A Multi-Phenotype Acquisition System for Pleurotus eryngii Based on RGB and Depth Imaging

Abstract

High-throughput phenotypic acquisition and analysis allow us to accurately quantify trait expressions, which is essential for developing intelligent breeding strategies. However, there is still much potential to explore in the field of high-throughput phenotyping for edible fungi. In this study, we developed a portable multi-phenotypic acquisition system for Pleurotus eryngii using RGB and RGB-D cameras. We developed an innovative Unet-based semantic segmentation model by integrating the ASPP structure with the VGG16 architecture. This allows for precise segmentation of the cap, gills and stem of the fruiting body. By leveraging depth images from RGB-D cameras, we can effectively collect phenotypic information about Pleurotus eryngii. By combining K-means clustering with Lab color space thresholds, we are able to achieve more precise automatic classification of Pleurotus eryngii cap colors. Moreover, AlexNet is utilized to classify the shapes of the fruiting bodies. The Aspp-VGGUnet network demonstrates remarkable performance with a mean Intersection over Union (mIoU) of 96.47% and a mean pixel accuracy (mPA) of 98.53%. These results reflect respective improvements of 3.03% and 2.23% compared to the standard Unet model, respectively. The average error in size phenotype measurement is just 0.15 ± 0.03 cm. The accuracy for cap color classification reaches 91.04%, while fruiting body shape classification achieves 97.90%. The proposed multi-phenotype acquisition system reduces the measurement time per sample from an average of 76 s (manual method) to about 2 s, substantially increasing data acquisition throughput and providing robust support for scalable phenotyping workflows in breeding research.

Plant phenotyping relevance

RGB・深度画像による食用菌の複数形質取得システムを開発し、セグメンテーション、サイズ・色・形状の自動測定性能を検証しており、植物（菌類）の表現型取得が中心である。

Code and data availability

The paper's Pleurotus eryngii image dataset (607 RGB-D and 304 RGB images, 1136 annotated after augmentation) is described as proprietary, and no public code, model checkpoints, or data repository URL is provided. The Data Availability Statement only offers further inquiries via the corresponding author, so paper-pheny

No evidence-backed public reproduction asset is currently recorded.