Machine Learning-Based Crop Prediction: Boosting Accuracy and Supporting Sustainable Farming
Harika Kairamkonda, Dr.Dasaradha Ramaiah
Agriculture, Neural Network, Neural Network, Crop prediction, Machine learning, Random Forest.
Agriculture employs more than half of India's population, making it a critical component of the country's economy. The unpredictability of weather and environmental circumstances, on the other hand, poses a substantial danger to agricultural productivity. Machine learning (ML) can be used as a decision-making tool for crop prediction and deciding which crops to produce to address this. Despite its utility, neural networks have drawbacks such as lower prediction efficiency and higher relative error. Supervised learning techniques also have difficulty capturing nonlinear correlations between input and output variables. Numerous studies have been conducted in order to develop reliable and effective crop categorization models, such as crop yield estimation based on meteorological conditions, disease diagnosis, and crop growth phases. This paper explores the use of the Random Forest algorithm, a type of ML technique, for crop prediction and provides a detailed analysis of its accuracy.This system takes inputs such as environmental parameters and soil characteristics before recommending the most suitable crop to grow.
Article Details
Unique Paper ID: 158464

Publication Volume & Issue: Volume 9, Issue 9

Page(s): 540 - 544
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