting models, particularly CatBoost, in accurately modeling hydrothermal carbonization outcomes and supporting data-driven biomass valorization strategies. Graphical abstract

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