Enhancing Agricultural Decision-Making through Data Analysis: Predicting Crop Health Outcomes

Abstract

This research employs advanced data analysis techniques to predict crop health outcomes during harvest seasons, with a focus on insect count, pesticide use, and soil type. The study encompasses two main components: feature correlation and predictive modeling. Feature engineering techniques are applied to capture variations in pesticide use and insect infestation, enhancing predictive capabilities. Ensemble methods, including Random Forest, XGBoost, and Decision Trees, are employed to forecast patterns of crop damage based on identified trends. Decision Trees exhibit robust capabilities, achieving an impressive accuracy rate of 90.03%. Random Forest excels with a robust accuracy of 90.35%, highlighting its classification abilities. XGBoost stands out with an accuracy rate of 86.51%. In contrast, Logistic Regression, Naive Bayes, and Convolutional Neural Networks face challenges, displaying lower accuracy. The evaluation further emphasizes the strength of ensemble methods and Decision Trees through precision, recall, and F1-Score metrics, providing a comprehensive understanding of relationships within pesticide damage. The framework of the study introduced in this paper can be seen as a major step forward with regard to agricultural decision-making. We present actionable strategies to enhance crop health while reducing damage through the integration of feature correlation, predictive modeling and precise evaluation metrics. The innovativeness is in the use of ensemble methods and Decision Trees that are implemented to promote informed decision-making among stakeholders through a sustainable approach to agriculture.