Data-driven clustering of smart farming to optimize agricultural practices through machine learning - Thongnim, Pattharaporn; Srinil, Phaitoon; Pukseng, Thanaphon (Bulletin of Electrical Engineering and Informatics, 2025)
This study investigates the optimization of durian farming practices in Eastern Thailand using data-driven clustering techniques. The research aims to identify distinct agricultural patterns and improve resource allocation in durian production. K-means clustering is applied to durian production area and yield data from 2012 to 2023. Cluster quality is assessed using the Davies-Bouldin index (DBI), Dunn index, and Silhouette score. The methodology included comparing clustering results before and after log transformation of the data. Three main clusters are identified which are large-scale high-yield producers, small-scale lower-yield areas, and medium-scale producers with moderate yields. Notably, log transformation did not consistently improve clustering performance with original data often producing better-defined clusters. This finding highlights the importance of carefully considering data pre processing methods. Furthermore, the data-driven clustering offers valuable insights for precision agriculture by identifying regions with higher productivity allowing for targeted interventions and better resource allocation. The results can guide farmers in optimizing durian cultivation strategies, potentially leading to increased yields and more sustainable farming practices in Eastern Thailand's durian industry.