Leveraging Data Mining to Innovate Agricultural Applications

Abstract

The WEKA (Waikato Environment for Knowledge Analysis) provides a comprehensive suite of tools and functionalities for applying data mining techniques to large and complex datasets. Designed to support both academic research and industrial applications, WEKA offers a user-friendly graphical interface along with a wide range of machine learning algorithms for tasks such as classification, regression, clustering, association rule mining, and visualization. This paper presents a detailed process model for conducting data analysis using WEKA and highlights the platform’s capabilities in supporting each phase of this model—from data preprocessing to model evaluation and deployment. Furthermore, the paper explores how the knowledge models generated by WEKA's data mining algorithms can be seamlessly integrated into larger software systems, thereby enabling the development of intelligent applications. The effectiveness of this approach is demonstrated through a real-world case study in the agricultural domain, specifically focusing on mushroom grading. In this case study, WEKA was utilized to analyze and interpret agricultural data, leading to the creation of a predictive model that can assist in automating the quality assessment of mushrooms based on key attributes. This demonstrates the practicality and scalability of WEKA in supporting domain-specific data-driven decision-making and application development.