Remote Sensing Crop Yield Loss Prediction
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Solution Overview
Problem
Current methods for predicting nitrogen loss and yield loss in crops due to nitrogen deficiency are inefficient and costly, as they rely on localized and time-consuming measurements, lacking the capability to determine the economic impact of nitrogen supplementation on crop yield using remote sensing technology.
Innovation Solution
A method utilizing remote sensing images to calculate a combination index value and relative index value, estimating yield loss and determining an economically optimal nitrogen fertilization rate by mapping these values onto a spatially referenced map, allowing for precise nitrogen application to minimize yield loss.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional localized measurements are used to assess nitrogen content, then measurement precision is improved, but productivity deteriorates due to time-consuming and labor-intensive repeated measurements across the field
Solution Approach 1:
The patent replaces manual mechanical measurement systems (soil sampling, leaf analysis equipment) with an optical remote sensing system using aerial photographs. The system captures reflected light across the field and processes it through algorithms to estimate nitrogen content, eliminating the need for physical sampling and laboratory analysis at multiple locations.
Solution Approach 2:
The patent creates a spatial map of nitrogen content by processing aerial photograph data into a digital representation of field conditions. This copy of the field's nitrogen status allows comprehensive assessment without physically measuring each location, enabling rapid field-wide evaluation while maintaining measurement accuracy through algorithmic processing.
2Reliability
If excess nitrogen fertilizer is applied at planting to compensate for anticipated losses, then yield loss due to nitrogen deficiency is reduced, but loss of substance increases due to nitrogen leaching and denitrification
Solution Approach 1:
The patent performs preliminary assessment of nitrogen loss risk by analyzing aerial photographs taken during the growing season. By detecting nitrogen deficiency symptoms early, the system enables timely rescue nitrogen applications before yield is significantly impacted, avoiding the need for excessive pre-planting nitrogen applications.
Solution Approach 2:
The patent generates spatially variable nitrogen application recommendations by processing aerial photograph data into field-specific or zone-specific advice. This allows precise nitrogen management tailored to local conditions within the field, applying nitrogen only where and when it is needed rather than uniform excessive application across the entire field.
3Reliability
If rescue nitrogen application is performed at midseason to correct nitrogen deficiency, then yield loss is reduced, but device complexity and operating costs increase due to need for specialized high-clearance equipment
Solution Approach 1:
The patent implements a feedback system where aerial photographs are analyzed to detect nitrogen deficiency, and this information feeds back to generate targeted rescue nitrogen application recommendations. This closed-loop approach ensures nitrogen is applied only where deficiency is detected, optimizing the use of specialized equipment and reducing unnecessary applications.
4Productivity
If aerial photographs are used to assess nitrogen status across the field, then productivity is improved through rapid field-wide evaluation, but measurement precision may deteriorate compared to localized measurements
Solution Approach 1:
The patent creates a digital copy of the field's nitrogen status through processing aerial photograph data into a spatial map. This computational model replicates the information that would be obtained through physical measurement, allowing rapid field-wide assessment while maintaining accuracy through algorithmic processing of the optical data.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables accurate prediction of crop yield loss due to nitrogen deficiency and provides economically optimal nitrogen application rates, reducing costs and improving nitrogen use efficiency by using aerial photographs and spatial coordinates to assess nitrogen stress and yield loss across entire fields.
Implementation Method 1
nitrogen-deficient corn reflects more visible light than nitrogen sufficient corn
Data Source
AI summary
A method for determining the yield loss of a crop using remote sensor data is described. The yield loss is determined using the reflectivity of light by the crop canopy measured from remote sensor data such as an aerial photograph that is digitized and spatially referenced to the field's longitude and latitude. Pixel values from the aerial photograph, expressed relative to pixel values from well fertilized areas of the field, are transformed to yield losses using a linear transformation that was developed using empirical data. A similar method is described to determine recommended nitrogen fertilization rates for the crop fields. The yield loss data is useful for nitrogen fertilization management, as it allows a producer of crops to weigh the expense of fertilization against the loss of revenue due to yield loss.