Spray Pattern Visualization Using Laser Diffraction Data
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Solution Overview
Problem
Current methods for analyzing and communicating agricultural spray patterns, particularly in terms of spray drift and leaf coverage, are inefficient and time-consuming, often resulting in incomplete and untimely results due to the complexity of variables involved, such as nozzle type and environmental factors.
Innovation Solution
A computer-implemented method that generates and displays agricultural spray pattern information based on user-selected variables, using laser diffraction analysis and environmental parameters like wind speed and boom height, allowing users to visualize droplet size distribution and assess spray effectiveness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If spray pattern testing is conducted to provide desired result data, then measurement precision is improved, but loss of time increases due to the time-consuming nature of testing
Solution Approach 1:
The system performs preliminary spray pattern analysis by collecting and storing spray data from multiple nozzles and environmental conditions in advance. When a user requests spray pattern information, the pre-analyzed data is quickly retrieved and presented, eliminating the need for time-consuming real-time testing while maintaining measurement precision through comprehensive pre-collected data.
2Reliability
If comprehensive spray testing including multiple variables is performed, then reliability of spray data is improved, but device complexity increases due to multiple testing parameters
Solution Approach 1:
The system segments the comprehensive spray testing into separate, independent components: nozzle type testing, environmental condition testing (wind speed, temperature, humidity), and spray pattern analysis. Each component is tested and stored separately in a database, allowing users to selectively combine results without requiring all components to be tested simultaneously, thus reducing system complexity while maintaining data reliability.
3Adaptability or versatility
If spray data is customized for individual presentations, then adaptability of information is improved, but loss of time increases due to manual customization requirements
Solution Approach 1:
The system creates standardized templates for spray pattern presentations that can be quickly copied and customized for different users. Pre-formatted report templates contain placeholder fields for various spray parameters, allowing users to generate customized presentations by simply filling in specific data points from the database rather than manually creating each presentation from scratch, significantly reducing preparation time while maintaining adaptability.
4Measurement precision
If complete spray analysis including all environmental factors is performed, then measurement precision is improved, but productivity decreases due to incomplete data availability
Solution Approach 1:
The system is designed to handle multiple types of spray analysis requests simultaneously using a single comprehensive database that stores spray pattern data under various environmental conditions. The database can quickly retrieve and present relevant data subsets based on different user needs (e.g., only wind speed effects, or only temperature effects, or combined effects), providing high-speed responses for specific queries while maintaining the capability for comprehensive analysis when needed, thus improving both precision and productivity.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables rapid delivery of meaningful spray test data in a user-friendly format, allowing users to compare and refine spray parameters, assess spray drift, and determine effective crop treatment under various environmental conditions.
Implementation Method 1
sprays analyzed using laser diffraction analysis
Data Source
AI summary
Computer-implemented systems and methods predict behavior of sprays based on receiving a selection of one or more variables affecting spray. Relative amounts of the droplets forming the spray are grouped into various droplet size classes, where each droplet size class represents a range of droplet sizes. The relative amounts of the spray in the classes is visually depicted on a computer display according to a distribution of droplets, a volume of spray falling within the droplet size classes, a chart depicting relative amounts of the spray as a function of droplet size, or according to a spray quality based on environmental factors.