Crop Status Determination with Dynamic Limit Dates and Weather Data
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Solution Overview
Problem
Remote imagery for crop status determination loses accuracy towards the end of the crop season due to variations in growth stages, making it challenging to assess nitrogen uptake, dry matter, or fresh matter accurately, especially in later stages where timely decisions are crucial.
Innovation Solution
A method that determines a crop status at a target date by using remote data from an earlier date, adjusting the status based on time differences, and incorporating growing degree day values and weather data to compensate for inaccuracies, with iterative updates based on historic weather data and photosynthetic active radiation absorption capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If remote imagery is used for crop status determination, then time and cost are saved compared to in-field inspection, but measurement precision deteriorates towards the end of the crop season due to growth stage variations
Solution Approach 1:
The system performs preliminary determination of the limit date when the crop reaches a specific growth stage (limit growing degree day value) before the target date. Image data is collected from this predetermined time window, ensuring the crop is still in a growth stage where remote sensing indicators remain accurate, thus preventing accuracy deterioration while maintaining time efficiency.
Solution Approach 2:
The system dynamically adjusts the image collection strategy by determining a variable limit date based on actual crop development (growing degree days) rather than using a fixed calendar date. This dynamic approach ensures that image data is always collected when the crop status is still accurately detectable by remote sensing, adapting to different growth rates and environmental conditions.
2Measurement precision
If image data from earlier dates is used to determine crop status at a later target date, then measurement precision is maintained, but loss of time increases due to the time gap between data collection and decision making
Solution Approach 1:
The system performs preliminary determination of the limit date in advance, establishing the optimal window for image collection before the target date. This allows the system to use the latest possible image data that still maintains accuracy, minimizing the time gap while preserving measurement precision.
Solution Approach 2:
The system changes the temporal parameter of image collection by determining a dynamic limit date based on crop development stage (growing degree days) rather than using a fixed offset from the target date. This allows optimization of the time gap based on actual crop growth rate, using the latest possible data that remains accurate.
3Measurement precision
If in-field inspection is performed to maintain measurement precision, then crop status accuracy is improved, but productivity decreases due to time-consuming manual analysis
Solution Approach 1:
The system performs preliminary automated analysis of remote image data to determine crop status indicators before the target date. This preliminary automated assessment maintains high productivity while the limit date mechanism ensures the data remains accurate, avoiding the need for time-consuming manual in-field inspection.
Solution Approach 2:
The system replaces mechanical in-field inspection with automated remote sensing and image processing. By substituting manual visual inspection with automated analysis of satellite or aerial imagery, the system maintains productivity advantages while the limit date approach ensures measurement precision is preserved through optimal timing of data collection.
Data Source
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AI summary
Method and system for determining a crop status at a target date, wherein a limit date for the crop is determined, the limit date being earlier than the target date, further comprising receiving image data from a first date, earlier or equal to the first date, processing the first image data, determining a crop status at the first date based on the processed first image data and adjusting the crop status based on the time difference between the first date and the target date. Further embodiments comprise generating an agricultural measure recommendation based on the adjusted crop status.