Enhancing irrigation management: Unsupervised machine learning coupled with geophysical and multispectral data for informed decision-making in rice production
Integrating diverse data sources for site-specific management zones (SSMZ) in precision agriculture is a complex task. Soil surveys using apparent electrical conductivity (ECa) have proven effective in capturing field variability. However, relying solely on one sensing data type may not fully ca...
| Autores principales: | , , , , , , |
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| Formato: | article |
| Lenguaje: | Inglés |
| Publicado: |
Elsevier
2025
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| Materias: | |
| Acceso en línea: | https://www-sciencedirect-com.recursos.agrosavia.co/science/article/pii/S2772375524002405?via%3Dihub http://hdl.handle.net/20.500.12324/41165 https://doi.org/10.1016/j.atech.2024.100635 |
| Sumario: | Integrating diverse data sources for site-specific management zones (SSMZ) in precision agriculture is a complex
task. Soil surveys using apparent electrical conductivity (ECa) have proven effective in capturing field variability.
However, relying solely on one sensing data type may not fully capture the intricate relationship between soil
properties and crop physiological processes. This research assessed the effectiveness of applying multivariate
geostatistical analysis and unsupervised machine learning (UML) to geophysical and multispectral data through
ECa, NDWI and NDVI indices, for delineating and validating the SSMZ at different crop cycles in five rice field of
Tolima department-Colombia. Lee´s correlation was conducted between ECa, NDWI and NDVI data. MULTISPATI PCA was used to assess the autocorrelation in the input variables before SSMZ delineation. The correlation results
revealed two field categories based on the direct or indirect relationship between ECa and the multispectral data.
On one hand, ECa values showed an inverse correlation (p < 0.01) with multispectral data (-0.25 to -0.45) for
fields 1a
, 1b and 4a
. On the other hand, there was a direct correlation (p < 0.01) between ECa and multispectral
data (0.31 to 0.51) for fields 2b and 5a
. For all rice fields, MULTISPATI-PCA explained a range between 75.01 %
to 95.64 % of the total variability. UML results highlighted two, three and four SSMZ, with two SSMZ being the
optimal management zones number. The validation suggests that the θ data showed a significant statistical
difference for two SSMZs (p < 0.05), regardless of the water content ranges. This study emphasized the
importance of integrating geophysical and multispectral datasets in precision agriculture for evaluating field
variability and crop conditions. |
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