• Chandagalu Shivalingaiah Santhosh Department of Computer Applications, JSS Science and Technology, University, JSS Technical Institutions Campus, Mysuru, 570006, Karnataka, India.
  • Kattekyathanalli Kalegowda Umesh Department of Information Science and Engineering, JSS Science and Technology University, JSS Technical Institutions Campus, Mysuru, 570006, Karnataka, India.



Machine Learning(ML), Ensemble, Coffee, Yield Prediction, Agronomic factors


Coffee is the most burned-through handled drink beside water, which is said to be the most exchanged cultivating product followed by oil in the entire globe. The two most significant sorts of coffee assortment filled in India are Arabica and Robusta out of 103 assortments of class coffee bean variety, which are economically exchanged around the planet. In this regard, we are taking major plantation crop in India i.e., Coffee for our research to explore and develop a predictive model for the development of coffee planters to take precise decisions in time during adverse situations in advance. Hence we propose a framework for coffee yield prediction which using machine learning ensemble approach to estimate the influence of agronomic factors to get a good coffee yield. Here, for our research work, the historic dataset is considered which is obtained from Central Coffee Research Institute (CCRI), Karnataka for the year (2008-2019). For the coffee yield prediction, we are considering agronomic factors like Age, Soil Nutrients: Organic carbon (OC), Phosphorus (P), Potassium (K), Alkaline (pH), Zone and Respective yield obtained in chikkamagaluru   region, Karnataka state, India. Different classifiers are used namely, Extra Tree Classifier, Random Forest Classifier, Decision Tree and Boosting Algorithms for prediction and performance of each is compared and analyzed. Our results shown that Extra Tree Classifier and Random forest (RF) classifier with a precision of 91% with good results based on performance metrics considered respectively is an effective and versatile machine-learning method compared to other algorithms used.


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How to Cite

Santhosh, C. S. ., & Umesh, K. K. (2023). AN ENSEMBLE APPROACH FOR COFFEE CROP YIELD PREDICTION BASED ON AGRONOMIC FACTORS . ASEAN Engineering Journal, 13(3), 29-38.