ORP-extractor: A novel pipeline for extracting the phenotypic parameters of growing Oudemansiella raphanipies based on synthetic dataset

IF 3.3 3区 农林科学 Q2 FOOD SCIENCE & TECHNOLOGY
Hua Yin, Lisi Wu, Quan Wei, Chaohui Guo, Minghui Chen, Long Xue, Chunqin Chen, Yinglong Wang
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引用次数: 0

Abstract

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.

Abstract Image

Abstract Image

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
利用计算机视觉获取表型参数对于智能育种、栽培管理和自动化收获至关重要。然而,裂缝乌德曼氏杆菌的闭塞性阻碍了准确的分割和表型信息的收集。本研究提出ORP-extractor (Oudemansiella raphanipies表型提取器),这是一种深度学习模型,旨在解决上述挑战。首先,为了实现单个Oudemansiella raphanies的实例分割并获得其完整的形状,设计了一种新的改进的Mask R-CNN网络(OR R-CNN),该网络综合了Cross-Criss注意力模块和PointNet的优点。此外,利用帽杆轮廓的形状先验性,提出了一种自动测量位置搜索方法来辅助表型参数的提取。最后,结合测量位置和深度图像计算4个表型参数(帽直径、帽高、茎直径和茎长)。此外,为了提高标注的准确性和节省成本,还提出了一种新的用于orp提取器训练的遮挡图像合成策略。分割结果显示,orp提取器的分割率为86.58%,而大小估计结果显示,orp提取器对帽径、帽高、茎径和茎长的分割率分别为4%、3%、7%和4%。该方法的优点是对遮挡的弧弧弧菌的大小进行分割和估计具有鲁棒性,可用于加速弧弧弧菌智能养殖、优化管理和机器人收获的发展。
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来源期刊
Journal of Food Measurement and Characterization
Journal of Food Measurement and Characterization Agricultural and Biological Sciences-Food Science
CiteScore
6.00
自引率
11.80%
发文量
425
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.
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