Published May 30, 2025 | Version CC-BY-NC-ND 4.0
Journal article Open

Crop Advisor: Intelligent Crop Recommendation System

Creators

  • 1. Department of Information Technology, JSPM's BSIOTR Wagholi, Pune (Maharashtra), India.

Contributors

Contact person:

  • 1. Department of Information Technology, JSPM's BSIOTR Wagholi, Pune (Maharashtra), India.
  • 2. Department of Computer Science Engineering, Savitribai Phule Pune University, Pune (Maharashtra), India.

Description

Abstract: Agriculture has long been a cornerstone of the Indian economy, crucial in sustaining livelihoods and contributing to national growth. By 2024, the sector will contribute approximately 18-20% of India's GDP and employ nearly half of the population. It also ensures food security for over 1.4 billion people. However, crop yields per hectare continue to lag international standards, which has been a significant factor contributing to the rising suicide rates among farmers. This paper proposes a machine learning-based Crop Regulating System to assist farmers. The system takes inputs such as historical and current yield data, weather conditions, soil quality and fertiliser usage from farmers and predicts weather impact, rainfall, and disease effect to predict crop yield before sowing. Also, the system takes inputs such as current market data, sowed land, market import/export data, historical retail data, and consumer data for market demand analysis. Machine learning algorithms analyze this data to predict the market demand and the yield for a chosen crop. After that machine learning algorithms like Regression Forest (RF) and Support Vector Machine (SVM) were used to provide Decision support to Farmers. Regression models like Support Vector Machines (SVM) and Random Forests (RF), Multiple Linear Regression (MLR) and classification models like K-Nearest Neighbors (KNN) are utilized for Crop Yield Prediction. Time series models such as AutoRegressive Integrated Moving Average (ARIMA), and Genetics Algorithms (GAs) are used for Market Demand Analysis.

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Additional details

Identifiers

DOI
10.54105/ijae.A1525.05010525
EISSN
2582-9319

Dates

Accepted
2025-05-15
Manuscript received on 06 January 2025 | First Revised Manuscript received on 17 January 2025 | Second Revised Manuscript received on 16 April 2025 | Manuscript Accepted on 15 May 2025 | Manuscript published on 30 May 2025.

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