Date of Award

8-2024

Document Type

Project

Degree Name

Master of Science in Computer Science

Department

School of Computer Science and Engineering

First Reader/Committee Chair

Dr BILAL KHAN

Abstract

Farming plays a role in ensuring survival, especially with the growing need for increased agricultural output. It is vital for farmers to efficiently choose the crops to cultivate. By using crop recommendation systems farmers can make decisions on what crops to plant leading to yields and improved resource management. The success of crop production depends on maintaining the balance of soil nutrients and favorable weather conditions. In this research project, we created a crop recommendation system utilizing learning methods to predict the appropriate crops based on essential soil nutrients and weather patterns. We worked with a dataset sourced from Kaggle, which included 2,200 entries featuring elements like Nitrogen, Phosphorus, Potassium, Temperature, Humidity, pH levels, and Rainfall data in CSV format. We compared two techniques: bagging and boosting. For bagging, we utilized base estimators such as Decision Tree, Random Forest, and Support Vector Classifier (SVC). Our analysis revealed that the Random Forest classifier attained an accuracy rate of 99% after optimizing parameters, emerging as the effective model for our dataset. Additionally, the boosting approach using the Gradient Boosting classifier also performed well, with an accuracy rate of 98%. These findings underscore how ensemble methods can significantly improve accuracy in crop recommendation systems. The bagging model based on Random Forest showed effectiveness in this scenario, providing insights for making decisions in agriculture.

Download

Share

COinS