Agriculture Monitoring System Using IoT and ML Smart and Improved Farming

This research paper investigates the development of a Smart Agriculture Monitoring System using IoT technology to optimize agricultural processes. The project aims to enhance crop yield, reduce resource consumption, and provide real-time insights to farmers. The system concentrates on data preparation, anomaly detection, and classification to efficiently monitor and manage agricultural activities. It does this by utilizing machine learning and Python programming approaches. The suggested method incorporates Internet of Things sensors to gather information on a number of variables, including humidity, temperature, and soil moisture. Prior to being examined for anomalies suggestive of possible problems such as insect infestation or nutritional deficits, these data are preprocessed to guarantee accuracy and dependability. For classification jobs, machine learning techniques are used, which allows the system to classify data & give farmers useful insights. The Smart Agriculture Monitoring System enables farmers to make well-informed decisions, increase crop yield, and optimize resource consumption by offering real-time monitoring and analysis. Through encouraging effective resource management and proactive problem-solving in farming operations, the project supports sustainable agriculture practices.