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Development of image-based wheat spike counter through a Faster R-CNN algorithm and application for genetic studies

The Crop Journal 2022年第5期10卷 1303-1311页

作者： Lei Li Muhammad Adeel Hassan Shurong Yang Furong Jing Mengjiao Yang Awais Rasheed Jiankang Wang Xianchun Xia Zhonghu He Yonggui Xiao Institute of Crop Sciences National Wheat Improvement CentreChinese Academy of Agricultural Sciences(CAAS)Beijing 100081China Electronic Information School Foshan PolytechnicFoshan 528137GuangdongChina International Maize and Wheat Improvement Centre(CIMMYT)China Office c/o CAASBeijing 100081China Department of Plant Sciences Quaid-i-Azam UniversityIslamabad 44000Pakistan

Spike number(SN) per unit area is one of the major determinants of grain yield in wheat. Development of high-throughput techniques to count SN from large populations enables rapid and cost-effective selection and facilitates genetic studies. In the present study, we used a deep-learning algorithm, i.e., Faster Region-based Convolutional Neural Networks(Faster R-CNN) on Red-Green-Blue(RGB) images to explore the possibility of image-based detection of SN and its application to identify the loci underlying SN. A doubled haploid population of 101 lines derived from the Yangmai 16/Zhongmai 895 cross was grown at two sites for SN phenotyping and genotyped using the high-density wheat 660 K SNP *** of manual spike number(MSN) in the field, image-based spike number(ISN), and verification of spike number(VSN) by Faster R-CNN revealed significant variation(P < 0.001) among genotypes, with high heritability ranged from 0.71 to 0.96. The coefficients of determination(R^(2)) between ISN and VSN was 0.83, which was higher than that between ISN and MSN(R^(2)= 0.51), and between VSN and MSN(R^(2)= 0.50). Results showed that VSN data can effectively predict wheat spikes with an average accuracy of 86.7% when validated using MSN data. Three QTL ***-4 DS, ***-7 DS, and ***-7 DL were identified based on MSN, ISN and VSN data, while ***-7 DS was detected in all the three data sets. These results indicate that using Faster R-CNN model for image-based identification of SN per unit area is a precise and rapid phenotyping method, which can be used for genetic studies of SN in wheat.

关键词： deeping learning High-throughput phenotyping QTL mapping RGB imaging

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Mixed Normal Vector Estimation Strategy for Unstructured Point Clouds

Mixed Normal Vector Estimation Strategy for Unstructured Poi...

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第32届中国控制与决策会议

作者： Zhaochen Zhang Wenkai Shi Rui Wu Mengjuan Yu Jianhui Nie College of Automation ＆ College of Artificial Intelligence Nanjing university of Posts and Telecommunications

The normal vector is a basic attribute of point clouds and it has important applications in point clouds matching,surface reconstruction,feature line extraction and many other *** normal vector estimation methods have poor robustness and are vulnerable to complex features,noise and ***,convolutional neural network（CNN） has made great progress in normal vector ***,the algorithm needs high hardware configuration and the efficiency becomes low on devices without Graphic Processing Unit（GPU）.To address the problem,we propose a normal vector estimation algorithm combining the principal component analysis（PCA） and ***,with Surface Variation（SV） applied,the point clouds is divided into two subsets:the feature region and the flat ***,the PCA algorithm is adopted in the flat region while the CNN method is adopted in the region close to complex ***,the structure of an existing CNN is optimized to reduce the running time and improve the estimation *** show that our method achieves good results in dealing with complex features and ***,compared with CNN with the original network structure,the algorithm effectively reduces the computation time and improves the accuracy of normal vector estimation.

关键词： Point Clouds Normal Estimation deeping learning PCA CNN

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PREDICTION OF THE SKULL'S ACOUSTIC PARAMETERS IN TRANSCRANIAL FOCUSED ULTRASOUND BASED ON NEURAL NETWORK

PREDICTION OF THE SKULL'S ACOUSTIC PARAMETERS IN TRANSCRANIA...

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2016年全国压电和声波理论及器件应用研讨会

作者： Xiang-da WANG Nan-xing LI Wei-jun LIN Chang SU State Key Laboratory of Acoustics Institute of Acoustics Chinese Academy of Sciences National Network New Media Engineering Research Center Institute of Acoustics Chinese Academy of Sciences

ISBN: (纸本)9781509011902

Background, Motivation and Objective The model for calculating the transcranial focused ultrasound(tc FUS) field is the Westervelt equation, in which the acoustic parameters of the skull are necessary. Thus, in order to achieve the precise focusing of transcranial ultrasound, it’s of great importance and significance to obtain accurate acoustic parameters of the skull. In the past few decades, deep learning based on the neural network has got a rapid progress and is of much popularity. In this paper, through the use of computed tomography(CT) of a monkey skull from Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, we have proposed a method utilizing feed-forward neural network(FFNN) to precisely predict the acoustic parameters of the skull based on its CT with tiny errors. Moreover, the predicting results of the acoustic parameters are applied to numerically simulate the transcranial focused ultrasound field, so as to further verify the effectiveness of the proposed method. Statement of Contribution/Methods We employ the method put forward by M. Fink et al to generate the learning data set. In their model, the acoustic parameters of the skull including density, sound velocity and acoustic absorption coefficient can be calculated based on the skull’s CT data. With the learning data set, our goal is to acquire the mapping relationship between the CT data and the different acoustic parameters through FFNN, namely the appropriate FFNN structure. In the following, focused ultrasonic fields calculated by the numerical algorithm of finite difference time domain(FDTD) with the predicted and the hypothetically real acoustic parameters of the monkey skull are compared and analyzed to further demonstrate the effectiveness of the proposed method. Results Prediction results show that the density, sound velocity and acoustic absorption coefficient are precisely predicted by our method with the accuracy rate of 99.95% at least. Furthermore, comparison of the

关键词： Transcranial focused ultrasound deeping learning Neural network

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