Crop Prediction Models Using Normalized Agronomic Data
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Solution Overview
Problem
Agricultural producers face challenges in optimizing crop productivity due to incomplete or imperfect information about various geographic, weather-related, and environmental factors, limiting their ability to make informed decisions about planting, growing, and harvesting strategies.
Innovation Solution
A system that normalizes crop growth information from disparate sources and uses machine learning operations to train a crop prediction engine, which maps land characteristics and farming operations to expected crop productivity, providing optimized farming operations to enhance productivity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If growers utilize existing crop production models with available information, then some crop production decisions can be made, but the quantity of information is so large that it limits the amount of information a grower can utilize, resulting in incomplete or imperfect decision-making
Solution Approach 1:
The system segments the large volume of crop production information into structured data categories (field characteristics, weather data, soil properties, historical yields) that can be systematically processed. By dividing the information into manageable segments, the system enables comprehensive utilization of all available data without overwhelming the decision-making process.
Solution Approach 2:
The patent introduces an intermediary system comprising data normalization modules, feature extraction algorithms, and machine learning models that act as mediators between the raw information and the grower. This intermediary processes and transforms the large quantity of information into actionable insights, enabling complete information utilization while maintaining system manageability.
2Productivity
If more comprehensive information is collected from multiple data sources, then crop production decisions can be optimized, but the complexity of processing and analyzing this information increases
Solution Approach 1:
The system performs preliminary actions by pre-processing and normalizing data from multiple sources before the actual crop production analysis. Data normalization, feature extraction, and model training are conducted in advance, so that when crop production decisions are needed, the complex processing has already been completed, enabling optimization without overwhelming complexity during decision-making.
Solution Approach 2:
The patent replaces manual information processing mechanisms with automated computational systems. Machine learning models and algorithms automatically process and analyze data from multiple sources, substituting the mechanical process of manual analysis with intelligent automated systems that handle complexity while delivering optimized crop production recommendations.
3Productivity
If traditional crop production models are used with limited information, then the system remains simple to operate, but the crop productivity optimization is insufficient due to incomplete information analysis
Solution Approach 1:
The system employs self-service mechanisms where machine learning models automatically analyze all available information and generate crop production recommendations without requiring the grower to manually process data. The system serves itself by autonomously performing data normalization, analysis, and optimization, thereby achieving high crop productivity while maintaining ease of operation for the user.
Solution Approach 2:
The patent transforms the system from manual parameter-based decision-making to automated data-driven parameter optimization. By changing the operational parameters from simple model inputs to comprehensive processed data outputs, the system achieves superior crop productivity while the interface remains user-friendly, requiring minimal user input despite complex underlying analysis.
Data Source
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AI summary
A crop prediction system performs various machine learning operations to predict crop production and to identify a set of farming operations that, if performed, optimize crop production. The crop prediction system uses crop prediction models trained using various machine learning operations based on geographic and agronomic information. Responsive to receiving a request from a grower, the crop prediction system can access information representation of a portion of land corresponding to the request, such as the location of the land and corresponding weather conditions and soil composition. The crop prediction system applies one or more crop prediction models to the access information to predict a crop production and identify an optimized set of farming operations for the grower to perform.