Remote Sensing NDVI Correction for Crop Density Variations
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Solution Overview
Problem
Existing remote sensing technologies for vegetation state evaluation in farm fields face inaccuracies due to the inclusion of soil areas in captured images, leading to incorrect NDVI values, especially in areas with a high proportion of soil and low crop density.
Innovation Solution
An information processing device with an evaluation information correction unit that utilizes vegetation cover rates derived from crop counts to generate correction information, allowing for the adjustment of NDVI values based on specific crop types, previous data, and environmental conditions to improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If remote sensing is performed using captured images of farm fields, then wide-area vegetation evaluation is achieved, but measurement precision deteriorates due to soil area inclusion
Solution Approach 1:
The farm field area is segmented into vegetation areas and soil areas based on vegetation cover rates. By dividing the evaluation area into distinct segments and treating them differently, the system can evaluate vegetation in crop-covered areas while excluding or separately handling soil areas, thus resolving the contradiction between wide-area evaluation and measurement precision.
Solution Approach 2:
Different evaluation methods are applied to different local areas based on their vegetation cover rates. Areas with high vegetation cover rates use standard NDVI calculation, while areas with low vegetation cover rates undergo correction or exclusion. This local differentiation allows wide-area evaluation while maintaining precision in each local zone.
2Ease of operation
If NDVI calculation is performed on captured images including soil areas, then evaluation process is simplified, but measurement precision deteriorates in areas with low crop density
Solution Approach 1:
Before performing NDVI calculation, the system preliminarily determines vegetation cover rates for different areas and identifies which areas require correction. This preliminary classification allows the system to maintain simple processing for most areas while applying precision correction only where necessary, balancing simplicity and accuracy.
Solution Approach 2:
The system changes the NDVI calculation parameter by applying correction coefficients to areas with low vegetation cover rates. Instead of completely redesigning the evaluation process, it modifies the NDVI values in specific areas based on vegetation cover rates, maintaining overall process simplicity while improving local precision.
3Measurement precision
If correction information based on crop counts is applied, then measurement precision improves, but device complexity increases
Solution Approach 1:
The system uses the captured image data itself to generate correction information by counting crops and calculating vegetation cover rates. Rather than requiring external complex measurement devices, the system extracts all necessary information from the existing image data, improving precision without significantly increasing device complexity.
Solution Approach 2:
The image processing system performs multiple functions: it captures images, counts crops, calculates vegetation cover rates, and applies corrections all in one unified process. This multi-functionality allows the system to improve measurement precision without adding separate complex devices, as the same processing unit handles all tasks.
Data Source
AI summary
An information processing device includes an evaluation information correction unit that corrects evaluation information associated with a target area according to correction information based on the number of crops of the target area.