Application of Bayesian Networks to assess soil quality for farmers

Author

Nasuku, Keith S.

Title

Application of Bayesian Networks to assess soil quality for farmers

Abstract

Modern agriculture faces the dual challenge of maximizing crop yields to feed a growing population while minimizing environmental impact. Traditionally, assessing soil quality and managing nutrients for optimal crop growth relies on time-consuming laboratory analyses and may not capture the dynamic nature of soil conditions. This research investigates the application of Bayesian networks, a specific type of probabilistic graphical model, to tackle complex problems and overcome existing challenges. The research investigates the efficacy of Bayesian networks in predicting soil quality and nutrient levels for farmers. It examines how these models can be built using diverse data sources, including historical soil image data. The research evaluates the impact of Bayesian networks on decision-making processes, considering factors like technological preparedness and long-term effects on soil health. The study also explores the limitations of this approach, including data availability and generalizability across different agricultural settings. By integrating Bayesian networks into agricultural practices, farmers can gain deeper insights into their soil conditions, leading to more sustainable and productive farming methods. This research contributes to the advancement of precision agriculture and highlights the importance of data-driven decision-making for a more sustainable and environmentally responsible agricultural future.

Date

June 2024

Publisher

BUSE

Keywords

Modern agriculture,

Assessing soil quality and managing nutrients,

Bayesian networks,

Supervisor

Mr. W. Kanyongo,