Efficient Machine Learning Regression Algorithm using Naïve Bayes Classifier for Crop Yield Prediction and Optimal Utilization of Fertilizer

doi:10.23940/ijpe.22.01.p6.4755

Int J Performability Eng ›› 2022, Vol. 18 ›› Issue (1): 47-55.doi: 10.23940/ijpe.22.01.p6.4755

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Efficient Machine Learning Regression Algorithm using Naïve Bayes Classifier for Crop Yield Prediction and Optimal Utilization of Fertilizer

C Chandana^a,* and G Parthasarathy^b

^aComputer Science and Engineering, REVA University, Bangalore, Karnataka, 560064, India;
^bSchool of Computing and Information Technology, REVA University, Bangalore, Karnataka, 560064, India

Contact: * E-mail address: punyachandu@gmail.com
About author:Mrs. Chandana C is currently working as an Assistant Professor in the department of Computer Science and Engineering, S J B Institute of Technology Bengaluru. She received his Bachelor’s Degree in Computer Science and Engineering from Coorg Institute of Technology, South Kodagu, Karnataka, India during the year 2008 and M. Tech in Computer Science and Engineering from SJCIT, Bengaluru, Karnataka, India during the year 2014. She is having close to 13 years of teaching experience.
Dr. G. Parthasarathy is an Associate Professor at School of Computing and Information Technology, REVA UNIVERSITY, Bangalore. He has pursued his Doctorate (Ph. D.) degree in Computer Science & Engineering department at Sathyabma University, Chennai, India in 2017. He received the M.E. degree from the Department of Computer Science and Engineering, Sathyabama University, Chennai, India in 2006. He has vast experience of more than 15 years in teaching under various colleges and Universities. He has published thirty papers in International level Journals and Conferences. He has published 8 patents in various domains. He is playing the role of reviewer for about four International reputed Journals like J. of Information & Knowledge Management (JIKM) and Conferences. He is a Microsoft Certified Professional and he is a member of Computer Society of India (CSI) and ACM. His research interests are in Data Mining, Web Mining, Big Data Analytics, Cloud Computing, Sentiment Analysis and Information Retrieval.

Abstract

Abstract: Objectives: the crop yield prediction rate has been improved using a machine learning regression algorithm (MLR) using a Naïve Bayes classifier. The optimal utilization of fertilizer is enhanced based on a potential of hydrogen (pH) value and alkalinity of soil. Method:the implementation of the proposed algorithm has been carried out by considering the various types of soils, percentage of nutrients like potassium (Pm), nitrogen (N) and Phosphorus (P) in the soil in that region, four to five years of data analysis of the amount of rainfall, atmosphere humidity, and crop yield to fertilizer utilization ratio of a particular region and duration. Finding: The designed system has a model to accurately and precisely predict crop yield and give required recommendations to the end-user regarding fertilizer ratio depending on soil and weather conditions of the land to improve the crop yield, thereby increasing the revenue of farmers. Novelty: the farmers have to determine the expected crop yield and required fertilizer by themselves. It is achieved more accurately by implementing the proposed algorithm compared to the existing Random Forest (RF) algorithm using data mining Decision support system. The analysis has been carried out on the above-mentioned attributes of data by adopting the data pre-processing, data testing, and validation to achieve a precise and accurate crop yield and fertilizer utilization model.

Key words: random forest algorithm, back-propagation algorithm, machine learning regression, naïve bayes classifier, sequential minimal

C Chandana and G Parthasarathy. Efficient Machine Learning Regression Algorithm using Naïve Bayes Classifier for Crop Yield Prediction and Optimal Utilization of Fertilizer [J]. Int J Performability Eng, 2022, 18(1): 47-55.

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Efficient Machine Learning Regression Algorithm using Naïve Bayes Classifier for Crop Yield Prediction and Optimal Utilization of Fertilizer

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