Machine Learning Algorithms for Data Mining and Predictive Analytics in Precision Agriculture

Artificial intelligence is a global topic that holds significant importance in recent years. It sparks debates about its usage, but despite the contradictions that arise, there are many benefits to applying it in various areas. In this paper, the authors investigate the possibilities of implementing machine learning algorithms to help optimize crop management for precision agriculture. Meeting current food demands becomes an increasingly challenging task as the population grows. Applying machine learning using IoT data analytics in the agricultural sector will bring forth additional advantages, increasing not only the quantity but also the quality of production from crop fields to meet the rising food demands. The main objective of the paper is to employ various machine learning algorithms for predictive modeling and subsequently develop a robust model capable of making accurate predictions on unseen agricultural data. The research methodology involves preprocessing the acquired dataset, including data cleaning and feature engineering to enhance model performance. The authors systematically apply a range of machine learning algorithms, such as Logistic regression, Decision trees, Naïve Bayes, and k-nearest Neighbor, to identify the most effective approach for crop yield prediction. For better model performance, boosting techniques of machine learning are also implemented. Cross-validation and performance metrics such as accuracy, confusion matrix, precision, recall and F1 score, are included to evaluate the accuracy of the models, aiming to highlight the strengths and weaknesses of each algorithm in the context of precision agriculture.