Crop Recommendation System using Machine Learning and Environment Parameters

TITLE	Crop Recommendation System using Machine Learning and Environment Parameters
ABSTRACT	Agriculture is a vital sector of India’s economy, yet traditional practices often disregard soil quality and environmental conditions, resulting in low productivity. To address this, a Crop Recommendation System (CRS) is developed using Internet of Things (IoT) data and machine learning. The system processes soil nutrients, pH, temperature, rainfall, and humidity to recommend suitable crops. Feature selection is achieved through Improved Distribution-based Chicken Swarm Optimization (IDCSO), while a Weight-based Long Short-Term Memory (WLSTM) model enhances prediction accuracy. Additional models, including Kernel Ridge, Lasso, Elastic Net, Random Forest, and Naïve Bayes, are applied for yield forecasting, with Random Forest and Naïve Bayes showing the best performance. The CRS also offers a simple interface for farmers, enabling personalized recommendations. This approach promotes precision farming, boosts productivity, and encourages sustainable agricultural practices.
AUTHOR	Akhilkumar s, Arjun, Abhishek N Department of Masters of Computer Applications, CMR Institute of Technology, Bengaluru, India
PUBLICATION DATE	2025-09-01
VOLUME	12
DOI	DOI:10.15680/IJARETY.2025.1204058
PDF	58_Crop Recommendation System using Machine Learning and Environment Parameters.pdf
KEYWORDS

Article