Real-Time Pose Estimation for Precision Weed Treatment
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Solution Overview
Problem
Current agricultural technologies face challenges in efficiently managing and optimizing crop growth and weed control, particularly in terms of land, chemical, time, and labor costs, necessitating a more advanced and integrated system for agricultural operations.
Innovation Solution
A machine learning-based agricultural vehicle system equipped with sensors and processors for real-time image analysis and treatment, capable of identifying agricultural objects, tracking their growth stages, and applying targeted treatments, such as chemical sprays or mechanical actions, to enhance crop management and weed control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning algorithms and real-time image analysis are implemented, then measurement precision and productivity are improved, but device complexity increases
Solution Approach 1:
The patent employs machine learning models as intermediary components between image capture and target identification. These models process visual data and translate it into actionable identification results, enabling high precision target detection without requiring complex custom-built recognition systems. The ML models serve as pre-trained intermediaries that bridge the gap between raw images and accurate target classification.
Solution Approach 2:
The system uses captured images as copies of the real-world agricultural environment, allowing analysis and identification without direct physical interaction with targets. By working with visual copies rather than physical samples, the system achieves high measurement precision while keeping the physical apparatus relatively simple.
2Loss of substance
If selective treatment activation is implemented, then loss of substance is reduced, but measurement precision must be improved
Solution Approach 1:
The patent applies local quality by delivering treatments specifically to identified target locations rather than uniform application across the entire field. The system determines precise spatial coordinates of detected targets and directs treatment mechanisms to those specific locations, ensuring chemicals are applied only where needed. This localized approach minimizes overall chemical usage while maintaining high detection accuracy requirements.
Solution Approach 2:
The machine learning system automatically identifies targets and triggers treatment activation without human intervention. The system serves itself by processing images, identifying targets, determining treatment parameters, and activating treatment mechanisms autonomously, reducing both chemical waste and labor requirements.
3Productivity
If real-time pose estimation and tracking are implemented, then productivity is improved, but use of energy increases
Solution Approach 1:
The system performs preliminary actions by pre-processing images and pre-identifying potential targets before final treatment decisions are made. Pose estimation and tracking are conducted on selected regions of interest rather than entire images, reducing computational energy requirements while maintaining high productivity in crop monitoring operations.
Solution Approach 2:
The patent segments the image processing task into distinct stages: initial image capture, region of interest identification, pose estimation on segmented regions, tracking, and treatment decision-making. By dividing the processing workload into segments rather than analyzing entire images continuously, the system achieves high productivity with reduced energy consumption per processing unit.
Data Source
AI summary
A method includes receiving sensor inputs including one or more images comprising one or more agricultural objects; continuously performing a pose estimation of the treatment system based on sensor inputs that are time synchronized and fused; identifying the one or more agricultural objects as target objects; tracking the one or more agricultural objects identified by the analyzing; controlling an orientation of the treatment mechanism according to the pose estimation for targeting the one or more agricultural objects; and activating the treatment mechanism to treat the one or more agricultural objects according to the orientation.