Intelligent Crop Management Optimization using Machine Learning Algorithms: A Linear Analytical Approach

This research paper explores Utilizing machine learning techniques in practice, to enhance crop management recommendations. It leverages historical and real-time data, encompassing weather conditions, soil characteristics, growth stages, and past information. A rigorous data prepossessing procedure is employed to generate a well-structured 1.31 lacs data set. Various methodologies are utilized to construct predictive. These models' effectiveness is evaluated using common assessment metrics, such as Recall, accuracy, precision, and F1-score. To enhance the reliability and transparency of the recommendations, ensemble methods such as Naive Bayes, Support vector machines, decision trees, gradient boosting, and random forests, Linear Regression, and k-Nearest Neighbors}, along with interpretative techniques, are incorporated. The results provide insights into different algorithms and predict crucial agricultural activities such as planting, irrigation, fertilization, and pest control. Among all the machine learning models considered, Naive Bayes emerges as the best-performing model, achieving perfect scores of 1.0 in accuracy, precision, recall, and F1-scores. The second-best machine learning model is Random Forests, which follows closely behind with Achieving remarkable results reaching 0.99. These prognostic parameters have a big impact on agricultural productivity and sustainability. In order to improve crop management productivity and resource efficiency, this study promotes the use of data-driven decision-making in agriculture.