Chlorophyll Estimation via Leaf Light Transmission and Reflection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for estimating chlorophyll content in plants are unreliable due to their reliance on color measurements, which are subjective and vary by leaf species, leading to over-application of nitrogen fertilizer, resulting in unnecessary costs and ecosystem risks.
Innovation Solution
The method involves capturing and processing digital images of plant leaves to estimate chlorophyll levels by using bidirectional reflectance parameters, iteratively refining these parameters to minimize variance across pixels, and adjusting for the leaf's index of refraction, allowing for the creation of nitrogen sufficiency maps and precise fertilizer application recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If color measurements are used to estimate chlorophyll content, then the measurement process is simple, but the measurement precision is poor due to subjectivity and species variation
Solution Approach 1:
The patent replaces subjective visual color assessment with objective digital image processing and spectral analysis. By capturing images through the leaf and analyzing light transmission spectra, the system substitutes human perception with quantifiable optical measurements, eliminating subjectivity while maintaining operational simplicity.
Solution Approach 2:
The patent transforms the measurement from visible color parameters to spectral transmission parameters across multiple wavelengths. By analyzing how light transmits through the leaf at different wavelengths, the system extracts chlorophyll content information more accurately, converting a subjective color metric into objective spectral data.
2Ease of manufacture
If published leaf model parameters are used for chlorophyll estimation, then the process is straightforward, but the reliability is poor due to inter-leaf variations not being accounted for
Solution Approach 1:
The patent measures spectral transmission locally for each pixel or small region of the leaf rather than applying a uniform published parameter to the entire leaf. This allows the model to adapt to local variations in leaf structure, thickness, and composition, improving reliability while keeping the process straightforward through automated pixel-by-pixel analysis.
Solution Approach 2:
The patent makes the leaf model parameters dynamic by deriving them from actual measurements on each leaf rather than using static published values. The system adapts the model parameters to match the specific leaf being measured, accounting for inter-leaf variations and improving estimation reliability.
3Productivity
If nitrogen fertilizer is over-applied to ensure sufficient supply, then crop yield is maximized, but unnecessary costs and environmental harm increase
Solution Approach 1:
The patent implements a feedback mechanism by measuring actual chlorophyll content in leaves and using this information to determine precise nitrogen application needs. The system provides real-time or near-real-time feedback on plant nitrogen status, allowing farmers to apply fertilizer only where and when needed, optimizing yield while reducing waste and environmental impact.
Solution Approach 2:
The patent changes the approach from fixed-rate nitrogen application to variable-rate application based on measured chlorophyll levels. By translating optical measurements into actionable nitrogen management parameters, the system enables precise fertilizer dosing that matches actual plant needs, eliminating over-application while ensuring sufficient supply for optimal yield.
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 estimation of chlorophyll content, reducing nitrogen over-application, minimizing environmental impact, and optimizing crop yields by providing precise fertilizer recommendations based on actual plant needs.
Implementation Method 1
capturing a first image comprising light transmitted through a leaf of a plant
Implementation Method 2
capturing a second image comprising light reflected from the leaf of the plant
Data Source
AI summary
A method for determining chlorophyll content of a plant comprises capturing a first image comprising light transmitted through a leaf of a plant; capturing a second image comprising light reflected from the leaf of the plant; estimating, from a plurality of pixels in the first image, a transmissive chlorophyll concentration value of the leaf; estimating a reflectance chlorophyll concentration value for the leaf from a plurality of pixels in the second image using bidirectional reflectance parameters for which a variance of the reflectance chlorophyll concentration value across the plurality of pixels in the second image is reduced; and determining an estimated chlorophyll concentration value for the plant based at least on the transmissive chlorophyll concentration value and the reflectance chlorophyll concentration value.