Crop Location Detection from Tool Depth and Plant Bed Profile
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Solution Overview
Problem
Current agricultural technologies lack efficient methods for autonomously detecting and accurately locating crops in a field, which hinders precise agricultural operations such as weeding, watering, and fertilizing, often requiring additional sensors and increasing costs.
Innovation Solution
An autonomous machine equipped with a ground-facing camera and depth sensors, capable of navigating agricultural fields, records images and depth information to estimate the surface profile and real lateral location of target plants, allowing it to perform agricultural functions like weeding, watering, and fertilizing with high accuracy without needing additional depth sensors or cameras.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If additional sensors (depth sensors and ground-facing cameras) are added to improve crop location detection accuracy, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The depth sensors and ground-facing cameras are integrated into the existing agricultural implement structure, allowing these sensors to serve multiple functions: depth mapping, crop detection, and surface profile estimation. This multi-functionality approach improves measurement precision while minimizing the addition of separate dedicated components, thereby controlling device complexity.
Solution Approach 2:
The system uses the implement's own structural components (tool modules, toolbar) as reference frames for sensor calibration and positioning. The implement structure itself provides the geometric relationships needed to transform sensor data into accurate crop location information, eliminating the need for additional external calibration equipment or reference systems.
2Measurement precision
If additional sensors are installed to achieve accurate crop location detection, then measurement precision is improved, but manufacturing cost increases
Solution Approach 1:
By designing the sensor system to perform multiple tasks (depth sensing, crop detection, surface mapping) with a single integrated setup, the patent reduces the total number of components that need to be manufactured and assembled. This approach maintains high measurement precision while lowering manufacturing costs through component consolidation.
Solution Approach 2:
The system creates a digital copy or model of the physical implement structure and crop field using sensor data. This virtual model allows for accurate crop location detection and analysis without requiring physical markers, test crops, or expensive calibration fixtures during manufacturing and deployment.
3Measurement precision
If the system processes data from multiple sensors to estimate surface profile and crop location, then measurement precision is improved, but loss of time in data processing increases
Solution Approach 1:
The system performs preliminary calibration of the sensor system to the implement structure during manufacturing or initial setup. Geometric relationships between sensors, toolbar, and tool modules are pre-established and stored. During field operation, this pre-calibrated data allows for rapid crop location calculation without real-time complex calibration computations, thus improving accuracy while minimizing processing time.
Solution Approach 2:
The patent replaces complex real-time mechanical measurement systems with computational methods. Instead of using additional mechanical sensors or physical measurement devices during operation, the system uses computer vision and data processing algorithms to calculate crop locations from sensor images and depth data, significantly reducing processing time while maintaining high precision.
4Device complexity
If the autonomous machine uses tool module extension distances and lateral positions to estimate surface profile, then device complexity is reduced, but measurement precision may be affected by tool module positioning accuracy
Solution Approach 1:
The system continuously monitors the extension distances and lateral positions of tool modules relative to the toolbar and uses this feedback to dynamically adjust and refine the surface profile estimation. By incorporating real-time positional feedback from the tool modules, the system compensates for positioning variations and maintains high measurement precision without requiring additional complex sensing equipment.
Data Source
AI summary
A method for detecting real lateral locations of target plants includes: recording an image of a ground area at a camera; detecting a target plant in the image; accessing a lateral pixel location of the target plant in the image; for each tool module in a set of tool modules arranged behind the camera and in contact with a plant bed: recording an extension distance of the tool module; and recording a lateral position of the tool module relative to the camera; estimating a depth profile of the plant bed proximal the target plant based on the extension distance and the lateral position of each tool module; estimating a lateral location of the target plant based on the lateral pixel location of the target plant and the depth profile of the plant bed surface proximal the target plant; and driving a tool module to a lateral position aligned with the lateral location of the target plant.