System Modeling for Prognostic Reasoning and Insight Exploration of Arecanut Crop Using Data Analytics and Formal Statistical Approach.

KARNATAKA (India); INDIA

Agriculture is the primary source of income for the majority of the Indian farming community. Plantation crops play a significant part in improving the farmers' economic condition. The proposed work aims to develop a system model for prognostic reasoning by analyzing the impact of fertilizer and irrigation on areca nut crop yield, as well as to predict diseases that may affect areca nut palms using data analytics and a formal statistical approach. The dataset is constructed by interacting with the farmers in the Mangaluru region of Karnataka, India. To find the optimal features, the formal statistical test chi-square is applied. The performance of various classifiers, such as Logistic Regression, Nave Bayes, Support Vector Machine, Decision Tree, and Random Forest, is examined during prognostic reasoning. For disease prediction and crop yield, the decision tree outperformed other classifiers with an accuracy of 96% and 95.86%, respectively. The most significant irrigation type and fertilizer for increasing areca nut crop yield are also identified. [ABSTRACT FROM AUTHOR]

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