Hybrid Crop Vision Navigation With Selective High-Resolution Imaging
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Solution Overview
Problem
Current agricultural technologies face inefficiencies and high costs in weed remediation and crop detection, as manual methods are labor-intensive and environmentally undesirable, and existing automated systems struggle to accurately distinguish between plants and weeds due to varying growth stages and lack of contrast in digital images.
Innovation Solution
An autonomous agricultural system equipped with GPS, high-resolution cameras, and machine vision capabilities that selectively switches between low-resolution and high-resolution image capture modes for rapid field traversal and accurate plant classification, using field maps and geolocation data to differentiate between crops and weeds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual methods are used for weed remediation and crop detection, then labor intensity and environmental harm are high, but automation can improve efficiency
Solution Approach 1:
The patent replaces manual mechanical weed removal and chemical herbicide application with an autonomous vehicle system equipped with computer vision and machine learning algorithms. The system uses cameras and processors to automatically detect, classify, and navigate to weeds, eliminating the need for manual labor and reducing environmental harm from herbicides.
Solution Approach 2:
The autonomous vehicle system performs self-navigation, self-detection, and self-classification of weeds and crops. The machine learning models continuously learn and improve their detection accuracy from field data, enabling the system to service itself without human intervention during operation.
2Measurement precision
If high-resolution image capture is used continuously for plant classification, then detection accuracy improves, but processing time and computational load increase
Solution Approach 1:
The system uses low-resolution imaging for most of the field to quickly identify potential weed locations, then applies high-resolution imaging only to suspected weed areas. This partial use of high-resolution capture reduces overall processing time while maintaining sufficient detection accuracy for weed identification and classification.
Solution Approach 2:
The patent divides the field inspection process into multiple stages: initial low-resolution scanning, intermediate classification, and detailed high-resolution analysis only for flagged areas. This segmentation allows the system to process large fields efficiently while maintaining high accuracy where needed.
Data Source
AI summary
In an embodiment, autonomous vehicles with global positioning systems (GPS) are used for field inspection to reduce fuel and labor costs and improve reliability with increased consistency in field crop inspection. A vehicle may be programmed to traverse a field while using sensors to detect objects and operating in a first image capture mode, for example, capturing low-resolution images of objects in the field, typically crops. Under program control, machine vision techniques are used with the low-resolution images to recognize crops, non-crop plant material or undefined objects. Under program control, location data is used to correlate recognized objects with digitally stored field maps to resolve whether a particular object is in a location at which crop planting is expected or not expected. Under program control, depending on whether an object in a low-resolution digital image is recognized as a crop, and whether the object is in an expected geo-location for crops, the vehicle may cease traversing temporarily and switch to a second image capture mode, for example, capturing a high-resolution image of the object, for use in disease analysis or classification, weed analysis or classification, alert notifications or other messages, or other processing. In this manner, a field may be rapidly traversed and imaged using coarse-level, rapid techniques that require lower processing resources, storage or memory, while automatically switching to execute special processing only when necessary to resolve unexpected objects or to perform operations such as disease classification that benefit from high-resolution images and more intensive use of processing resources, storage or memory.