Remote Monitoring and Control of Agricultural Systems Using IoT and Machine Learning

Cutting-edge technology is essential to improve resource use, output, and farming methods to meet climate change and other needs. This research uses IoT and machine learning to remotely track and operate agricultural infrastructure.The Internet of Things (IoT) lets several sensors strategically placed around the farm collect data simultaneously. These sensors assess temperature, humidity, and soil wetness, which determine crop health. The data is wirelessly delivered from a central location to a designated area for processing and analysis.Machine learning is used to interpret data. These algorithms can improve irrigation schedules, predict food yields, diagnose diseases, and offer insect control alternatives. Machine learning (ML) models improve by learning from data and adapting to environmental circumstances.The system allows remote farming monitoring using actuators and automated tools. An easy-to-use interface on desktops or mobile devices allows agricultural experts utilise machine learning models to regulate insecticides, modify irrigation water, and start harvesting remotely.The proposed technology improves farming productivity, durability, resource efficiency, and cost. It encourages farmers to make sensible decisions and manage their resources to better address environmental issues.To conclude, Machine Learning and the Internet of Things could considerably improve farming system remote monitoring and control. The development of new technologies in this field is crucial to its long-term success and ability to provide for future generations.