Hua Yin, Lisi Wu, Quan Wei, Chaohui Guo, Minghui Chen, Long Xue, Chunqin Chen, Yinglong Wang
{"title":"ORP-extractor:一种基于合成数据集提取生长弧菌表型参数的新管道","authors":"Hua Yin, Lisi Wu, Quan Wei, Chaohui Guo, Minghui Chen, Long Xue, Chunqin Chen, Yinglong Wang","doi":"10.1007/s11694-025-03390-8","DOIUrl":null,"url":null,"abstract":"<div><p>The acquisition of phenotype parameters with computer vision is crucial for smart breeding, cultivation management, and automated harvesting. However, occlusion in <i>Oudemansiella raphanipies</i> hinders accurate segmentation and phenotype information collection. This study proposes ORP-extractor (<i>Oudemansiella raphanipies</i> phenotype extractor), a deep learning model designed to address the above-mentioned challenges. Initially, to realize instance segmentation of individual <i>Oudemansiella raphanipies</i> and acquired its complete shape<i>,</i> a newly improved Mask R-CNN networks (named OR R-CNN) was designed, which integrated the advantages of the Cross-Criss attention module and PointNet. Furthermore, with the shape prior of the cap-stem contour, an automatic measurement-position search method was proposed to assist in phenotype parameter extraction. Finally, four phenotypic parameters (cap diameter, cap height, stem diameter and stem length) were calculated combining the measurement positions with depth image. In addition, to increase the accuracy of annotation and save cost, a novel occlusion image synthesis strategy for ORP-extractor training also introduced. The segmentation results showed an AP of 86.58%, while the size estimation results showed that the ORP-extractor achieved a MAPE of 4%, 3%, 7% and 4% for cap diameter, cap height, stem diameter, and stem length, respectively. The advantages of the present methodology are its robustness for segmenting and estimating the size of occluded <i>Oudemansiella raphanipies</i>, which can be used to help accelerate development of intelligent breeding, optimized management and robotic harvesting of <i>Oudemansiella raphanipies</i>.</p></div>","PeriodicalId":631,"journal":{"name":"Journal of Food Measurement and Characterization","volume":"19 9","pages":"6406 - 6424"},"PeriodicalIF":3.3000,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"ORP-extractor: A novel pipeline for extracting the phenotypic parameters of growing Oudemansiella raphanipies based on synthetic dataset\",\"authors\":\"Hua Yin, Lisi Wu, Quan Wei, Chaohui Guo, Minghui Chen, Long Xue, Chunqin Chen, Yinglong Wang\",\"doi\":\"10.1007/s11694-025-03390-8\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>The acquisition of phenotype parameters with computer vision is crucial for smart breeding, cultivation management, and automated harvesting. However, occlusion in <i>Oudemansiella raphanipies</i> hinders accurate segmentation and phenotype information collection. This study proposes ORP-extractor (<i>Oudemansiella raphanipies</i> phenotype extractor), a deep learning model designed to address the above-mentioned challenges. Initially, to realize instance segmentation of individual <i>Oudemansiella raphanipies</i> and acquired its complete shape<i>,</i> a newly improved Mask R-CNN networks (named OR R-CNN) was designed, which integrated the advantages of the Cross-Criss attention module and PointNet. Furthermore, with the shape prior of the cap-stem contour, an automatic measurement-position search method was proposed to assist in phenotype parameter extraction. Finally, four phenotypic parameters (cap diameter, cap height, stem diameter and stem length) were calculated combining the measurement positions with depth image. In addition, to increase the accuracy of annotation and save cost, a novel occlusion image synthesis strategy for ORP-extractor training also introduced. The segmentation results showed an AP of 86.58%, while the size estimation results showed that the ORP-extractor achieved a MAPE of 4%, 3%, 7% and 4% for cap diameter, cap height, stem diameter, and stem length, respectively. The advantages of the present methodology are its robustness for segmenting and estimating the size of occluded <i>Oudemansiella raphanipies</i>, which can be used to help accelerate development of intelligent breeding, optimized management and robotic harvesting of <i>Oudemansiella raphanipies</i>.</p></div>\",\"PeriodicalId\":631,\"journal\":{\"name\":\"Journal of Food Measurement and Characterization\",\"volume\":\"19 9\",\"pages\":\"6406 - 6424\"},\"PeriodicalIF\":3.3000,\"publicationDate\":\"2025-06-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Food Measurement and Characterization\",\"FirstCategoryId\":\"97\",\"ListUrlMain\":\"https://link.springer.com/article/10.1007/s11694-025-03390-8\",\"RegionNum\":3,\"RegionCategory\":\"农林科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"FOOD SCIENCE & TECHNOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Food Measurement and Characterization","FirstCategoryId":"97","ListUrlMain":"https://link.springer.com/article/10.1007/s11694-025-03390-8","RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"FOOD SCIENCE & TECHNOLOGY","Score":null,"Total":0}
ORP-extractor: A novel pipeline for extracting the phenotypic parameters of growing Oudemansiella raphanipies based on synthetic dataset
The acquisition of phenotype parameters with computer vision is crucial for smart breeding, cultivation management, and automated harvesting. However, occlusion in Oudemansiella raphanipies hinders accurate segmentation and phenotype information collection. This study proposes ORP-extractor (Oudemansiella raphanipies phenotype extractor), a deep learning model designed to address the above-mentioned challenges. Initially, to realize instance segmentation of individual Oudemansiella raphanipies and acquired its complete shape, a newly improved Mask R-CNN networks (named OR R-CNN) was designed, which integrated the advantages of the Cross-Criss attention module and PointNet. Furthermore, with the shape prior of the cap-stem contour, an automatic measurement-position search method was proposed to assist in phenotype parameter extraction. Finally, four phenotypic parameters (cap diameter, cap height, stem diameter and stem length) were calculated combining the measurement positions with depth image. In addition, to increase the accuracy of annotation and save cost, a novel occlusion image synthesis strategy for ORP-extractor training also introduced. The segmentation results showed an AP of 86.58%, while the size estimation results showed that the ORP-extractor achieved a MAPE of 4%, 3%, 7% and 4% for cap diameter, cap height, stem diameter, and stem length, respectively. The advantages of the present methodology are its robustness for segmenting and estimating the size of occluded Oudemansiella raphanipies, which can be used to help accelerate development of intelligent breeding, optimized management and robotic harvesting of Oudemansiella raphanipies.
期刊介绍:
This interdisciplinary journal publishes new measurement results, characteristic properties, differentiating patterns, measurement methods and procedures for such purposes as food process innovation, product development, quality control, and safety assurance.
The journal encompasses all topics related to food property measurement and characterization, including all types of measured properties of food and food materials, features and patterns, measurement principles and techniques, development and evaluation of technologies, novel uses and applications, and industrial implementation of systems and procedures.