Spatial Classification of Fertilizer Requirements Using Fuzzy C-Means on Shallot Agricultural Land

Spatial variability in soil fertility constrains productivity in intensive shallot farming, yet fertilizer is frequently applied uniformly across fields. This practice results in nutrient inefficiencies, increased costs, and heightened environmental risks. This study introduces a fertilizer requirement mapping framework utilizing Fuzzy C-Means (FCM) clustering, a machine learning technique for data grouping, applied to in-situ measurements of soil Nitrogen (N), Phosphorus (P), and Potassium (K). The framework was evaluated in a 500 × 500 m shallot field in Srikayangan, Kulon Progo, Indonesia, subdivided into 10 × 10 m management blocks suitable for smallholder operations. Soil NPK levels were measured using IoT sensor nodes and georeferenced with GNSS, while high-resolution RGB imagery from a UAV provided spatial context. Normalized NPK data were clustered with FCM to delineate fertility zones exhibiting nutrient differences. To operationalize clustering results, a nutrient-priority decision logic identified the most limiting nutrient (N, P, or K) for each block. Fertilizer recommendation points were visualized on a UAV-derived orthomosaic map to facilitate interpretation and field application. The results indicate that this approach effectively captures gradual fertility transitions and produces actionable fertilizer zones for site-specific nutrient management (SSNM) in smallholder systems. The study demonstrates the practical integration of fuzzy clustering, IoT-based soil sensing, and UAV mapping to inform precision agriculture decisions.

@article{Hujja2025,
  title     = {Spatial Classification of Fertilizer Requirements Using Fuzzy C-Means on Shallot Agricultural Land},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {12},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Roghib Muhammad Hujja and Ahmad Ashari and Danang Lelono and Agus Prasekti},
  doi       = {10.14569/IJACSA.2025.0161299},
  url       = {https://doi.org/10.14569/IJACSA.2025.0161299}
}