Fertilizer Determination Using Vegetation Index and Correction Factors
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Solution Overview
Problem
Current methods for site-specific fertilizer application in agriculture are inefficient due to uniform dosage practices that fail to account for soil heterogeneity and productivity variations, leading to over- or under-fertilization in different sub-areas, and require cumbersome calibration and frequent updates for variety-specific corrections.
Innovation Solution
A method that calculates fertilizer requirements based on vegetation index measurements, developmental stage, and yield potential, using characteristic curves to determine optimal nutrient uptake and applying a correction factor for site-specific parameters like fertilizer duration and soil mineralization, enabling precise, sub-plot specific fertilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If uniform fertilizer dosage is applied over the entire field, then the fertilization process is simple and quick, but over-fertilization or under-fertilization occurs in individual sub-areas leading to economic and ecological problems
Solution Approach 1:
The field is divided into multiple sub-areas based on soil heterogeneity and productivity variations. Each sub-area receives a customized fertilizer dosage according to its specific needs, determined by vegetation index measurements and yield potential assessments. This segmentation enables precise fertilizer application that matches actual crop requirements in each zone.
Solution Approach 2:
Different fertilizer dosages are applied to different sub-areas according to their specific productivity levels and nutrient requirements. High-yield sub-areas receive higher fertilizer amounts while low-yield sub-areas receive reduced dosages. This local quality approach ensures that each area receives the appropriate amount of fertilizer rather than a uniform application across the entire field.
2Measurement precision
If vegetation index detection sensors are calibrated using N testers, then nutrient status measurement is possible, but the calibration process is extremely cumbersome and requires significant experience
Solution Approach 1:
Instead of using complex N testers that measure light transmission through leaves, the system uses vegetation index detection sensors that measure light reflection from the canopy. These sensors are calibrated using simplified characteristic curves and correction factors that can be applied without requiring the operator to have specialized experience with leaf transmission measurements.
Solution Approach 2:
The calibration approach changes from direct N content measurement using N testers to indirect measurement through vegetation indices. The system uses characteristic curves that relate vegetation index values to nutrient status, along with variety-specific and yield-potential-specific correction factors. This parameter transformation simplifies the calibration process while maintaining measurement accuracy.
3Measurement precision
If variety correction tables are updated annually to account for different plant varieties, then measurement accuracy across varieties is improved, but the handling and maintenance of sensor data becomes more complex
Solution Approach 1:
The correction factors are made dynamic and adaptive rather than static annual updates. The system automatically adjusts correction factors based on real-time measurements of vegetation indices, yield potential assessments, and observed crop responses. This dynamic approach allows the system to adapt to new varieties without requiring manual updates of correction tables, reducing maintenance complexity while maintaining accuracy.
4Loss of substance
If fertilizer application is optimized for current yield potential only, then current year fertilizer consumption is optimized, but weather-dependent nutrient release from soil cannot be taken into account
Solution Approach 1:
The system implements continuous monitoring of vegetation indices throughout the growing season and uses this feedback to adjust fertilizer application rates. Real-time measurements of crop nutrient status and growth responses provide feedback that allows the system to adapt fertilizer dosages to actual crop needs and environmental conditions, including weather-dependent nutrient release from the soil.
Solution Approach 2:
The system performs preliminary assessments of yield potential and soil characteristics before fertilizer application, and uses this information to pre-calculate optimal dosages. However, it maintains the flexibility to adjust these preliminary plans based on actual field conditions and weather patterns that develop during the growing season.
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 optimizes fertilizer use by avoiding over- or under-fertilization, exploiting the yield potential of each sub-area, and simplifies the calibration process, ensuring accurate nutrient delivery based on current and expected nutrient status, productivity, and environmental conditions.
Implementation Method 1
The REIP index utilizes the light absorption and reflection behavior of plants. Accordingly, these plants have the general property of absorbing light of a certain wavelength for the most part, while reflecting the longer-wave light for the most part.
Implementation Method 2
The REIP index utilizes the light absorption and reflection behavior of plants... this main inflection point (REIP) can be used to determine biomass growth and nitrogen content.
Data Source
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AI summary
A method and a device for discharging fertilizer for agricultural cultivated plants are disclosed. According to the invention, the required quantity of fertilizer is determined depending on the current nutrient uptake (nutrient status), optimum nutrient uptake (nutrient requirement) by the fertilizing deadline, and optimum nutrient uptake (nutrient requirement) by the next fertilizing deadline, wherein other influencing factors are taken into consideration via a correction factor DIMA.