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A spatial interpolation based on neighbor cluster adaptive model with spatial color block clustering algorithm

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Abstract

Accurate soil nutrient data are crucial for precise fertilizer recommendations in intelligent agriculture. However, the process of soil testing, which includes collecting samples, determining available nutrients and interpreting results, is expensive. To address this challenge, spatial interpolation methods are commonly used to predict soil fertility. Yet, existing techniques like IDW (Inverse Distance Weighting) and OK (Ordinary Kriging) face limitations, making it difficult to achieve highly accurate estimates. Therefore, this paper introduces NCAMS (Neighbor Cluster Adaptive Model with Spatial Color Block), a novel interpolation approach that automatically identifies nearby points crucial for estimating soil nutrient values at a given location. In our approach, we not only consider spatial correlation but also incorporate the soil variables of sampled points. Delaunay triangulation and hash functions further divide data points into distinct clusters, with our model automatically selecting specific clusters. Moreover, our interpolation method integrates IDW and OK without requiring extensive training on real-world data. Extensive experiments on four real-world datasets, conducted through cross-validation, demonstrate the superior performance of our approach compared to eight state-of-the-art methods.

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Availability of data and materials

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request. The data that support the findings of this study are available from [47,48,49] but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of [47,48,49].

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Zhu, L., Chen, F. & Song, X. A spatial interpolation based on neighbor cluster adaptive model with spatial color block clustering algorithm. Appl Intell 55, 53 (2025). https://doi.org/10.1007/s10489-024-05913-0

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