A system for soil sampling and fertility assessment

Abstract

A soil sampling and fertility assessment system consisting of a field division module for subdividing an agricultural field, a GPS-based sampling module for capturing georeferenced soil samples, a laboratory interface for processing soil analysis results, and a geodata processing module for geostatistical modeling to delineate homogeneous fertility zones for nutrient management.

Description

Application area of ​​the invention

[0001] The invention relates to precision agriculture systems that integrate GPS-based field sampling, laboratory analyses and GIS-based geostatistics to delineate homogeneous soil fertility zones for optimized nutrient management. Background of the invention

[0002] Soil fertility exhibits significant spatial heterogeneity at the field level. Conventional sampling methods often average this variability, reducing the accuracy of nutrient recommendations. Precision agriculture utilizes GPS, GIS, and geostatistical methods such as variograms and kriging to model spatial dependencies and interpolate soil properties across fields. This enables the establishment of management zones and the variable application of inputs. Previous work has shown that selecting appropriate semivariogram models, validating them through cross-validation, and combining multivariate geostatistics with clustering can reliably delineate homogeneous zones.Nevertheless, there is still a need for an integrated, end-to-end system that standardizes field subdivision, GPS-based sampling, recording of laboratory results, geodata processing and zone delineation based on spatial variance in order to optimize sample size and decision accuracy. Summary of the invention

[0003] The invention relates to a system with modules for recording and subdividing field boundaries, a GPS-supported sampling workflow, laboratory data acquisition, GIS data processing, and spatial variance analysis for delineating homogeneous fertility zones. A control unit coordinates the data flow from sampling planning to zone map output and applies geostatistical models to quantify spatial dependencies and generate interpolated fertility areas for nutrient planning.

[0004] In various implementations, the system divides the field into preliminary grids or layers, controls GPS-guided sampling at predefined points, processes laboratory results for key soil parameters, fits and validates semivariogram models, generates kriging surfaces with uncertainty estimates, and applies clustering or rule-based segmentation to identify homogeneous zones. The resulting zones enable optimized sampling sizes, precise nutrient dosing, and sustainable resource management. Detailed description

[0005] The system includes a field partitioning module that imports or digitizes field boundaries and generates an initial sampling design, such as a regular grid or a layered layout based on the terrain, previous yield maps, or remote sensing data available in GIS. The module supports variable sampling densities in suspected sub-areas with high variance to improve model fit and reduce uncertainty in fertility estimates.

[0006] A GPS sampling module enables on-site navigation to each sampling point using GNSS / DGPS coordinates, assigns a unique ID to each soil core or sample, and captures metadata such as depth, timestamp, and environmental information. This module increases positioning accuracy and ensures reproducible sampling for future campaigns and validations.

[0007] An interface for laboratory analyses processes certified test results for soil pH, organic matter, macro- and micronutrients, cation exchange capacity, electrical conductivity, and soil type. Data quality checks validate ranges, identify outliers, and harmonize units to a common scheme suitable for geostatistical evaluations and seasonal comparisons.

[0008] The geodata processing module creates experimental semivariograms for selected properties, evaluates nugget, sill, and range parameters, and fits candidate models (e.g., spherical, exponential, Gaussian) using weighted least squares. Cross-validation metrics such as mean error and root mean square error are used to select models that minimize bias and improve prediction accuracy.

[0009] Kriging or related model-based interpolation methods generate continuous fertility areas with associated uncertainty levels that account for irregularly distributed samples, such as those typically collected in practical field campaigns. The system stores variogram parameters and model diagnostics to support reproducibility and iterative improvement in subsequent seasons.

[0010] A module for determining spatial variance quantifies the heterogeneity within fields and derives homogeneous management zones using multivariate geostatistics and clustering over stacked raster soil property data. This approach groups areas with similar fertility signatures, thus enabling agronomically meaningful zones that are stable over time and can be used for variable fertilizer application.

[0011] The controller provides decision support for optimizing sample size and placement by simulating the expected reduction in prediction error as a function of additional samples, thus enabling cost-effective resampling in areas of high uncertainty. It can recommend targeted resampling in regions where the semivariogram range and nugget behavior indicate small-scale variability.

[0012] The interface to the dosing instructions generates GIS-compatible zone maps and supports export to control units for variable application rates. This aligns with the workflows of precision agriculture, which reduces costs and environmental impact through demand-based nutrient distribution. Version control stores the history of zones and parameters to ensure temporal stability and allow for adjustments to sampling strategies.

[0013] Security and data management features include role-based access control, audit logs for changes to laboratory data, and standardized metadata for sampling, ensuring traceability from field to recommendation. The modular design allows for the integration of remote sensing indices and yield maps to improve zone delineation in data-rich environments.

[0014] In operation, the user loads a field boundary, creates a sampling design, takes GPS-referenced samples, imports laboratory results, performs a semivariogram fit with cross-validation, generates kriging fertility maps with uncertainty factors, and delineates homogeneous zones for nutrient planning. The system emphasizes statistical accuracy and practical applicability to improve the accuracy of fertility assessments and support decisions for sustainable agriculture.

Claims

[1] A soil sampling and fertility assessment system consisting of a field division module for subdividing an agricultural field, a GPS-based sampling module for capturing georeferenced soil samples, a laboratory interface for processing soil analysis results and a geodata processing module for geostatistical modeling to delineate homogeneous fertility zones for nutrient management. [2] System according to claim 1, wherein the geodata processing module creates experimental semivariograms and adapts selected variogram models using cross-validation metrics and generates interpolated fertility areas using kriging with associated uncertainty levels. [3] System according to claim 1, wherein the system optimizes the sample size and placement by quantifying the spatial variance and recommending a condensation of the sample in sub-areas of high uncertainty or high variability identified by variogram parameters. [4] System according to claim 1, wherein the system applies multivariate geostatistics and clustering to multiple raster datasets of soil properties to derive homogeneous management zones suitable for variable nutrient application and exports GIS-enabled application notes.