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Machine vision-based detection method for key phenotypic information of shiitake mushroom stipes
Jiuxiao Zhao1,2, Mingfei Wang1,2, Zuolin Li2,3
1Intelligent Equipment Technology Research Center,Beijing Academy of Agriculture and Forestry Sciences, Beijing, China.
Frontiers in Plant Science
|February 9, 2026
Summary
Accurate measurement of shiitake mushroom stipe traits is now possible with advanced image processing. This breakthrough enhances shiitake breeding efficiency and crop quality.
Area of Science:
- Agricultural Science
- Computer Vision
- Biotechnology
Background:
- Measuring key shiitake mushroom stipe traits is challenging and inaccurate.
- This limitation hinders breeding efficiency and product quality in shiitake cultivation.
Purpose of the Study:
- To develop an accurate and efficient method for detecting key shiitake mushroom stipe traits.
- To address the limitations in current shiitake breeding practices.
Main Methods:
- Developed a stipe detection model (ACmix-ADown-YOLOv11n) and precise segmentation using EfficientSAM.
- Utilized OpenCV for calculating 12 phenotypic features and a CoTAttention-YOLOv11n-Ghost-pose algorithm for centerline prediction.
Main Results:
- The detection model achieved 93.7% AP, and the pose algorithm achieved 97.2% AP with 96.1% recall.
- High accuracy (R² > 0.978) was observed for predicting stipe lengths across categories.
Conclusions:
- The proposed image processing method is effective and reliable for detecting shiitake stipe traits.
- This technology significantly supports intelligent shiitake breeding and improves cultivation quality.
Keywords:
centerline detectiondeep learningdetection of stipe phenotypeedge detectionshiitake mushroom breedingMore Related Videos
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