TY - JOUR
KW - Agriculture
KW - Cultivation
KW - Data Analysis
KW - Machine Learning
KW - Regression
AU - Nilesh P. Sable
AU - Rajkumar V. Patil
AU - Mahendra Deore
AU - Ratnmala Bhimanpallewar
AU - Parikshit N. Mahalle
AB - 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.
IS - Regular issue
M1 - 1
N2 - 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.
PY - 2024
SE - 39
SP - 39
EP - 54
T2 - International Journal of Interactive Multimedia and Artificial Intelligence
TI - Machine Learning Based Agricultural Profitability Recommendation Systems: A Paradigm Shift in Crop Cultivation
UR - https://www.ijimai.org/journal/bibcite/reference/3505
VL - 9
SN - 1989-1660
ER -