Machine Learning Based Agricultural Profitability Recommendation Systems: A Paradigm Shift in Crop Cultivation.

Nilesh P. Sable; Rajkumar V. Patil; Mahendra Deore; Ratnmala Bhimanpallewar; Parikshit N. Mahalle

doi:10.9781/ijimai.2024.10.005

Authors

Nilesh P. Sable Bansilal Ramnath Agarwal Charitable Trust's Vishwakarma Institute of Information Technology (India).
Rajkumar V. Patil DES Pune University
Mahendra Deore Malla Reddy Engineering College for Women
Ratnmala Bhimanpallewar Bansilal Ramnath Agarwal Charitable Trust's Vishwakarma Institute of Information Technology (India).
Parikshit N. Mahalle Bansilal Ramnath Agarwal Charitable Trust's Vishwakarma Institute of Information Technology (India).

DOI:

https://doi.org/10.9781/ijimai.2024.10.005

Keywords:

Agriculture, Cultivation, Data Analysis, Machine Learning, Regression

Abstract

In India, the demand for fruits and vegetables has been consistently increasing alongside the rising population, making crop production a crucial aspect of agriculture. However, despite the growing demand and potential profitability, farmers have been slow to transition from traditional food grain crops to fruits and vegetables. In this paper, we explore the changing demands of food categories in India, highlighting the shift towards increased consumption of fruits and vegetables. Despite the potential benefits, farmers face various challenges and uncertainties associated with cultivating these crops. To address this, we propose the use of Machine Learning (ML) and Deep Learning (DL) techniques to analyze historical market price data for fruits and vegetables from 2016 to 2021 and predict future prices. This accurate prediction system will aid farmers in deciding which crops to grow and when to harvest, ultimately maximizing profits.

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