Work-Vehicle Crop Mapping Without Complex Sensors
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Solution Overview
Problem
Existing crop mapping technologies require expensive and sophisticated sensors like stereoscopic cameras and RADAR/LiDAR, limiting their use to modern, high-cost equipment.
Innovation Solution
A computer-implemented method using a single camera and a positioning system to map crops, employing a perception model and constructing lines of best fit to create a comprehensive crop map, even on less sophisticated vehicles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If stereoscopic cameras and multiple sensor modalities (RADAR, LiDAR) are used to map crops, then mapping accuracy and depth information are improved, but equipment cost and device complexity increase significantly
Solution Approach 1:
The patent extracts and eliminates the need for complex sensor arrangements (stereoscopic cameras, RADAR, LiDAR) by using a single camera combined with positioning system data. The invention takes out the essential functionality of crop detection and mapping while removing unnecessary complex sensors, achieving the same mapping capability with simpler equipment.
Solution Approach 2:
The patent creates a virtual copy of the complex sensor system's output by using image data from a single camera combined with positioning information. Instead of using multiple physical sensors to directly capture depth and position data, the system copies the functional output by processing 2D images with known vehicle position and orientation data to derive crop positions and depths virtually.
2Adaptability or versatility
If stereoscopic cameras and multiple sensors are installed on work vehicles, then crop mapping capability is improved, but vehicle cost increases
Solution Approach 1:
The patent replaces expensive, sophisticated sensors with a much cheaper single camera system. The camera can be a standard, off-the-shelf model rather than specialized stereoscopic or 3D sensing equipment. This principle applies by using a simple, inexpensive imaging device that can be easily installed on older work vehicles without requiring expensive sensor hardware.
Solution Approach 2:
The patent makes the single camera system universal by combining it with positioning system data (GPS, inertial measurement units) to achieve multiple functions: crop detection, position mapping, and depth estimation. The same single camera that captures images also serves as the basis for all mapping operations when combined with positioning data, eliminating the need for separate specialized sensors.
3Device complexity
If a single camera and positioning system are used to map crops, then device complexity and cost are reduced, but mapping precision may deteriorate
Solution Approach 1:
The patent introduces positioning system data (vehicle position, orientation, speed) as an intermediary that bridges the gap between simple camera images and accurate crop mapping. The positioning system acts as a mediator that provides the additional spatial information needed to accurately determine crop positions and depths from 2D camera images, compensating for the simplicity of the single camera system.
Solution Approach 2:
The patent transforms the 2D image data from the single camera into 3D spatial information by adding the positioning dimension. By combining 2D image coordinates with 3D vehicle position and orientation data from the positioning system, the system creates accurate 3D crop positions without needing multiple cameras or complex sensors. This dimensional transformation enables precise mapping with simplified hardware.
Data Source
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AI summary
The present disclosure provides a computer-implemented method of mapping crops in a field including a plurality of rows of crops. The computer-implemented method comprises: receiving, from a positioning system, positions of a work vehicle moving along a route between the plurality of rows of crops, and, from a camera mounted to a side of the work vehicle, images of the respective rows; detecting, using a perception model, the crops from the images; determining a position of each detected crop using the positions of the work vehicle and a predetermined lateral distance from the work vehicle; and constructing a map of the detected crops using their positions.