The estimated global population for 2050 is 9 billion, which implies an increase in food demand. Agriculture is the primary source of food production worldwide, and improving its efficiency and productivity through an integration with information and communication technology system, so-called “smart farming”, is a promising approach to optimizing food supply. This research employed bibliometric analysis techniques to investigate smart farming trends, identify their potential benefits, and analyze their research insight. Data were collected from 1141 publications in the Scopus database in the period 1997–2021 and were extracted using VOS Viewer, which quantified the connections between the articles using the co-citation unit, resulting in a mapping of 10 clusters, ranging from agriculture to soil moisture. Finally, the analysis further focuses on the three major themes of smart farming, namely the IoT; blockchain and agricultural robots; and smart agriculture, crops, and irrigation.

The traditional approach to weight measurement, which measures each sheep individually, is time-consuming, and sometimes, human error triggers other issues, such as data validation, sheep classification, etc. Most farmers or breeders nowadays still manage their livestock traditionally, which is inefficient. We proposed that the Livestock Live Monitoring System is designed to collect measured data in real-time and display data in graphics; these models combine Bluetooth Low Energy as a wearable sensor to identify animals and Smart Weight Measurement to deliver weight and health data to the cloud system. This system aims to measure livestock and store the data in the server application so livestock monitoring can be done in real-time and remotely. The technology used in this system is ESP32, Load Cell, Bluetooth Low Energy, and Message Queue Telemetry Transport Protocol. Wearable devices act as an identification tag for livestock, and a smart weight scale is used to weigh the livestock and integrate it with the system. Two sheep are used as experiment objects, and their measured weight is compared to their weight when measured traditionally using a conventional scale. Based on the experiment, the weight data measured using the system has an accuracy of 99.82% for sheep number 1 and 99.17% for sheep number 2. This proposed system provides many benefits, including real-time livestock monitoring, cost efficiency, and an efficient feeding system for sheep using weight data.