Crop Recommendation and Crop Yield Estimation Using Machine Learning and IoT

Abstract

In the contemporary landscape of agriculture, the amalgamation of cutting-edge technologies such as machine learning (ML) and Internet of Things (IoT) has emerged as a transformative force, revolutionizing traditional farming practices. This paper introduces a comprehensive framework for crop recommendation and yield estimation, leveraging the power of ML algorithms and IoT devices to optimize agricultural operations. The dataset utilized in this study was meticulously curated from reputable sources including Kaggle and Google, encompassing a vast array of agricultural parameters ranging from soil characteristics to climate conditions. This rich dataset serves as the foundation for training and validating ML models, enabling robust analysis and prediction. A diverse range of ML algorithms was employed to process and interpret the dataset, with the objective of identifying the most proficient algorithm for crop recommendation and yield estimation tasks. Through rigorous experimentation and comparative analysis, the algorithm exhibiting superior performance across multiple evaluation metrics was meticulously chosen. Moreover, to bridge the gap between sophisticated ML techniques and practical application in the field, a user-friendly interface was developed using Flask, a micro web framework for Python. This intuitive interface empowers stakeholders to seamlessly access the trained model, facilitating real-time crop recommendations and yield estimations based on input parameters. The culmination of this research endeavor presents a paradigm shift in agricultural management, offering stakeholders unprecedented insights into crop selection and productivity optimization. By harnessing the potential of ML and IoT technologies, this framework not only enhances decision-making processes but also contributes to sustainable agricultural practices and global food security.