Plant Surface Spray Coverage Imaging for Real-Time Application Control
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Solution Overview
Problem
Current agrochemical spray systems lack real-time monitoring capabilities to quantify liquid coverage on plant surfaces, leading to inefficiencies in pesticide application and increased environmental impact.
Innovation Solution
A system and method for automatically quantifying liquid coverage on plant surfaces using image processing and machine learning algorithms to analyze pre-spray and post-spray images, adjusting sprayer parameters for optimal coverage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional spray systems are used without real-time monitoring, then pesticide application can be performed, but liquid coverage on plant surfaces cannot be quantified and optimized
Solution Approach 1:
The patent replaces manual visual inspection and physical measurement methods with automated image processing and machine learning algorithms. The system uses computer vision to capture images of plant surfaces and automatically quantifies liquid coverage through digital image analysis, eliminating the need for manual sampling and measurement while achieving precise, real-time coverage assessment.
Solution Approach 2:
The patent introduces an intermediary computational layer between the spray application and the operator. The image processing system and machine learning algorithms act as intermediaries that automatically analyze coverage images, calculate coverage metrics, and provide feedback to the operator, bridging the gap between physical spray application and intelligent decision-making.
2Adaptability or versatility
If farmers apply pesticides at recommended rates per acre, then standard application protocols are followed, but variability in application efficiency on plants is not accounted for
Solution Approach 1:
The patent implements a feedback mechanism where real-time image analysis of plant surface coverage is provided back to the operator. The system captures images during or after spray application, automatically calculates coverage percentages and distribution patterns, and feeds this information back to allow immediate adjustment of application rates and techniques, creating a closed-loop system that adapts to actual field conditions.
Solution Approach 2:
The patent enables preliminary assessment of spray coverage by capturing and analyzing images during the application process or immediately afterward. This allows farmers to evaluate coverage quality before leaving the field, identifying areas that require re-spraying or adjustment, and making informed decisions about additional application needs based on actual coverage data rather than assuming uniform distribution.
3Reliability
If spray parameters are optimized for calm conditions, then optimal coverage is achieved when wind speeds are negligible, but efficiency decreases when wind speeds increase to 2-3 mph
Solution Approach 1:
The patent transforms the static spray parameter settings into a dynamic system that adapts to changing environmental conditions. By using real-time image analysis to assess actual coverage outcomes, the system provides feedback that allows operators to dynamically adjust spray parameters such as pressure, flow rate, and nozzle positioning in response to varying wind conditions, maintaining effective coverage across different environmental scenarios.
Data Source
AI summary
Presented herein are systems and methods for automatically quantifying liquid coverage on exposed plant surfaces (e.g., leaves).