Agricultural Seeding Rate Recommendations With Yield Response Curves
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Solution Overview
Problem
Existing seed planting methods often rely on subjective biases and lack objective data, leading to inefficiencies in crop performance due to suboptimal seeding rates.
Innovation Solution
A computer-implemented method and system that utilizes machine learning models to analyze historical data from multiple agricultural fields, including yield, seeding rates, soil, and genetic data, to recommend optimal seeding rates based on yield response curves and economic returns, using an ensemble of XGBRegressor models for precise predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional subjective methods are used to determine seeding rates, then the process is simple and quick, but the accuracy and objectivity of seeding rate recommendations deteriorate
Solution Approach 1:
The patent replaces traditional subjective human judgment with an automated machine learning system that processes historical field data, soil data, weather data, and genetic data to objectively determine optimal seeding rates. This substitution of mechanical/automated systems for human judgment resolves the contradiction by providing precise, data-driven recommendations without requiring complex manual analysis procedures.
Solution Approach 2:
The patent introduces an ensemble of XGBRegressor machine learning models as an intermediary between raw agricultural data and seeding rate recommendations. These models process multiple data sources (historical yield, soil properties, weather conditions, genetic information) and translate them into actionable seeding rate guidance, resolving the contradiction by providing accurate recommendations through a structured computational intermediary rather than direct subjective assessment.
2Measurement precision
If historical data from multiple fields is analyzed using machine learning models, then the accuracy of yield predictions improves, but the data processing time and computational complexity increase
Solution Approach 1:
The patent performs preliminary actions by pre-processing and storing historical field data, soil data, and weather data in structured formats before actual seeding rate recommendations are needed. The ensemble models are trained in advance on comprehensive historical datasets, so that when seeding rate recommendations are required, the system can quickly query pre-processed data and generate recommendations without performing extensive real-time analysis, thus reducing data processing time while maintaining high accuracy.
3Reliability
If ensemble of XGBRegressor models is used for predictions, then the reliability of seeding rate recommendations improves, but the computational resources required increase
Solution Approach 1:
The patent employs an ensemble of XGBRegressor models, which uses multiple models rather than a single model to make predictions. This partial or excessive action approach improves reliability by aggregating predictions from multiple models, reducing the impact of individual model errors. The ensemble approach processes data through several models and combines their outputs, providing more robust and reliable seeding rate recommendations at the cost of increased computational resources, which directly addresses the contradiction by prioritizing reliability over minimal energy consumption.
Data Source
AI summary
Systems and methods are provided for use in recommending seeding rates for agricultural fields. An example computer-implemented method includes accessing data related to multiple agricultural fields in a region and separating the accessed data into a training set and a validation set, based on timing associated with harvest of crops of the multiple agricultural fields. The method also includes training an ensemble of models, representative of seeding rate relative to yield, based on the training set, and generating a response curve, defining a yield response to seeding rate, based on the trained ensemble of models and generating a validation curve, based on the validation set. The method further includes calculating an error between the generated response curve and the validation curve and recommending a seeding rate for a target field in the region, based on the response curve and the calculated error.