Evaluation Index Selection for Crop Growth Analysis
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Solution Overview
Problem
Existing evaluation indices, such as NDVI, are not suitable for all states of agricultural crops, leading to reduced accuracy in analyzing changes in agricultural land due to decreased variations in red and near-infrared light components as crops grow, making it difficult to accurately assess crop growth and land conditions.
Innovation Solution
An information processing apparatus and method that calculates multiple evaluation indices based on imaging data, calculates evaluation values from these indices, and selects the most appropriate index for analysis, allowing for the use of suitable indices for different crop growth stages and conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single evaluation index (e.g., NDVI) is used for analysis, then the analysis process is simple, but the accuracy of analysis is lowered when the subject state changes
Solution Approach 1:
The system calculates multiple types of evaluation indices (NDVI, EVI, SAVI, etc.) that can universally apply to different subject states and growth stages. Each index serves multiple functions by capturing different aspects of vegetation characteristics under various conditions, allowing the system to maintain high analysis accuracy across diverse scenarios without requiring separate specialized methods for each condition.
Solution Approach 2:
The system changes the parameters by calculating multiple evaluation indices with different formulation parameters (e.g., NDVI uses red and near-infrared bands, while EVI incorporates blue band and has different weighting coefficients). By varying the spectral bands and calculation parameters across multiple indices, the system adapts to different subject states, resolving the contradiction between maintaining simple analysis processes and achieving high accuracy across changing conditions.
2Measurement precision
If multiple evaluation indices are calculated, then the accuracy of analysis is improved for different subject states, but the calculation complexity increases
Solution Approach 1:
The system performs preliminary calculation of multiple evaluation indices simultaneously from the acquired spectral data. By pre-calculating all necessary indices before analysis, the system avoids repeated calculations during the analysis phase, thereby improving accuracy for different subject states while controlling the overall calculation complexity through efficient batch processing.
Solution Approach 2:
The system automatically selects the most appropriate evaluation index based on the calculated values and subject state characteristics, without requiring manual intervention. This self-service mechanism resolves the contradiction by enabling accurate analysis through multiple indices while keeping the operational complexity low, as the system autonomously determines which index to use based on the calculated data.
3Adaptability or versatility
If multiple evaluation indices are calculated and automatically selected, then the adaptability to different crop states is improved, but the processing time increases
Solution Approach 1:
The system replaces manual selection of evaluation indices with an automated computational mechanism that selects indices based on calculated values and predefined criteria. This substitution of automatic algorithmic selection for manual or trial-and-error selection improves adaptability to different crop states while reducing the time loss associated with manual intervention and iterative testing of different indices.
Data Source
AI summary
Selection of an evaluation index based upon statistical values of respective evaluation indices is disclosed. In one example, an information processing apparatus comprises an evaluation index unit that determines a plurality of evaluation indices on a basis of imaging data obtained by imaging a subject, wherein the evaluation indices are respectively based upon different wavelength component combinations of the imaging data. An evaluation value unit determines an evaluation value based on a statistical value of each of the evaluation indices, for each of the evaluation indices. A selection unit then determines a selected evaluation index from the evaluation indices on a basis of the evaluation values.