Site-Specific Fertilizer Recommendation Using Time-Adjusted Crop Imagery
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Solution Overview
Problem
Existing methods for determining site-specific fertilizer recommendations face challenges due to limitations in revisit time and cloud occurrence, leading to the need for computationally intensive cloud-compensating methods and the use of outdated or suboptimal image data, which affect the accuracy of nutrient status assessment in agricultural fields.
Innovation Solution
A method that adjusts crop nutrient status using remote data with time stamps, selects suitable image data based on vegetation index changes, and accounts for time differences and weather data to generate a variable fertilizer recommendation, reducing the need for intensive cloud compensation algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If cloud-compensating methods are used to handle remote image data, then the availability of image data is improved, but the computational effort and processing complexity increase significantly
Solution Approach 1:
The patent extracts only the essential information needed for fertilizer recommendation by using simplified vegetation indices (NDVI, GNDVI, EVI) rather than performing full cloud compensation processing. This selective extraction of key data elements avoids the computational burden of complete cloud-compensating methods while still providing sufficient information for accurate nutrient status assessment.
Solution Approach 2:
The system uses readily available public satellite imagery from Sentinel-2 and Landsat satellites without requiring expensive specialized imaging systems. By leveraging free, frequently updated open-source data, the patent avoids the need for costly cloud-compensation infrastructure while maintaining reliable data availability for fertilizer recommendations.
2Ease of operation
If remote imagery is used to determine crop nitrogen levels, then the need for in-field inspection is eliminated, but the accuracy is reduced due to cloud occurrence and revisit time limitations
Solution Approach 1:
The patent combines multiple vegetation indices (NDVI, GNDVI, EVI) derived from satellite imagery with crop growth models and weather data to compensate for the limitations of remote sensing. This integration of multiple data sources and processing methods enhances the accuracy of crop nitrogen level assessment while maintaining the ease of remote operation.
Solution Approach 2:
The system performs preliminary processing of satellite imagery by generating vegetation indices and filtering for cloud-free conditions before using the data for fertilizer recommendations. This pre-processing step ensures that only high-quality, cloud-free image data is used, thereby maintaining measurement precision while preserving the remote assessment capability.
3Manufacturing precision
If multiple cloud detection and compensation methods are implemented, then the quality of image data is improved, but the processing time and computational resources increase
Solution Approach 1:
The patent applies a selective approach to cloud detection and compensation by using simplified vegetation indices and filtering methods rather than implementing multiple comprehensive cloud compensation algorithms. This partial action approach achieves sufficient image data quality for fertilizer recommendations without the excessive processing time and computational resources required by more thorough methods.
4Reliability
If outdated remote image data is used due to revisit time limitations, then data availability is maintained, but the accuracy of current crop nutrient status assessment is reduced
Solution Approach 1:
The system dynamically adjusts the fertilizer recommendation based on the time elapsed since image acquisition by integrating crop growth models that account for nitrogen uptake rates and environmental conditions. This dynamic adjustment compensates for the temporal gap between outdated image data and current crop status, maintaining measurement precision while preserving data availability.
Solution Approach 2:
The patent incorporates feedback from crop growth models and weather data to adjust and update the fertilizer recommendations based on the time difference between image acquisition and current conditions. This feedback mechanism allows the system to maintain accurate nutrient status assessment even when using outdated remote image data by continuously refining the recommendations based on current environmental factors.
Data Source
AI summary
A computer-implemented method for providing a site-specific variable fertilizer recommendation for a crop at a given point in time, including determining at least one agricultural field including at least one crop, scheduling a fertilization application for the agricultural field, and determining a crop nutrient status of the agricultural field. Determining the crop nutrient status includes receiving remote data, including image data of the agricultural field and a time stamp indicative of when the image data was taken, generating at least one vegetation index indicative of a crop nutrient status based on the image data, and determining a crop nutrient status based on the vegetation index. The method further includes adjusting the crop nutrient status based on the time difference between the time stamp of the image data and the scheduled fertilizer application, and determining a variable fertilizer recommendation for the agricultural field based on the adjusted crop nutrient status.