Master Thesis Topic:
"Agriculture Crop Production Optimization System using Machine Learning."
Problem Statement:
The agricultural sector faces significant challenges including the scarcity of arable land, environmental changes, unpredictable weather patterns, pests, and diseases, leading to food insecurity and increased global food demand. Traditional agricultural methods relying on experience and historical data are time-consuming and labor-intensive, resulting in suboptimal outcomes. There is a need to develop an agricultural production optimization system that utilizes machine learning techniques to improve productivity.
Aim:
The goal of this research is to establish an agricultural business production optimization system using machine learning techniques to predict crop yields and optimize resource utilization.
Objective:
The objectives of this study aim to contribute to sustainable agriculture by optimizing agricultural production processes, minimizing the environmental impact of farming procedures, and guaranteeing food security as well as to create a user-friendly system that analyzes agricultural data, applies artificial intelligence algorithms such as decision tree regressors and XGBoost regressors, and makes accurate predictions to optimize resource utilization and enhance agricultural production processes.
Skills:
Python (Programming Language), Anaconda Jupyter Notebook, MySQL, Microsoft Power BI, Tableau, MS Power Point, MS Excel, MS Word.