Crop Phenotype Data Fusion With Adaptive Sensor Weighting
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Solution Overview
Problem
Traditional crop phenotype data fusion methods suffer from low fusion efficiency and poor fusion effects when integrating data from different dimensions and/or periods, failing to leverage the potential relevance and complementarity of multi-dimensional data and lacking adaptability to dynamic changes in the crop growth environment.
Innovation Solution
A method and apparatus that dynamically adjusts weights based on intrinsic and extrinsic parameter data, using a weight adjustment model trained on sample data sets to enhance the relevance and timeliness of data fusion, incorporating a self-learning mechanism for continuous optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional crop phenotype data fusion methods are used to integrate data from different dimensions and periods, then data fusion can be performed, but the fusion efficiency is low and the fusion effect is poor
Solution Approach 1:
The patent applies dynamics by transitioning from static, fixed-weight data fusion to dynamic, adaptive weight adjustment. The system continuously learns from new data and adjusts fusion weights in real-time based on current environmental conditions and data quality, enabling the fusion process to adapt to changing agricultural scenarios and improve both efficiency and effectiveness.
Solution Approach 2:
The patent changes the parameter of fusion weights from fixed values to dynamically adjustable parameters. By introducing learnable weight parameters that can be optimized through training on sample datasets, the system enables flexible adjustment of data contribution from different sources, dimensions, and time periods, thereby improving fusion performance.
2Adaptability or versatility
If traditional data fusion methods are used, then data integration is achieved, but the methods lack adaptability to dynamic changes in the crop growth environment
Solution Approach 1:
The patent implements feedback mechanisms where the system continuously monitors data quality, environmental conditions, and fusion results. This feedback is used to adjust fusion weights and improve the system's adaptability to changing agricultural environments, ensuring reliable data fusion across varying conditions while maintaining robustness through iterative optimization.
Solution Approach 2:
The system performs self-service through automated weight adjustment and adaptive learning without requiring manual intervention. The model automatically adapts to new environmental conditions and data patterns by learning from sample datasets and continuously optimizing fusion parameters, enabling the system to serve itself in maintaining reliability and adaptability.
3Ease of operation
If fixed weight data fusion is used, then the fusion process is simple, but the relevance and timeliness of data fusion are reduced
Solution Approach 1:
The patent applies preliminary action by pre-training the weight adjustment model on comprehensive sample datasets that represent various agricultural scenarios. This pre-learning enables the system to quickly adapt to new situations with minimal additional training, maintaining both operational simplicity and data relevance by having the model already equipped with knowledge of optimal fusion strategies for different conditions.
Data Source
AI summary
A crop phenotype data fusion method and apparatus, an electronic device and a storage medium are provided. The method includes: inputting target data corresponding to the three-dimensional first to-be-fused crop phenotype data among the to-be-fused crop phenotype data into a weight adjustment model; acquiring a weight value corresponding to each first to-be-fused crop phenotype datum output by the weight adjustment model; and performing data fusion on the plurality of to-be-fused crop phenotype data based on the weight value corresponding to each first to-be-fused crop phenotype data and intrinsic and extrinsic parameter data of the crop phenotype sensors that collect the plurality of to-be-fused crop phenotype data. The target data includes environmental data when collecting crop phenotype data, relative position information between the crop phenotype sensors and the crop, and the target parameter values of the crop phenotype data. The target parameter values include signal-to-noise ratios and/or feature entropies.