The livestock sector is an essential component of the global economy. Farmers are losing interest in this profession as animals suffer from a variety of bad health conditions, unpredictable fatal illnesses. The temperature and humidity of the farm have a stronger impact on the health of the cattle. Monitoring the health of dairy cattle and the environmental state of farms can assist to tackle these concerns. In this research, we describe a system that allows farmers to monitor livestock health metrics including body temperature as well as environmental data like temperature, humidity, light levels, quality of air, and dampness. The device will automatically maintain optimum environmental conditions for the livestock in addition to monitoring environmental parameters. Our suggested method would assure adequate water supply to the cattle in order to increase milk production supply. Three ESP32 microcontrollers are utilized as clients in this system to sense different health and environmental aspects, and an ESP32 microcontroller is used as a server to wirelessly link the three ESP32 clients. Through a web application, health and environmental factors may be accessed on the internet. It may also be accessed remotely on a mobile phone. This revolution in advanced technological farm automation will help to improve productivity by reducing the need for human intervention. Finally, the proposed solution would assist in boosting productivity while saving the farmer's time and effort.

Optimizing energy management for household appliances is essential for maximizing domestic energy utilization and enabling preventive maintenance. Recent studies indicate that traditional forecasting approaches frequently lack the necessary accuracy and real-time learning capabilities required for effective management of household energy. This study demonstrates the implementation of a comprehensive strategy that integrates Internet of Things (IoT) data, machine learning (ML), and explainable artificial intelligence (XAI) to improve the accuracy and interpretability of predicting energy usage in residential buildings. Our research focuses on the rising issues faced by IoT-based smart systems, partic- ularly the deficiencies in the performance of current solutions. Therefore, as compared to the other 17 models that were examined, polynomial regression demonstrated outstanding performance. Our solution utilizes a non-intrusive sensor to collect data without disrupting its operation. Real-time data collecting is achieved through a Flask-based web page with Ngrok for external access.The efficacy of the proposed system was assessed using many metrics, yielding highly satisfac- tory results: the root mean square error (RMSE) was 0.03, the mean absolute error (MAE) was 0.02, the mean absolute percentage error (MAPE) was 0.04, and the coefficient of determination (R²) was 0.9989. However, modern cutting-edge methods still face considerable hurdles when it comes to interpretability. In order to tackle these problems, we include XAI techniques such as SHAP and LIME. Explainable Artificial Intelligence (XAI) improves the interpretability of the model by elucidating the impact of various variables on energy consumption forecasts. Not only does this increase the effectiveness of the model, but it also promotes comprehension of the data and enables them to identify the elements that influence home energy usage.