Improved Crop Yields and Resource Efficiency in IoT-based Agriculture with Machine Learning

Despite popular belief, agricultural research today is more based on hard evidence; exact; precise, and rigorous than ever before. Almost every industry has been disrupted by the spread of IoT-based technologies, including urban planning, healthcare, the electricity grid, the home, and agriculture, frequently referred to as "smart agriculture". Machine learning (ML) and IoT data analytics in agriculture can boost crop yields to meet rising food demand. These revolutionary developments are upending standard agricultural practices and giving rise to new and finest opportunities, but with some drawbacks. Optimal agriculture output requires this research seeks to develop an effective and precise system that uses Crop selection choices made using algorithms that utilize Internet of Things (IoT) sensors and ML Therefore, this paper, proposed an ensemble model using machine learning for crop prediction based on IoT data which is collected from the IoT sensors using the PLX-DAQ tool. There are several suggested machines learning models, including "Naive Bayes, Decision Tree, Random Forest Support Vector Machine, and K-Nearest Neighbour,". According to the experimental findings, ensemble learning had the greatest accuracy of 97.45% for predicting early crop yields. The results of this research will significantly increase the dependence on data for choices relating to climate change and agricultural practices.