Autonomous Crop Scouting Vehicle for High-Speed Pest Detection
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Solution Overview
Problem
Current agricultural systems face challenges in efficiently detecting and addressing pests and diseases in crops, leading to reduced harvest yields and increased economic losses due to manual labor inefficiencies and limitations of existing robotic systems.
Innovation Solution
An autonomous vehicle system equipped with cameras, machine learning models, and lighting devices for image capture and analysis, capable of operating at high speeds and in various lighting conditions, to detect pests and diseases and guide precise pesticide or fungicide applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Difficulty of detecting and measuring
If manual laborers scout fields for pests and diseases, then detection can be performed with simple equipment, but the sampling rate is low and revisit time is high
Solution Approach 1:
The patent replaces manual mechanical scouting with an autonomous robotic vehicle equipped with sensors, cameras, and machine learning algorithms. The robot autonomously navigates fields, captures images of crops, and uses computer vision to detect pests and diseases, substituting human labor with automated mechanical and computational systems to achieve higher sampling rates and more frequent monitoring.
2Measurement precision
If robotic systems travel slowly to capture images, then image quality can be maintained, but the inspection time increases and sampling rate decreases
Solution Approach 1:
The patent implements periodic action by having the robotic vehicle travel at higher speeds between sampling points and perform rapid sequential imaging at designated locations. The system uses intermittent capture rather than continuous slow movement, allowing the robot to maintain detection accuracy while reducing overall inspection time and increasing the frequency of field coverage.
3Ease of manufacture
If existing robotic systems are used for weed detection, then simple static object detection can be performed, but the systems cannot detect moving pests or operate at night
Solution Approach 1:
The patent applies universality by designing a multi-functional robotic system that can perform multiple detection tasks: identifying static weeds, detecting moving pests through motion-sensitive cameras and algorithms, and operating in various lighting conditions using integrated illumination devices. This transforms a simple weed-detection robot into a versatile platform capable of comprehensive crop health monitoring.
4Productivity
If existing robotic systems operate continuously, then coverage can be maximized, but crop damage occurs due to physical contact
Solution Approach 1:
The patent uses another dimension by employing overhead aerial imaging from the robotic vehicle to monitor crops from above rather than requiring physical contact at ground level. This dimensional shift allows continuous field coverage and frequent revisits without the robot's mechanical components damaging sensitive crop plants during operation.
Data Source
AI summary
An agricultural autonomous vehicle is provided which is operable to traverse a field and perform one or more detection tasks.