Weed Removal Imaging With Human-in-the-Loop Plant Identification
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Solution Overview
Problem
Current systems for targeted weed control in agriculture struggle with real-time detection and differentiation of weeds in dense weed populations and adverse weather conditions, often leading to inaccurate removal of crop plants.
Innovation Solution
A device and method utilizing multiple cameras and a network of human users to manually identify and correct the positions of crop and weed plants via a server, ensuring accurate weed removal by transmitting images and positional data wirelessly, allowing for statistical evaluation and synchronization with the weed removal device.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated optical detection systems are used for real-time weed identification, then productivity is improved, but measurement precision deteriorates under dense weed populations and poor visual conditions
Solution Approach 1:
The patent introduces an intermediary step where captured images are transmitted to remote terminals for manual analysis by human users. This intermediary human review process acts as a mediator between automated detection and final weed removal decisions, allowing complex visual discrimination tasks to be performed by human cognition rather than purely automated systems, thereby maintaining high accuracy even in challenging visual conditions.
Solution Approach 2:
The system transitions from a single-dimension automated optical detection approach to a multi-dimensional solution by adding temporal dimension (delayed processing with manual review) and human cognitive dimension. Images are captured in real-time but processed through multiple dimensions: automated preliminary analysis, manual human review, and feedback loops, allowing the system to overcome the limitations of real-time automated visual discrimination in dense or adverse conditions.
2Productivity
If automated AI processing is used for plant differentiation, then productivity is improved, but reliability deteriorates when crops and weeds are visually similar
Solution Approach 1:
Human users serve as an intermediary verification layer for plant differentiation. When automated AI processing encounters visually similar crops and weeds that are difficult to differentiate, the system routes these cases to human reviewers who provide expert botanical knowledge and visual discrimination skills, thereby enhancing reliability without completely sacrificing productivity through selective manual review.
Solution Approach 2:
The system implements feedback mechanisms where manual review results are fed back into the automated processing system. This feedback loop allows the AI to learn from human expert decisions and continuously improve its differentiation accuracy, while also providing a verification mechanism that catches errors in difficult cases, thereby enhancing overall system reliability.
3Measurement precision
If manual image review by human users is implemented, then measurement precision is improved, but loss of time increases due to image transmission and review delays
Solution Approach 1:
The system applies partial manual review rather than reviewing all images manually. Automated processing handles the majority of clear, unambiguous cases quickly, while only images that fall below a confidence threshold or present difficult discrimination challenges are routed to manual review. This partial application of manual action maintains high accuracy for problematic cases while minimizing time loss by avoiding unnecessary manual review of obvious cases.
Solution Approach 2:
The automated system performs preliminary processing and pre-screening of images before they reach human reviewers. This preliminary action filters out easy cases that can be handled automatically, preparing and prioritizing only the difficult or ambiguous images for manual review, thereby reducing the overall time burden on human users while maintaining high accuracy for the cases that require their expertise.
4Productivity
If complex automated detection systems are deployed, then productivity is improved, but device complexity increases
Solution Approach 1:
The system segments the weed detection and control task into distinct functional modules: image capture by cameras, preliminary automated processing by AI algorithms, image transmission to remote terminals, manual review by human users, and feedback to the control system. This segmentation allows each component to be independently optimized and managed, reducing overall system complexity while maintaining high productivity through coordinated operation of specialized subsystems.
Data Source
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AI summary
A device for removing or destroying weeds (12), comprising a first image-capturing apparatus (1) for capturing a first ground image (2), a data-processing unit (3), which is connected to the first image-capturing apparatus, and an agricultural appliance (4), which is controlled by the data-processing unit (3) and is designed for the removal or destruction, at specific points, of weeds, the data-processing unit (3) being designed to receive the first ground image (2) from the first image-capturing apparatus (1), to receive manually determined position data (13) from at least one connected terminal (10), which position data indicate the position of weeds (12) and/or cultivated plants (11) and/or ground structures, for example planting rows, identified in the first ground image (2) by one or more users, and to control the agricultural appliance (4) on the basis of the position data (13) in such a way that the agricultural appliance selectively removes or destroys the weeds (12).