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Research on an Improved Segmentation Recognition Algorithm of Overlapping Agaricus bisporus
Shuzhen Yang1,2, Bowen Ni1, Wanhe Du2
1School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China.
Sensors (Basel, Switzerland)
|May 28, 2022
Summary
This study presents an advanced algorithm for segmenting and recognizing overlapping Agaricus bisporus mushrooms in automated picking systems. The method achieves over 96% accuracy, improving efficiency in complex agricultural environments.
Area of Science:
- Agricultural Engineering
- Computer Vision
- Robotics
Background:
- Automated harvesting of Agaricus bisporus faces challenges in accurately identifying and segmenting overlapping mushrooms.
- Complex adhesion between mushrooms hinders precise identification in factory environments.
Purpose of the Study:
- To develop a robust segmentation and recognition algorithm for overlapping Agaricus bisporus to enhance automated picking.
- To improve the accuracy and adaptability of mushroom identification systems in dynamic agricultural settings.
Main Methods:
- Image processing techniques including global gradient thresholding, Canny edge detection, and morphological operations.
- Feature extraction using Harris corner detection for identifying segmentation points.
- Contour merging and grouping with a branch definition algorithm.
- Outline reconstruction via least squares ellipse and minimum distance circle fitting.
Main Results:
- The algorithm effectively segments overlapping Agaricus bisporus, achieving a recognition rate exceeding 96%.
- Demonstrated adaptability to complex planting environments and uneven illumination conditions.
- Achieved an average coordinate deviation rate of less than 1.59%.
Conclusions:
- The proposed algorithm significantly enhances the capability of automated systems for identifying and segmenting overlapping Agaricus bisporus.
- This method offers a practical solution for improving efficiency and accuracy in mushroom harvesting operations.
