Area-wise Semantic Segmentation for Plant Processing
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Solution Overview
Problem
Current pixel-wise semantic segmentation methods for plant processing in agriculture are inefficient due to high computational requirements, increased training effort, and poor quality of ground truth annotations, while maintaining high spatial resolution is necessary to preserve detail, leading to reduced accuracy and increased noise.
Innovation Solution
Implementing area-wise semantic segmentation using a neural network with a downsampling path and inserting prediction layers in the upsampling path to reduce spatial resolution, allowing for coarser segmentation and reducing computational load, and using inheritance rules for annotated training data to maintain detail and quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If pixel-wise semantic segmentation is used to maintain high spatial resolution, then image detail is preserved, but computational time and processing speed are significantly reduced
Solution Approach 1:
The patent divides the image processing task into two stages: first, coarse segmentation at reduced resolution to identify general plant locations; second, detailed analysis only for those segmented regions. This segmentation approach allows the system to maintain measurement precision where needed while improving overall processing speed by avoiding pixel-wise analysis of the entire high-resolution image.
Solution Approach 2:
The patent applies partial action by performing full-resolution semantic segmentation only on regions identified as containing plants, rather than processing the entire high-resolution image at full resolution. This selective approach maintains measurement precision for plant detection while significantly reducing the computational burden and improving processing speed.
2Measurement precision
If pixel-wise semantic segmentation is used to restore original resolution, then spatial accuracy is maintained, but training effort and computational resources are significantly increased
Solution Approach 1:
The training process is segmented into two parts: training a coarse segmentation model at reduced resolution, and then training a refinement model only for the upsampling path. This segmentation of training reduces the overall training time and computational resources required compared to training a complete pixel-wise segmentation model at full resolution.
Solution Approach 2:
The patent applies partial action in training by only training the upsampling path and prediction layers at full resolution, while keeping the downsampling path trained at reduced resolution. This approach maintains spatial accuracy for plant detection while significantly reducing training effort and computational resources compared to training the entire network at full resolution.
3Measurement precision
If pixel-wise semantic segmentation is used, then detailed plant structures are detected, but annotation effort and costs for ground truth are significantly increased
Solution Approach 1:
The patent applies partial action by requiring detailed pixel-level annotations only for regions containing plants, rather than annotating every pixel in the entire high-resolution image. The coarse segmentation at reduced resolution provides the framework, and detailed annotations are applied only where needed, significantly reducing annotation effort while maintaining detection accuracy for plant structures.
4Measurement precision
If pixel-wise semantic segmentation is used, then comprehensive feature analysis is performed, but noise in classification increases due to insufficient features per pixel
Solution Approach 1:
The patent segments the classification process into two levels: coarse classification at reduced resolution where sufficient features are available for reliable decision-making, and fine-grained classification only for plant-containing regions. This segmentation reduces classification noise by avoiding pixel-wise decisions where insufficient features would lead to unreliable classifications.
Solution Approach 2:
The patent applies partial action by performing comprehensive feature analysis and classification only for regions identified as containing plants, rather than attempting pixel-wise classification across the entire image. This approach improves reliability by ensuring that classification decisions are made only where sufficient features are available, reducing noise in the classification results.
Data Source
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AI summary
A method (100, 200) for treating plants growing in a field has the following steps: selecting (S102) a treatment tool for treating plants, said treatment tool having a specific spatial precision; capturing (S104) an image (10) of the field, said image being correlated with positional information; determining (S106, S206) a position of a plant to be treated in the field by means of semantic segmentation of the image (10), said position of the plant being determined using a spatial resolution which corresponds to the spatial precision of the treatment tool; guiding (S108) the treatment tool to the position of the plant; and treating (S110) the plant using the treatment tool.