<?xml version="1.0" encoding="utf-8"?>
<export-example>
  <doc>
    <id>4085</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>13</pageNumber>
    <edition/>
    <issue/>
    <volume>5</volume>
    <articleNumber>68</articleNumber>
    <type>article</type>
    <publisherName>American Association for the Advancement of Science (AAAS)</publisherName>
    <publisherPlace>Washington</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Efficient Noninvasive FHB Estimation using RGB Images from a Novel Multiyear, Multirater Dataset</title>
    <abstract language="eng">Fusarium head blight (FHB) is one of the most prevalent wheat diseases, causing substantial yield losses and health risks. Efficient phenotyping of FHB is crucial for accelerating resistance breeding, but currently used methods are time-consuming and expensive. The present article suggests a noninvasive classification model for FHB severity estimation using red–green–blue (RGB) images, without requiring extensive preprocessing. The model accepts images taken from consumer-grade, low-cost RGB cameras and classifies the FHB severity into 6 ordinal levels. In addition, we introduce a novel dataset consisting of around 3,000 images from 3 different years (2020, 2021, and 2022) and 2 FHB severity assessments per image from independent raters. We used a pretrained EfficientNet (size b0), redesigned as a regression model. The results demonstrate that the interrater reliability (Cohen’s kappa, κ) is substantially lower than the achieved individual network-to-rater results, e.g., 0.68 and 0.76 for the data captured in 2020, respectively. The model shows a generalization effect when trained with data from multiple years and tested on data from an independent year. Thus, using the images from 2020 and 2021 for training and 2022 for testing, we improved the Fw1 score by 0.14, the accuracy by 0.11, κ by 0.12, and reduced the root mean squared error by 0.5 compared to the best network trained only on a single year’s data. The proposed lightweight model and methods could be deployed on mobile devices to automatically and objectively assess FHB severity with images from low-cost RGB cameras. The source code and the dataset are available at https://github.com/cvims/FHB_classification.</abstract>
    <parentTitle language="eng">Plant Phenomics</parentTitle>
    <identifier type="issn">2643-6515</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-40853</identifier>
    <enrichment key="opus_doi_flag">true</enrichment>
    <enrichment key="opus_import_data">{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,9,25]],"date-time":"2023-09-25T17:10:51Z","timestamp":1695661851843},"reference-count":51,"publisher":"American Association for the Advancement of Science (AAAS)","content-domain":{"domain":["spj.science.org"],"crossmark-restriction":true},"short-container-title":["Plant Phenomics"],"published-print":{"date-parts":[[2023,1]]},"abstract":"&lt;jats:p&gt;\n            &lt;jats:italic&gt;Fusarium&lt;\/jats:italic&gt;\n            head blight (FHB) is one of the most prevalent wheat diseases, causing substantial yield losses and health risks. Efficient phenotyping of FHB is crucial for accelerating resistance breeding, but currently used methods are time-consuming and expensive. The present article suggests a noninvasive classification model for FHB severity estimation using red\u2013green\u2013blue (RGB) images, without requiring extensive preprocessing. The model accepts images taken from consumer-grade, low-cost RGB cameras and classifies the FHB severity into 6 ordinal levels. In addition, we introduce a novel dataset consisting of around 3,000 images from 3 different years (2020, 2021, and 2022) and 2 FHB severity assessments per image from independent raters. We used a pretrained EfficientNet (size b0), redesigned as a regression model. The results demonstrate that the interrater reliability (Cohen\u2019s kappa,\n            &lt;jats:italic&gt;\u03ba&lt;\/jats:italic&gt;\n            ) is substantially lower than the achieved individual network-to-rater results, e.g., 0.68 and 0.76 for the data captured in 2020, respectively. The model shows a generalization effect when trained with data from multiple years and tested on data from an independent year. Thus, using the images from 2020 and 2021 for training and 2022 for testing, we improved the\n            &lt;jats:inline-formula&gt;\n              &lt;mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" display=\"inline\" overflow=\"scroll\"&gt;\n                &lt;mml:msubsup&gt;\n                  &lt;mml:mi&gt;F&lt;\/mml:mi&gt;\n                  &lt;mml:mn&gt;1&lt;\/mml:mn&gt;\n                  &lt;mml:mi&gt;w&lt;\/mml:mi&gt;\n                &lt;\/mml:msubsup&gt;\n              &lt;\/mml:math&gt;\n            &lt;\/jats:inline-formula&gt;\n            score by 0.14, the accuracy by 0.11,\n            &lt;jats:italic&gt;\u03ba&lt;\/jats:italic&gt;\n            by 0.12, and reduced the root mean squared error by 0.5 compared to the best network trained only on a single year\u2019s data. The proposed lightweight model and methods could be deployed on mobile devices to automatically and objectively assess FHB severity with images from low-cost RGB cameras. The source code and the dataset are available at\n            &lt;jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/cvims\/FHB_classification\"&gt;https:\/\/github.com\/cvims\/FHB_classification&lt;\/jats:ext-link&gt;\n            .\n          &lt;\/jats:p&gt;","DOI":"10.34133\/plantphenomics.0068","type":"journal-article","created":{"date-parts":[[2023,6,21]],"date-time":"2023-06-21T14:28:39Z","timestamp":1687357719000},"update-policy":"http:\/\/dx.doi.org\/10.34133\/aaas_crossmark_01","source":"Crossref","is-referenced-by-count":1,"title":["Efficient Noninvasive FHB Estimation using RGB Images from a Novel Multiyear, Multirater Dataset"],"prefix":"10.34133","volume":"5","author":[{"ORCID":"http:\/\/orcid.org\/0000-0002-9025-7824","authenticated-orcid":true,"given":"Dominik","family":"R\u00f6\u00dfle","sequence":"first","affiliation":[{"name":"AImotion Bavaria, Technische Hochschule Ingolstadt, Ingolstadt, Germany."}]},{"given":"Lukas","family":"Prey","sequence":"additional","affiliation":[{"name":"Hochschule Weihenstephan-Triesdorf, Weidenbach, Germany."}]},{"given":"Ludwig","family":"Ramgraber","sequence":"additional","affiliation":[{"name":"Saatzucht Josef Breun GmbH and Co. KG, Herzogenaurach, Germany."}]},{"given":"Anja","family":"Hanemann","sequence":"additional","affiliation":[{"name":"Saatzucht Josef Breun GmbH and Co. KG, Herzogenaurach, Germany."}]},{"given":"Daniel","family":"Cremers","sequence":"additional","affiliation":[{"name":"Technical University of Munich, Munich, Germany."}]},{"given":"Patrick Ole","family":"Noack","sequence":"additional","affiliation":[{"name":"Hochschule Weihenstephan-Triesdorf, Weidenbach, Germany."}]},{"given":"Torsten","family":"Sch\u00f6n","sequence":"additional","affiliation":[{"name":"AImotion Bavaria, Technische Hochschule Ingolstadt, Ingolstadt, Germany."}]}],"member":"221","reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00122-021-03807-0"},{"issue":"6","key":"e_1_3_1_3_2","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1007\/s42535-019-00054-z","article-title":"Fusarium infection in wheat, aggressiveness and changes in grain quality: A review","volume":"32","author":"Alconada TM","year":"2019","unstructured":"Alconada TM, Moure MC, Ortega LM. Fusarium infection in wheat, aggressiveness and changes in grain quality: A review. Vegetos. 2019;32(6):441\u2013449.","journal-title":"Vegetos"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1111\/mpp.12618"},{"issue":"4","key":"e_1_3_1_5_2","doi-asserted-by":"crossref","first-page":"333","DOI":"10.3920\/WMJ2019.2438","article-title":"Fusarium head blight and mycotoxins in wheat: Prevention and control strategies across the food chain","volume":"12","author":"Torres AM","year":"2019","unstructured":"Torres AM, Palacios SA, Yerkovich N, Palazzini JM, Battilani P, Leslie JF, Logrieco AF, Chulze SN. Fusarium head blight and mycotoxins in wheat: Prevention and control strategies across the food chain. World Mycotoxin J. 2019;12(4):333\u2013355.","journal-title":"World Mycotoxin J"},{"key":"e_1_3_1_6_2","unstructured":"Stack RW McMullen MP. A visual scale to estimate severity of Fusarium head blight in wheat. NDSU; November 1998. p. 1095."},{"issue":"1","key":"e_1_3_1_7_2","doi-asserted-by":"crossref","DOI":"10.1186\/s42483-020-00049-8","article-title":"From visual estimates to fully automated sensor-based measurements of plant disease severity: Status and challenges for improving accuracy","volume":"2","author":"Bock CH","year":"2020","unstructured":"Bock CH, Barbedo JGA, Ponte EMD, Bohnenkamp D, Mahlein A-K. From visual estimates to fully automated sensor-based measurements of plant disease severity: Status and challenges for improving accuracy. Phytopathol Res. 2020;2(1): Article 9.","journal-title":"Phytopathol Res"},{"issue":"18","key":"e_1_3_1_8_2","doi-asserted-by":"crossref","DOI":"10.3390\/app9183894","article-title":"Identification of Fusarium head blight in winter wheat ears based on fisher\u2019s linear discriminant analysis and a support vector machine","volume":"9","author":"Huang L","year":"2019","unstructured":"Huang L, Wu Z, Huang W, Ma H, Zhao J. Identification of Fusarium head blight in winter wheat ears based on fisher\u2019s linear discriminant analysis and a support vector machine. Appl Sci. 2019;9(18): Article 3894.","journal-title":"Appl Sci"},{"issue":"1","key":"e_1_3_1_9_2","first-page":"1","article-title":"Identification of Fusarium head blight in winter wheat ears using continuous wavelet analysis","volume":"20","author":"Ma H","year":"2020","unstructured":"Ma H, Huang W, Jing Y, Pignatti S, Laneve G, Dong Y, Ye H, Liu L, Guo A, Jiang J. Identification of Fusarium head blight in winter wheat ears using continuous wavelet analysis. Sensors. 2020;20(1):1\u201315.","journal-title":"Sensors"},{"key":"e_1_3_1_10_2","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2020.105588","article-title":"Integrating spectral and image data to detect Fusarium head blight of wheat","volume":"175","author":"Zhang DY","year":"2020","unstructured":"Zhang DY, Chen G, Yin X, Hu RJ, Gu CY, Pan ZG, Zhou XG, Chen Y. Integrating spectral and image data to detect Fusarium head blight of wheat. Comput Electron Agric. 2020;175: Article 105588.","journal-title":"Comput Electron Agric"},{"issue":"1","key":"e_1_3_1_11_2","doi-asserted-by":"crossref","first-page":"32","DOI":"10.3390\/agriculture4010032","article-title":"Hyperspectral and chlorophyll fluorescence imaging for early detection of plant diseases, with special reference to Fusarium spec. infections on wheat","volume":"4","author":"Bauriegel E","year":"2014","unstructured":"Bauriegel E, Herppich W. Hyperspectral and chlorophyll fluorescence imaging for early detection of plant diseases, with special reference to Fusarium spec. infections on wheat. Agriculture. 2014;4(1):32\u201357.","journal-title":"Agriculture"},{"issue":"23","key":"e_1_3_1_12_2","doi-asserted-by":"crossref","DOI":"10.3390\/rs11232752","article-title":"Establishment of plotyield prediction models in soybean breeding programs using UAV-based hyperspectral remote sensing","volume":"11","author":"Zhang X","year":"2019","unstructured":"Zhang X, Zhao J, Yang G, Liu J, Cao J, Li C, Zhao X, Gai J. Establishment of plotyield prediction models in soybean breeding programs using UAV-based hyperspectral remote sensing. Remote Sens. 2019;11(23): Article 2752.","journal-title":"Remote Sens"},{"issue":"3","key":"e_1_3_1_13_2","doi-asserted-by":"crossref","DOI":"10.3390\/rs10030395","article-title":"Classifying wheat hyperspectral pixels of healthy heads and Fusarium head blight disease using a deep neural network in the wild field","volume":"10","author":"Jin X","year":"2018","unstructured":"Jin X, Jie L, Wang S, Qi HJ, Li SW. Classifying wheat hyperspectral pixels of healthy heads and Fusarium head blight disease using a deep neural network in the wild field. Remote Sens. 2018;10(3): Article 395.","journal-title":"Remote Sens"},{"key":"e_1_3_1_14_2","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1016\/j.biosystemseng.2018.01.004","article-title":"Hyperspectral measurements of yellow rust and Fusarium head blight in cereal crops: Part 2: On-line field measurement","volume":"167","author":"Whetton RL","year":"2018","unstructured":"Whetton RL, Waine TW, Mouazen AM. Hyperspectral measurements of yellow rust and Fusarium head blight in cereal crops: Part 2: On-line field measurement. Biosyst Eng. 2018;167:144\u2013158.","journal-title":"Biosyst Eng"},{"key":"e_1_3_1_15_2","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.biosystemseng.2016.01.017","article-title":"A review on the main challenges in automatic plant disease identification based on visible range images","volume":"144","author":"Barbedo JGA","year":"2016","unstructured":"Barbedo JGA. A review on the main challenges in automatic plant disease identification based on visible range images. Biosyst Eng. 2016;144:52\u201360.","journal-title":"Biosyst Eng"},{"issue":"7","key":"e_1_3_1_16_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s11042-022-12160-3","article-title":"Deep learning in wheat diseases classification: A systematic review","volume":"81","author":"Kumar D","year":"2022","unstructured":"Kumar D, Kukreja V. Deep learning in wheat diseases classification: A systematic review. Multimed Tools Appl. 2022;81(7):1\u201345.","journal-title":"Multimed Tools Appl"},{"issue":"8","key":"e_1_3_1_17_2","first-page":"1","article-title":"Estimation of Fusarium head blight severity based on transfer learning","volume":"12","author":"Gao C","year":"2022","unstructured":"Gao C, Gong Z, Ji X, Dang M, He Q, Sun H, Guo W. Estimation of Fusarium head blight severity based on transfer learning. Agronomy. 2022;12(8):1\u201316.","journal-title":"Agronomy"},{"key":"e_1_3_1_18_2","doi-asserted-by":"crossref","unstructured":"Deng J Dong W Socher R Li L-J Li K Fei-Fei L. ImageNet: A large-scale hierarchical image database. Paper presented at: 2009 IEEE Conference on Computer Vision and Pattern Recognition; 2009 Jun 20\u201325; Miami USA.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"e_1_3_1_19_2","doi-asserted-by":"crossref","unstructured":"He K Zhang X Ren S Sun J. Deep residual learning for image recognition. Paper presented at: Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition; 2016 Jun 27\u201330; Las Vegas USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_1_20_2","unstructured":"Simonyan K Zisserman A. Very deep convolutional networks for large-scale image recognition. Paper presented at: International Conference on Learning Representations; 2015 Nov 03\u201306; Kuala Lumpur Malaysia."},{"key":"e_1_3_1_21_2","unstructured":"Howard AG Zhu M Chen B Kalenichenko D Wang W Weyand T Andreetto M Adam H. Mobilenets: Efficient convolutional neural networks for mobile vision applications. ArXiv. 2017. https:\/\/doi.org\/10.48550\/arXiv.1704.04861"},{"key":"e_1_3_1_22_2","doi-asserted-by":"crossref","DOI":"10.3389\/fpls.2020.599886","article-title":"Fusion of deep convolution and shallow features to recognize the severity of wheat Fusarium head blight","volume":"11","author":"Gu C","year":"2021","unstructured":"Gu C, Wang D, Zhang H, Zhang J, Zhang D, Liang D. Fusion of deep convolution and shallow features to recognize the severity of wheat Fusarium head blight. Fronti Plant Sci. 2021;11: Article 599886.","journal-title":"Fronti Plant Sci"},{"issue":"6","key":"e_1_3_1_23_2","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"Imagenet classification with deep convolutional neural networks","volume":"60","author":"Krizhevsky A","year":"2017","unstructured":"Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. Commun ACM. 2017;60(6):84\u201390.","journal-title":"Commun ACM"},{"issue":"20","key":"e_1_3_1_24_2","doi-asserted-by":"crossref","DOI":"10.3390\/rs11202375","article-title":"Using neural network to identify the severity of wheat Fusarium head blight in the field environment","volume":"11","author":"Zhang D","year":"2019","unstructured":"Zhang D, Wang D, Gu C, Jin N, Zhao H, Chen G, Liang H, Liang D. Using neural network to identify the severity of wheat Fusarium head blight in the field environment. Remote Sens. 2019;11(20): Article 2375.","journal-title":"Remote Sens"},{"issue":"22","key":"e_1_3_1_25_2","doi-asserted-by":"crossref","first-page":"2658","DOI":"10.3390\/rs11222658","article-title":"Detection of Fusarium head blight in wheat using a deep neural network and color imaging","volume":"11","author":"Qiu R","year":"2019","unstructured":"Qiu R, Yang C, Moghimi A, Zhang M, Steffenson BJ, Hirsch CD. Detection of Fusarium head blight in wheat using a deep neural network and color imaging. Remote Sens. 2019;11(22):2658.","journal-title":"Remote Sens"},{"key":"e_1_3_1_26_2","doi-asserted-by":"crossref","unstructured":"He K Gkioxari G Doll\u00b4ar P Girshick R Mask r-cnn. Paper presented at: 2017 IEEE International Conference on Computer Vision (ICCV); 2017 Oct 22\u201329; Venice Italy.","DOI":"10.1109\/ICCV.2017.322"},{"key":"e_1_3_1_27_2","doi-asserted-by":"crossref","unstructured":"Lin T-Y Maire M Belongie S Hays J Perona P Ramanan D Doll\u00e1r P Zitnick CL. Microsoft COCO: Common objects in context. Computer Vision \u2013 ECCV 2014 ; Cham: Springer; 2014. p. 740\u2013755","DOI":"10.1007\/978-3-319-10602-1_48"},{"issue":"9","key":"e_1_3_1_28_2","doi-asserted-by":"crossref","first-page":"1493","DOI":"10.3390\/agriculture12091493","article-title":"Automatic tandem dual BlendMask networks for severity assessment of wheat Fusarium head blight","volume":"12","author":"Gao Y","year":"2022","unstructured":"Gao Y, Wang H, Li M, Su W-H. Automatic tandem dual BlendMask networks for severity assessment of wheat Fusarium head blight. Agriculture. 2022;12(9):1493.","journal-title":"Agriculture"},{"issue":"1","key":"e_1_3_1_29_2","first-page":"1","article-title":"Automatic evaluation of wheat resistance to Fusarium head blight using dual mask-rcnn deep learning frameworks in computer vision","volume":"13","author":"Su WH","year":"2021","unstructured":"Su WH, Zhang J, Yang C, Page R, Szinyei T, Hirsch CD, Steffenson BJ. Automatic evaluation of wheat resistance to Fusarium head blight using dual mask-rcnn deep learning frameworks in computer vision. Remote Sens. 2021;13(1):1\u201320.","journal-title":"Remote Sens"},{"issue":"14","key":"e_1_3_1_30_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3390\/rs14143481","article-title":"A lightweight model for wheat ear Fusarium head blight detection based on RGB images","volume":"14","author":"Hong Q","year":"2022","unstructured":"Hong Q, Jiang L, Zhang Z, Ji S, Gu C, Mao W, Li W, Liu T, Li B, Tan C. A lightweight model for wheat ear Fusarium head blight detection based on RGB images. Remote Sens. 2022;14(14):1\u201320.","journal-title":"Remote Sens"},{"key":"e_1_3_1_31_2","unstructured":"Bochkovskiy A Wang C Liao HM. Yolov4: Optimal speed and accuracy of object detection. ArXiv. 2020. https:\/\/doi.org\/10.48550\/arXiv.2004.10934"},{"issue":"13","key":"e_1_3_1_32_2","doi-asserted-by":"crossref","DOI":"10.3390\/rs13132437","article-title":"Wheat Fusarium head blight detection using uav-based spectral and texture features in optimal window size","volume":"13","author":"Xiao Y","year":"2021","unstructured":"Xiao Y, Dong Y, Huang W, Liu L, Ma H. Wheat Fusarium head blight detection using uav-based spectral and texture features in optimal window size. Remote Sens. 2021;13(13): Article 2437.","journal-title":"Remote Sens"},{"issue":"2","key":"e_1_3_1_33_2","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1080\/07352681003617285","article-title":"Plant disease severity estimated visually, by digital photography and image analysis, and by hyperspectral imaging","volume":"29","author":"Bock CH","year":"2010","unstructured":"Bock CH, Poole GH, Parker PE, Gottwald TR. Plant disease severity estimated visually, by digital photography and image analysis, and by hyperspectral imaging. Crit Rev Plant Sci. 2010;29(2):59\u2013107.","journal-title":"Crit Rev Plant Sci"},{"issue":"8","key":"e_1_3_1_34_2","doi-asserted-by":"crossref","first-page":"806","DOI":"10.1094\/Phyto-83-806","article-title":"Assessing the accuracy, intra-rater repeatability, and inter-rater reliability of disease assessment systems","volume":"83","author":"Nutter FW","year":"1993","unstructured":"Nutter FW Jr. Assessing the accuracy, intra-rater repeatability, and inter-rater reliability of disease assessment systems. Phytopathol. 1993;83(8):806\u2013812.","journal-title":"Phytopathol"},{"issue":"1","key":"e_1_3_1_35_2","doi-asserted-by":"crossref","DOI":"10.1186\/s40537-019-0197-0","article-title":"A survey on image data augmentation for deep learning","volume":"6","author":"Shorten C","year":"2019","unstructured":"Shorten C, Khoshgoftaar TM. A survey on image data augmentation for deep learning. J Big Data. 2019;6(1): Article 60.","journal-title":"J Big Data"},{"key":"e_1_3_1_36_2","doi-asserted-by":"crossref","DOI":"10.3389\/fpls.2021.673505","article-title":"Wheat spike blast image classification using deep convolutional neural networks","volume":"12","author":"Fernandez-Campos M","year":"2021","unstructured":"Fernandez-Campos M, Huang YT, Jahanshahi MR, Wang T, Jin J, Telenko DE, Gongora-Canul C, Cruz CD. Wheat spike blast image classification using deep convolutional neural networks. Front Plant Sci. 2021;12: Article 673505.","journal-title":"Front Plant Sci"},{"key":"e_1_3_1_37_2","unstructured":"Tan M Le Q. EfficientNet: Rethinking model scaling for convolutional neural networks. In: K. Chaudhuri and R. Salakhutdinov editors. International conference on machine learning . Long Beach: PMLR; 2019. pp. 6105\u20136114."},{"issue":"8","key":"e_1_3_1_38_2","doi-asserted-by":"crossref","first-page":"1500","DOI":"10.3390\/plants10081500","article-title":"Image-based wheat fungi diseases identification by deep learning","volume":"10","author":"Genaev MA","year":"2021","unstructured":"Genaev MA, Skolotneva ES, Gultyaeva EI, Orlova EA, Bechtold NP, Afonnikov DA. Image-based wheat fungi diseases identification by deep learning. Plants. 2021;10(8):1500.","journal-title":"Plants"},{"issue":"19","key":"e_1_3_1_39_2","doi-asserted-by":"crossref","first-page":"7237","DOI":"10.3390\/s22197237","article-title":"Evaluation of effective class-balancing techniques for CNN-based assessment of Aphanomyces root rot resistance in pea (Pisum sativum L.)","volume":"22","author":"Divyanth LG","year":"2022","unstructured":"Divyanth LG, Marzougui A, Gonz\u00e1lez-Bernal MJ, McGee RJ, Rubiales D, Sankaran S. Evaluation of effective class-balancing techniques for CNN-based assessment of Aphanomyces root rot resistance in pea (Pisum sativum L.). Sensors. 2022;22(19):7237.","journal-title":"Sensors"},{"issue":"12","key":"e_1_3_1_40_2","first-page":"2643","article-title":"Strawberry fungal leaf scorch disease identification in real-time strawberry field using deep learning architectures","volume":"10","author":"Abbas I","year":"2021","unstructured":"Abbas I, Liu J, Amin M, Tariq A, Tunio MH. Strawberry fungal leaf scorch disease identification in real-time strawberry field using deep learning architectures. Plan Theory. 2021;10(12):2643.","journal-title":"Plan Theory"},{"key":"e_1_3_1_41_2","doi-asserted-by":"publisher","DOI":"10.3390\/rs14246345"},{"key":"e_1_3_1_42_2","unstructured":"Bundessortenamt Richtlinien f\u00fcr die Durchf\u00fchrung von landwirtschaftlichen Wertpr\u00fcfungen und Sortenversuchen. In: Richtlinien f\u00fcr die Durchf\u00fchrung von landwirtschaftlichen Wertpr\u00fcfungen und Sortenversuchen . 2000. pp. 1\u2013348; https:\/\/www.bundessortenamt.de\/bsa\/media\/Files\/Richtlinie_LW2000.pdf."},{"key":"e_1_3_1_43_2","unstructured":"Kingma DP Ba J. Adam: A method for stochastic optimization. Paper presented at: ICLR 2015. Proceedings of the 3rd International Conference on Learning Representations; 2015 May 7\u20139; San Diego USA."},{"issue":"23","key":"e_1_3_1_44_2","doi-asserted-by":"crossref","first-page":"2858","DOI":"10.3390\/rs11232858","article-title":"Assessment of the degree of building damage caused by disaster using convolutional neural networks in combination with ordinal regression","volume":"11","author":"Ci T","year":"2019","unstructured":"Ci T, Liu Z, Wang Y. Assessment of the degree of building damage caused by disaster using convolutional neural networks in combination with ordinal regression. Remote Sens. 2019;11(23):2858.","journal-title":"Remote Sens"},{"issue":"1","key":"e_1_3_1_45_2","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1177\/001316446002000104","article-title":"A coefficient of agreement for nominal scales","volume":"20","author":"Cohen J","year":"1960","unstructured":"Cohen J. A coefficient of agreement for nominal scales. Educ Psychol Meas. 1960;20(1):37\u201346.","journal-title":"Educ Psychol Meas"},{"issue":"3","key":"e_1_3_1_46_2","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1080\/00029238.1971.11080840","article-title":"A new procedure for assessing reliability of scoring EEG sleep recordings","volume":"11","author":"Cicchetti DV","year":"1971","unstructured":"Cicchetti DV, Allison T. A new procedure for assessing reliability of scoring EEG sleep recordings. Am J EEG Technol. 1971;11(3):101\u2013110.","journal-title":"Am J EEG Technol"},{"issue":"4","key":"e_1_3_1_47_2","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1016\/j.ipm.2009.03.002","article-title":"A systematic analysis of performance measures for classification tasks","volume":"45","author":"Sokolova M","year":"2009","unstructured":"Sokolova M, Lapalme G. A systematic analysis of performance measures for classification tasks. Inf Process Manag. 2009;45(4):427\u2013437.","journal-title":"Inf Process Manag"},{"key":"e_1_3_1_48_2","doi-asserted-by":"crossref","unstructured":"Plevris V Solorzano G Bakas N Seghier MB Investigation of performance metrics in regression analysis and machine learning-based prediction models. Paper presented at: 8th European Congress on Computational Methods in Applied Sciences and Engineering (ECCOMAS Congress 2022). 2022 Nov 24.","DOI":"10.23967\/eccomas.2022.155"},{"key":"e_1_3_1_49_2","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.compag.2018.08.013","article-title":"Impact of dataset size and variety on the effectiveness of deep learning and transfer learning for plant disease classification","volume":"153","author":"Barbedo JGA","year":"2018","unstructured":"Barbedo JGA. Impact of dataset size and variety on the effectiveness of deep learning and transfer learning for plant disease classification. Comput Electron Agric. 2018;153:46\u201353.","journal-title":"Comput Electron Agric"},{"issue":"11","key":"e_1_3_1_50_2","doi-asserted-by":"crossref","first-page":"2784","DOI":"10.3390\/agronomy12112784","article-title":"Efficient identification of apple leaf diseases in the wild using convolutional neural networks","volume":"12","author":"Yang Q","year":"2022","unstructured":"Yang Q, Duan S, Wang L. Efficient identification of apple leaf diseases in the wild using convolutional neural networks. Agronomy. 2022;12(11):2784.","journal-title":"Agronomy"},{"key":"e_1_3_1_51_2","doi-asserted-by":"crossref","first-page":"983625","DOI":"10.3389\/fpls.2022.983625","article-title":"DIANA: A deep learning-based paprika plant disease and pest phenotyping system with disease severity analysis","volume":"13","author":"Ilyas T","year":"2022","unstructured":"Ilyas T, Jin H, Siddique MI, Lee SJ, Kim H, Chua L. DIANA: A deep learning-based paprika plant disease and pest phenotyping system with disease severity analysis. Front Plant Sci. 2022;13:983625.","journal-title":"Front Plant Sci"},{"key":"e_1_3_1_52_2","unstructured":"Mirza M Osindero S. Conditional generative adversarial nets. arXiv. 2014. https:\/\/doi.org\/10.48550\/arXiv.1411.1784."}],"container-title":["Plant Phenomics"],"original-title":[],"language":"en","deposited":{"date-parts":[[2023,7,14]],"date-time":"2023-07-14T18:03:03Z","timestamp":1689357783000},"score":1,"resource":{"primary":{"URL":"https:\/\/spj.science.org\/doi\/10.34133\/plantphenomics.0068"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1]]},"references-count":51,"alternative-id":["10.34133\/plantphenomics.0068"],"URL":"http:\/\/dx.doi.org\/10.34133\/plantphenomics.0068","relation":{},"ISSN":["2643-6515"],"issn-type":[{"value":"2643-6515","type":"electronic"}],"subject":["Agronomy and Crop Science"],"published":{"date-parts":[[2023,1]]},"assertion":[{"value":"2023-01-25","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-06-19","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-07-14","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}</enrichment>
    <enrichment key="local_crossrefDocumentType">journal-article</enrichment>
    <enrichment key="local_import_origin">crossref</enrichment>
    <enrichment key="local_doiImportPopulated">SubjectUncontrolled_1,PersonAuthorFirstName_1,PersonAuthorLastName_1,PersonAuthorIdentifierOrcid_1,PersonAuthorFirstName_2,PersonAuthorLastName_2,PersonAuthorFirstName_3,PersonAuthorLastName_3,PersonAuthorFirstName_4,PersonAuthorLastName_4,PersonAuthorFirstName_5,PersonAuthorLastName_5,PersonAuthorFirstName_6,PersonAuthorLastName_6,PersonAuthorFirstName_7,PersonAuthorLastName_7,PublisherName,TitleMain_1,Language,TitleAbstract_1,TitleParent_1,Volume,CompletedYear,IdentifierIssn</enrichment>
    <enrichment key="opus.source">doi-import</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.34133/plantphenomics.0068</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_DownloadUrl">https://github.com/cvims/FHB_classification</enrichment>
    <enrichment key="THI_DownloadUrl">https://spj.science.org/doi/suppl/10.34133/plantphenomics.0068/suppl_file/plantphenomics.0068.f1.pdf</enrichment>
    <enrichment key="THI_articleversion">published</enrichment>
    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Dominik</first_name>
      <last_name>Rößle</last_name>
    </author>
    <author>
      <first_name>Lukas</first_name>
      <last_name>Prey</last_name>
    </author>
    <author>
      <first_name>Ludwig</first_name>
      <last_name>Ramgraber</last_name>
    </author>
    <author>
      <first_name>Anja</first_name>
      <last_name>Hanemann</last_name>
    </author>
    <author>
      <first_name>Daniel</first_name>
      <last_name>Cremers</last_name>
    </author>
    <author>
      <first_name>Patrick Ole</first_name>
      <last_name>Noack</last_name>
    </author>
    <author>
      <first_name>Torsten</first_name>
      <last_name>Schön</last_name>
    </author>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19309">Fakultät Informatik</collection>
    <collection role="institutes" number="19379">AImotion Bavaria</collection>
    <collection role="persons" number="41270">Schön, Torsten</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/4085/plantphenomics.0068.pdf</file>
  </doc>
  <doc>
    <id>2582</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>42</pageFirst>
    <pageLast>47</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <articleNumber/>
    <type>conferenceobject</type>
    <publisherName>IEEE</publisherName>
    <publisherPlace>Piscataway</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2022-08-01</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">AImotion Challenge Results: a Framework for AirSim Autonomous Vehicles and Motion Replication</title>
    <parentTitle language="eng">2022 2nd International Conference on Computers and Automation (CompAuto 2022): Proceedings</parentTitle>
    <identifier type="isbn">978-1-6654-8194-6</identifier>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_openaccess">nein</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1109/CompAuto55930.2022.00015</enrichment>
    <enrichment key="THI_conferenceName">2022 2nd International Conference on Computers and Automation (CompAuto), Paris (France), 18.-20.08.2022</enrichment>
    <author>
      <first_name>Bruno</first_name>
      <last_name>José Souza</last_name>
    </author>
    <author>
      <first_name>Lucas C.</first_name>
      <last_name>de Assis</last_name>
    </author>
    <author>
      <first_name>Dominik</first_name>
      <last_name>Rößle</last_name>
    </author>
    <author>
      <first_name>Roberto</first_name>
      <last_name>Zanetti Freire</last_name>
    </author>
    <author>
      <first_name>Daniel</first_name>
      <last_name>Cremers</last_name>
    </author>
    <author>
      <first_name>Torsten</first_name>
      <last_name>Schön</last_name>
    </author>
    <author>
      <first_name>Munir</first_name>
      <last_name>Georges</last_name>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>framework</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>automous vehicle</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>computational intelligence</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>TurtleBot</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>AirSim</value>
    </subject>
    <collection role="institutes" number="19309">Fakultät Informatik</collection>
    <collection role="institutes" number="19379">AImotion Bavaria</collection>
    <collection role="persons" number="40770">Georges, Munir</collection>
    <collection role="persons" number="41270">Schön, Torsten</collection>
  </doc>
</export-example>
