Spatial Soil Maps for Data-Driven Agriculture Interventions
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Solution Overview
Problem
Current agricultural practices rely heavily on expert knowledge and biased scientific trials, leading to inefficiencies and resource waste due to high capital requirements, variable soil conditions, and data scarcity, limiting predictive capabilities and scalability in precision agriculture.
Innovation Solution
A data-driven approach integrating high-resolution satellite imagery, topographic data, and spatial modeling to generate accurate soil microbiome and physicochemical maps with reduced sampling requirements, using a mapping predictors catalog and cloud removal algorithms, and implementing AI-based decision support systems for agriculture interventions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional expert knowledge and biased scientific trials are used for agricultural decisions, then decision-making is simplified, but accuracy and reliability of predictions deteriorate due to limited data and high capital requirements
Solution Approach 1:
The patent introduces remote sensing technology as an intermediary between the agricultural site and the decision-making process. Satellites and drones capture spatial data that serves as a mediator to inform soil microbiome mapping and intervention decisions, enabling reliable predictions without requiring complex on-site measurement systems.
Solution Approach 2:
The patent replaces traditional mechanical sampling methods with a data-driven approach using satellite imagery and spatial modeling. Instead of physically sampling every location, the system uses remote sensing data combined with machine learning algorithms to predict soil microbiome characteristics, reducing the need for extensive physical sampling infrastructure.
2Measurement precision
If extensive soil sampling is performed to map microbiome features, then mapping accuracy is improved, but sampling costs and time requirements increase
Solution Approach 1:
The patent divides the agricultural site into multiple spatial zones based on remote sensing data and soil characteristics. By segmenting the site into distinct areas with similar properties, the system can map microbiome features more efficiently, focusing sampling efforts on representative locations rather than conducting comprehensive site-wide sampling.
Solution Approach 2:
The patent creates a digital copy of the soil microbiome distribution through spatial modeling and machine learning algorithms. This virtual representation allows the system to predict microbiome characteristics across the entire site without requiring physical sampling at every location, maintaining mapping accuracy while significantly reducing sampling time and costs.
3Productivity
If precision agriculture technologies are implemented, then agricultural productivity is improved, but capital requirements and sampling costs increase
Solution Approach 1:
The patent makes remote sensing technology universal by using satellite and drone imagery for multiple purposes: mapping soil microbiome features, identifying spatial zones, guiding sampling locations, and informing agricultural intervention decisions. This multi-functionality reduces the need for separate specialized equipment for each function, lowering overall capital requirements.
Solution Approach 2:
The patent changes the parameters of data collection by using readily available remote sensing data (satellite imagery, drone photos) instead of requiring expensive specialized soil analysis equipment. By transforming the approach from physical sampling to data-driven prediction, the system maintains high productivity while reducing capital and sampling costs.
Data Source
AI summary
Systems and methods for generating high-resolution spatial maps of microbiome and physicochemical indices for an agriculture site are provided. The spatial maps are generated from a limited/reduced number of physical samples acquired using a smart sampling tool provided by the systems and methods described. Insights for the agriculture site can be used to guide selection and application of interventions, according to various intervention archetypes, based upon the customized needs of the agriculture site. Performance of the agriculture site can thus be enhanced in an unprecedented, accessible, and sustainable manner.