Predictive Seed Scripting for Soybeans Using Vegetative Index Productivity Scores
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional agricultural data management systems fail to accurately predict yields for fields with intra-field crop variability, as they do not account for geo-location-specific observations, leading to difficulties in optimizing seeding rates across subfields, which can result in suboptimal crop productivity.
Innovation Solution
A computer-implemented system that identifies target fields with intra-field crop variability by analyzing historical data and digital images to determine vegetative index values and productivity scores, allowing for adjusted seeding rates to be prescribed and applied at a subfield level, optimizing crop yield.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional agricultural data management systems are used to predict yields, then the prediction process is simple, but the accuracy of yield prediction for fields with intra-field variability deteriorates
Solution Approach 1:
The system divides fields into multiple subfields based on geo-location and observed variability, allowing yield prediction and seeding rate optimization to be performed at the subfield level rather than treating each field as a homogeneous unit. This segmentation enables accurate capture of intra-field variability while maintaining manageable system complexity through automated processing.
Solution Approach 2:
The system applies local quality by using geo-location-specific observations and subfield-level data to tailor yield predictions and seeding rate recommendations to specific areas within fields. This allows the system to account for local variations in soil, topography, and crop performance without requiring complex manual analysis of each area.
2Measurement precision
If soil probes are installed at every subfield level to measure crop variability, then measurement accuracy improves, but the cost and complexity of installation and maintenance increases
Solution Approach 1:
The system uses multi-functional data collection approaches that leverage existing agricultural data sources (yield monitors, satellite imagery, weather data, soil tests) to estimate crop variability at subfield levels. This universal approach eliminates the need for dedicated soil probes at every subfield while still achieving accurate measurement of crop variability through integration of multiple data types.
Solution Approach 2:
The system creates digital copies and models of field conditions using satellite imagery, aerial photography, and remote sensing data to represent physical field characteristics. These digital representations allow accurate assessment of crop variability without requiring physical soil probes at every location, reducing installation and maintenance complexity while preserving measurement precision.
3Productivity
If uniform seeding rates are applied across entire fields, then the seeding process is simple and fast, but crop productivity deteriorates due to inability to account for subfield variability
Solution Approach 1:
The system implements dynamic seeding rate adjustment by generating variable rate seeding prescriptions that adapt to subfield-specific conditions. The system automatically calculates optimal seeding rates for each subfield based on observed variability, crop productivity estimates, and yield goals, then provides these dynamic recommendations to guide precise seeding operations across different field areas.
Solution Approach 2:
The system performs preliminary analysis of field variability, digital image processing, and productivity estimation before the seeding operation. By completing these assessments in advance, the system prepares subfield-level seeding rate recommendations ahead of time, allowing farmers to implement variable rate seeding without adding complexity or time to the actual seeding process.
4Measurement precision
If detailed subfield-level observations are collected to understand yield variations, then yield prediction accuracy improves, but the amount of data and processing required increases
Solution Approach 1:
The system extracts only the most relevant information from large volumes of agricultural data by focusing on key indicators of crop variability and productivity. Through digital image processing and statistical analysis, the system identifies and extracts critical subfield-level characteristics from satellite imagery, aerial photos, and field observations, converting vast amounts of raw data into concise, actionable insights for yield prediction and seeding rate optimization.
Data Source
AI summary
A method and apparatus for adjusting seeding rates at a sub-field level is provided. The method comprises identifying, using a server computer, a set of target agricultural fields with intra-field crop variability based upon historical agricultural data comprising historical yield data and historical observed agricultural data for a plurality of fields; receiving, over a digital data communication network at the server computer, a plurality of digital images of the set of target agricultural fields; determining, using the server computer, vegetative index values for geo-locations within each field of the set of target agricultural fields using subsets of the plurality of digital images, wherein each subset among the subsets of the plurality of digital images corresponds to a specific target field in the set of target agricultural fields; for each target field in the set of target agricultural fields, determining, using the server computer, a plurality of sub-field zones based upon vegetative index values for geo-locations within each target field, wherein each sub-field zone of the plurality of sub-field zones contains similar vegetative index values; determining, using the server computer, vegetative index productivity scores for each sub-field zone of each target field in the set of target agricultural fields, wherein the vegetative index productivity scores represent a relative crop productivity specific to a type of seed planted within corresponding sub-fields zones; receiving, over a digital data communication network at the server computer, current seeding rates for each of the sub-field zones of the set of target agricultural fields; determining, using the server computer, adjusted seeding rates for each of the sub-fields of the set of target agricultural fields by adjusting the current seeding rates using the vegetative index productivity scores corresponding to each of the sub-fields zones; sending the adjusted seeding rates for each of the sub-field zones of each of the target agricultural fields to a field manager computing device.