Neural Network Object Identification for Automated Field Rock Removal
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for removing rocks and other objects from agricultural fields are inefficient, often requiring manual operation and multiple passes, leading to high labor costs and safety hazards due to the failure of existing implements to effectively clear rocks, which can damage equipment and pose safety risks.
Innovation Solution
The use of neural networks to identify objects in images collected by an image-collection vehicle, guiding an object-collection system to automatically pick up and remove identified objects from the field, by training multiple neural networks for different conditions and selecting the most accurate one based on image analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual implements (rakes, windrowers, sieves) are used to clear rocks, then field clearing can be performed, but the failure rate is high and multiple passes are required
Solution Approach 1:
The patent replaces manual mechanical implements (rakes, windrowers, sieves) with an automated system combining neural network-based computer vision and automated collection machinery. The neural networks identify rock locations with high accuracy, and the automated collection system removes rocks without human intervention, eliminating the need for multiple passes and significantly improving both reliability and productivity.
Solution Approach 2:
The system enables the collection vehicle to autonomously identify and collect rocks without human intervention. The neural networks automatically process images, locate rocks, and guide the collection mechanism, allowing the system to service itself and eliminating the need for manual operation and multiple passes.
2Reliability
If manual operation and human intervention are used to pick rocks, then rocks can be removed, but labor costs increase and work is slow
Solution Approach 1:
The patent replaces manual human labor with an automated system comprising neural networks for rock identification and an automated collection mechanism. This substitution eliminates the time-consuming nature of manual rock picking while maintaining reliable rock removal capability, significantly reducing the loss of time.
Solution Approach 2:
The system performs rock identification and collection autonomously without human intervention. The neural networks continuously analyze images and guide the collection mechanism, enabling the system to service itself and eliminate the time loss associated with manual operation.
3Device complexity
If a single neural network is used for object identification, then the system is simpler, but accuracy varies across different conditions
Solution Approach 1:
The patent divides the object identification task into multiple specialized neural networks, each trained for specific conditions (e.g., different rock types, lighting conditions, or environmental scenarios). This segmentation allows each network to specialize in particular conditions, improving identification accuracy across diverse scenarios while managing complexity through modular architecture.
Solution Approach 2:
Different neural networks are deployed for different local conditions or regions of the image. The system selects or switches between networks based on the specific conditions encountered, ensuring that the most appropriate specialized network handles each identification task, thereby optimizing accuracy for local conditions.
Data Source
AI summary
An object identification method is disclosed. The method includes training a first neural network for a first set of conditions regarding a first plurality of objects, training a second neural network for a second set of conditions regarding a second plurality of objects, receiving a plurality of target images associated with a target set of conditions in which to identify objects, analyzing the plurality of target images using the first and second neural networks to identify objects in the plurality of target images resulting in object identification information, and selecting the first neural network or the second neural network as a preferred neural network for the target set of conditions based on an analysis of the object identification information.