Date of Award
5-2024
Document Type
Project
Degree Name
Master of Science in Computer Science
Department
School of Computer Science and Engineering
First Reader/Committee Chair
Qingquan Sun
Abstract
This project presents the development of a sophisticated machine-learning model aimed at enhancing agricultural productivity by predicting the optimal fertilizer suited to specific crop requirements. Leveraging a diverse set of features including soil color, pH levels, rainfall, temperature, and crop type, our model offers tailored recommendations to farmers. Three powerful algorithms, Support Vector Machines (SVM), Artificial Neural Networks (ANN), and XG-Boost, were implemented to facilitate the prediction process. Through comprehensive experimentation and evaluation, we assessed the performance of each algorithm in accurately predicting the best fertilizer for maximizing crop yield. The project not only contributes to the advancement of machine learning techniques in agriculture but also holds significant implications for sustainable farming practices and food security.
Recommended Citation
Bommireddy, Durga Rajesh, "Recommendation System using machine learning for fertilizer prediction" (2024). Electronic Theses, Projects, and Dissertations. 1943.
https://scholarworks.lib.csusb.edu/etd/1943