Crop Growth Stage Prediction via Multi-Model Aggregation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional methods for predicting crop growth stages are often time-consuming, cumbersome, and subjective, providing limited effectiveness across the entire growth period of crops from planting to harvest, as they rely on on-site examination and may not accurately account for various growth stages.
Innovation Solution
A system and method that utilize a combination of multiple growth stage prediction models, each specific to a range of growth stages, to aggregate and merge predictions, providing accurate overall growth stage predictions for crops, and instruct agricultural machines based on these predictions for optimal treatment and harvesting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional on-site examination methods are used to determine crop growth stages, then the process is simple to operate, but the accuracy and effectiveness are limited across the entire growth period
Solution Approach 1:
The patent divides the crop growth period into multiple distinct growth stages, with each stage having its own specialized prediction model. This segmentation allows each model to focus on specific growth characteristics, improving overall prediction accuracy while maintaining manageable complexity through modular architecture
Solution Approach 2:
The system employs a unified platform that integrates multiple specialized models, allowing a single system to handle diverse prediction tasks across different growth stages. The aggregation mechanism enables the system to universally apply appropriate models based on the current growth stage, achieving both precision and versatility
2Measurement precision
If multiple growth stage models are used to predict crop growth stages, then the accuracy is improved, but the computational complexity and time required increase
Solution Approach 1:
The system performs preliminary classification to determine the current growth stage before applying the appropriate prediction model. This preliminary action prevents unnecessary computation by selecting only the relevant model for the current stage, reducing overall processing time while maintaining high accuracy through stage-specific optimization
Solution Approach 2:
Different prediction models with optimized characteristics are applied to different growth stages based on local requirements. Each model is tailored to the specific characteristics of its target growth stage, ensuring optimal performance without the overhead of running all models for every prediction
3Adaptability or versatility
If a single growth stage model is used, then the system is simple to implement, but it cannot accurately account for various growth stages throughout the crop growth period
Solution Approach 1:
The system dynamically selects and switches between different prediction models based on the current growth stage of the crop. This dynamic adaptation allows the system to handle the full range of growth stages with specialized models while maintaining a relatively simple operational interface through automated stage detection and model selection
Data Source
AI summary
Systems and methods for predicting growth stages of crops in fields are provided. One example computer-implemented method includes accessing data associated with a target field and/or a crop in the target field and generating growth stage predictions, via multiple growth stage models, each associated with an interval of a growth period of the crop. The method also includes aggregating the growth stage predictions from the multiple growth stage models and outputting the aggregate growth stage prediction for the field. The method then further includes instructing operation of an agricultural machine relative to the target field and/or the crop in the target field, based on the aggregate growth stage prediction for the field, to treat and/or harvest the crop in the field.