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 capable of operating at high speeds and in various lighting conditions, which travels along crop rows to detect pests and diseases through image analysis and transmit real-time data for precise pesticide or fungicide application.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual laborers scout fields for pests and diseases, then detection can be performed with human judgment, but the process is slow, expensive, and has high revisit time
Solution Approach 1:
The patent replaces manual labor with an autonomous vehicle system equipped with cameras and machine learning models. The system captures images of crops and uses computer vision algorithms to automatically detect pests and diseases, substituting human mechanical inspection with an automated optical and computational system. This resolves the contradiction by maintaining detection accuracy through advanced image analysis while dramatically increasing inspection speed and reducing revisit time.
Solution Approach 2:
The autonomous vehicle system performs self-directed field inspection without human intervention. It autonomously navigates through crops, captures images, processes data onboard using machine learning models, and identifies pests and diseases independently. This self-service capability eliminates the need for manual laborers while maintaining or improving detection accuracy through consistent algorithmic application across the entire field.
2Measurement precision
If robotic systems travel slowly to capture images, then image quality can be maintained, but it takes multiple days to inspect a field resulting in low sampling rate
Solution Approach 1:
The system performs preliminary actions by capturing multiple images at different positions and angles before final analysis. The autonomous vehicle takes overlapping images as it moves through the field, ensuring comprehensive coverage. This preliminary image capture approach allows the system to maintain image quality while moving at higher speeds, as the redundant images provide multiple opportunities to capture clear views of pests and diseases without requiring slow, methodical inspection.
Solution Approach 2:
The autonomous vehicle operates continuously through the field without stopping or slowing down significantly. The imaging system captures images continuously as the vehicle moves, processing data in real-time or near-real-time. This continuous operation eliminates the need for pause-and-inspect cycles, maintaining image quality through consistent capture while dramatically reducing total inspection time compared to slow, stop-and-go robotic systems.
3Illumination intensity
If existing robotic systems operate during the day, then natural lighting is available, but they cannot perform detection at night
Solution Approach 1:
The patent introduces artificial lighting as an intermediary to bridge the gap between natural light availability and operational requirements. LEDs or other light sources mounted on the autonomous vehicle provide illumination during nighttime operations, enabling the imaging system to capture images with sufficient quality regardless of ambient light conditions. This intermediary lighting system extends operational versatility to include night detection while maintaining image quality comparable to daytime operations.
4Productivity
If autonomous vehicles travel through crop rows, then detection coverage is improved, but plant leaves or branches are typically destroyed
Solution Approach 1:
The autonomous vehicle changes the spatial dimension of operation by traveling in the inter-row spaces between crop rows rather than through the crop canopy itself. This dimensional shift allows the vehicle to access and inspect crops from the side, maintaining detection coverage of leaves and plants while avoiding direct contact that would cause damage. The imaging system captures images of crop foliage from this lateral perspective, achieving comprehensive detection without the mechanical damage associated with vehicles traveling through dense plant areas.
Data Source
AI summary
An agricultural autonomous vehicle is provided which is operable to traverse a field and perform one or more detection tasks.