Agronomy Map Annotations for Lodged Crop Harvest Decisions
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Solution Overview
Problem
Agricultural machines face challenges in effectively managing and analyzing agronomy data, particularly in situations where crops are lodged due to unfavorable weather or soil conditions, which can impact harvest yields and machine operations.
Innovation Solution
A method is developed to generate georeferenced annotations on a map by analyzing crop state data and agricultural characteristic data, including lodging direction, magnitude, health metrics, and weather conditions, to provide alerts, explanations, predictions, recommendations, and prescriptions for improving crop management and machine operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If agricultural machines operate in lodged crop conditions, then machine operations continue, but crop management effectiveness deteriorates
Solution Approach 1:
The system continuously collects agronomy data from sensors during machine operations, processes this data through analysis modules, and provides real-time feedback through the display interface. This closed-loop feedback enables operators to adjust operations based on actual crop conditions, maintaining management effectiveness even when operating in challenging lodged crop conditions.
Solution Approach 2:
The control system acts as an intermediary between the agricultural machine operations and the crop management objectives. It processes raw sensor data through multiple analysis modules (lodging detection, yield prediction, health assessment) and translates this information into actionable insights displayed to operators, bridging the gap between machine operation and crop management effectiveness.
2Loss of information
If multiple types of agronomy data are collected and analyzed, then actionable insights are generated, but system complexity increases
Solution Approach 1:
The data analysis system is segmented into multiple specialized modules, each handling a specific type of agronomy data (lodging detection, yield prediction, health assessment). This modular segmentation allows the system to process diverse data types through dedicated analysis pathways, reducing overall system complexity while maintaining comprehensive information processing capabilities.
Solution Approach 2:
The control system is designed with multi-functional analysis modules that can process various types of agronomy data (crop state, environmental conditions, soil properties) through unified data processing frameworks. This universality allows the system to handle multiple data types without proportionally increasing complexity, as the same infrastructure supports diverse analytical functions.
3Productivity
If real-time agronomy data analysis is performed, then harvest yield optimization is achieved, but processing time increases
Solution Approach 1:
The system performs preliminary data processing and analysis during crop growth operations, preparing agronomy data and identifying trends before harvest begins. By conducting yield predictions and health assessments in advance, the system reduces the time required for final harvest optimization decisions, enabling faster response during the critical harvest period.
Solution Approach 2:
The data collection and analysis process operates continuously throughout the growing season, maintaining constant monitoring of crop conditions, environmental factors, and yield parameters. This continuous action ensures that the system accumulates data over time without interruption, enabling real-time harvest yield optimization without significant processing delays when harvest begins.
Data Source
AI summary
Annotations for a map of a worksite are generated by a controller. The controller receives georeferenced crop state data and georeferenced agricultural characteristic data, collectively referred to as agronomy data. The georeferenced crop state data and georeferenced agricultural characteristic data are analyzed by the controller to determine relationships therebetween. The controller generates the annotations based on the determined relationships. The controller generates explanations, which are type of annotation, based on determined relationships between current agronomy data and historical agronomy data.