An optimization framework for intelligent irrigation system installation in fragmented paddy fields

• This study proposed an optimization framework for IIS unit installation. • The framework integrates NN-distance–based DBSCAN with 2-opt route optimization. • The approach identifies optimal field combinations for fragmented paddy fields in Japan. • This study improves the robustness of DBSCAN for handling non-uniform spatial data. • This framework can be applied in smart agricultural systems for fragmented fields. The fragmented structure of Japanese paddy fields increases labor requirements for patrol and irrigation management. While Intelligent Irrigation System (IIS) units can effectively reduce labor input, their benefits are influenced by the location of the installations. Consequently, determining optimal field combinations under varying installation conditions (varying numbers of IIS units and farmer datasets) has become a critical issue. This study proposes and validated a two-stage optimization framework for IIS unit installation that employs patrol-route distance reduction as the evaluation metric. In the first stage, Density-based Spatial Clustering of Applications with Noise (DBSCAN) with the Normalized Nearest-distance (NN-distance) method was applied to mitigate search space explosion under non-uniform densities. In the second stage, the 2-opt algorithm was used to optimize patrol routes and quantify labor reduction. Validation results showed that the framework compressed the candidate solution space and alleviated the computational complexity associated with the Non-deterministic Polynomial-time hard (NP-hard) nature of the problem. Furthermore, the NN-distance method-maintained solution quality and outperformed the conventional k-distance approach by mitigating over-clustering and over-segmentation under non-uniform spatial distributions. Case analyses revealed that the benefits of IIS unit installation depend not only on the number of installed units but also strongly on the spatial distribution of fields. Overall, the proposed framework enhances the applicability of DBSCAN to non-uniform spatial data, provides guidance for differentiated installation strategies, and offers a reproducible methodological framework for deploying smart agricultural technologies in fragmented agricultural systems.