Local Industrialization Based Lucrative Farming Using Machine Learning Technique - Abstract & Framework Overview

Aug 3, 2026

Crop prediction attracts significant research attention because choosing the right crop affects agricultural business outcomes. Traditional decisions rely heavily on ranchers’ experience, but atmospheric fluctuations make correct selection difficult. The proposed approach targets the Satara, Sangli, and Kolhapur regions of Maharashtra by using feature selection and machine learning classifiers. Random Forest is used within a final framework to predict suitable crops from climate and soil-related parameters and to support profit-oriented industrialization alongside fertilizer and compost recommendations.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages7

Reading Time18 min

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Introduction

Related Work

Local Industrialization Based Lucrative Farming Using Machine Learning Technique Sakshi A. Patil1, Dr. Mrunal S.Bewoor2, Mrs. Sheetal S. Patil3, Dr. Rohini B. Jadhav4, Dr. Avinash M. Pawar5, Mrs. Sonali D. Mali6, Dr. Amol K. Kadam7 1Department of Computer Engineering, Bharti Vidyapeeth Deemed to be university College of Engineering Pune, India [sapatilpg21-comp@bvucoep.edu.in](sapatilpg21-comp@bvucoep.edu.in) 2Department of Computer Engineering, Bharti Vidyapeeth Deemed to be university College of Engineering Pune, India [msbewoor@bvucoep.edu.in](msbewoor@bvucoep.edu.in) 3Department of Computer Engineering, Bharti Vidyapeeth Deemed to be university College of Engineering Pune, India [sspatil@bvucoep.edu.in](sspatil@bvucoep.edu.in) 4 Department of Information Technology Bharti Vidyapeeth Deemed to be university College of Engineering Pune, India [rbjadhav@bvucoep.edu.in](rbjadhav@bvucoep.edu.in) 5Bharati Vidyapeeth's College of Engineering for Women, Pune, India [avinash.m.pawar@bharatividyapeeth.edu](avinash.m.pawar@bharatividyapeeth.edu) 6Department of Information Technology Bharti Vidyapeeth Deemed to be university College of Engineering Pune, India [sdmali@bvucoep.edu.in](sdmali@bvucoep.edu.in) 7Department of Computer Engineering, Bharti Vidyapeeth Deemed to be university College of Engineering Pune, India [akkadam@bvucoep.edu.in](akkadam@bvucoep.edu.in) Abstract—In recent times, agriculture have gained lot of attention of researchers. More precisely, crop prediction is trending topic for research as it leads agri-business to success or failure. Crop prediction totally rest on climatic and chemical changes. In the past which crop to promote was elected by rancher. All the decisions related to its cultivation, fertilizing, harvesting and farm maintenance was taken by rancher himself with his experience. But as we can see because of constant fluctuations in atmospheric conditions coming to any conclusion have become very tough. Picking correct crop to grow at right times under right circumstances can help rancher to make more business. To achieve what we cannot do manually we have started building machine learning models for it nowadays. To predict the crop deciding which parameters to consider and whose impact will be more on final decision is also equally important. For this we use feature selection models. This will alter the underdone data into more precise one. Though there have been various techniques to resolve this problem better performance is still desirable. In this research we have provided more precise & optimum solution for crop prediction keeping Satara, Sangli, Kolhapur region of Maharashtra. Along with crop & composts to increase harvest we are offering industrialization around so rancher can trade the yield & earn more profit. The proposed solution is using machine learning algorithms like KNN, Random Forest, Naïve Bayes where Random Forest outperforms others so we are using it to build our final framework to predict crop. Keywords-prediction; machine learning models; cultivation; feature selection models; industrialization. I. INTRODUCTION The use of machine learning technologies in different fields for the growth of industry & country is rapidly increasing nowadays. Growth of country also depends on food industry because with increasing population and urbanization available agricultural land is limited & will remain same. Nowadays farmers financial situation is also highly unstable because not taking right crop at right time and right place. Because of this they have to face financial loss very often. So, the aim is to get more and more yield from that available land with maximum profit. For this we need to make changes in farming methodology which we have been using traditionally. Problem with this is we don’t exactly know about many factors which affect the growth of crop. In recent years, researchers have been working on multiple technologies which c

FAQs

Why is crop prediction considered challenging for farmers?

Crop selection depends on climate and chemical changes, and atmospheric conditions fluctuate constantly. These variations make it difficult to conclude which crop will perform best at the right time and place.

What machine learning techniques are used in the proposed crop prediction framework?

The framework uses machine learning algorithms such as KNN, Random Forest, and Naïve Bayes. Random Forest is reported to outperform the others and is used for the final prediction model.

How does the research improve prediction quality before classification?

Feature selection models are used to transform underdone data into a more precise dataset. This improves the inputs used for crop prediction and aims for better overall performance.

Aug 3, 2026

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