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Fungal diversity notes 1512–1610: taxonomic and phylogenetic contributions on genera and species of fungal taxa

Fungal Diversity 2022年第6期117卷 1-272页

作者： Ruvishika S.Jayawardena Kevin D.Hyde Song Wang Ya‑Ru Sun Nakarin Suwannarach Phongeun Sysouphanthong Mohamed A.Abdel‑Wahab Faten A.Abdel‑Aziz Pranami D.Abeywickrama Vanessa P.Abreu Alireza Armand AndréAptroot Dan‑Feng Bao Dominik Begerow Jean‑Michel Bellanger Jadson D.P.Bezerra Digvijayini Bundhun Mark S.Calabon Ting Cao Taimy Cantillo João LVRCarvalho Napalai Chaiwan Che‑Chih Chen Régis Courtecuisse Bao‑Kai Cui Ulrike Damm Cvetomir M.Denchev Teodor T.Denchev Chun Y.Deng Bandarupalli Devadatha Nimali Ide Silva Lidiane Ados Santos Nawal K.Dubey Sylvain Dumez Himashi SFerdinandez André L.Firmino Yusufon Gaforov Achala J.Gajanayake Deecksha Gomdola Sugantha Gunaseelan Shucheng‑He Zin H.Htet Malarvizhi Kaliyaperumal Martin Kemler Kezhocuyi Kezo Nuwan DKularathnage Marco Leonardi Ji‑Peng Li Chunfang Liao Shun Liu Michael Loizides Thatsanee Luangharn Jian Ma Hugo Madrid S.Mahadevakumar Sajeewa S.N.Maharachchikumbura Dimuthu S.Manamgoda María P.Martín Niranjan Mekala Pierre‑Arthur Moreau Yan‑Hong Mu Pasouvang Pahoua Dhandevi Pem Olinto L.Pereira Wiphawanee Phonrob Chayanard Phukhamsakda Mubashar Raza Guang‑Cong Ren Andrea C.Rinaldi Walter Rossi Binu C.Samarakoon Milan CSamarakoon Vemuri V.Sarma Indunil C.Senanayake Archana Singh Maria F.Souza Cristina M.Souza‑Motta Adriano A.Spielmann Wenxin Su Xia Tang XingGuo Tian Kasun M.Thambugala Naritsada Thongklang Danushka S.Tennakoon Nopparat Wannathes DingPeng Wei Stéphane Welti Subodini N.Wijesinghe Hongde Yang Yunhui Yang Hai‑Sheng Yuan Huang Zhang Jingyi Zhang Abhaya Balasuriya Chitrabhanu SBhunjun Timur S.Bulgakov Lei Cai Erio Camporesi Putarak Chomnunti Y.S.Deepika Mingkwan Doilom Wei‑Jun Duan Shi‑Ling Han Naruemon Huanraluek EBGareth Jones NLakshmidevi Yu Li Saisamorn Lumyong Zong‑Long Luo Surapong Khuna Jaturong Kumla Ishara S.Manawasinghe Ausana Mapook Wilawan Punyaboon Saowaluck Tibpromma Yong‑Zhong Lu JiYe Yan Yong Wang Center of Excellence in Fungal Research Mae Fah Luang UniversityChiang Rai 57100Thailand School of Science Mae Fah Luang UniversityChiang Rai 57100Thailand Innovative Institute for Plant Health Zhongkai University of Agriculture and EngineeringGuangzhou 510225P.R.China Department of Plant Pathology College of AgricultureGuizhou UniversityGuiyang 550025GuizhouChina Research Center of Microbial Diversity and Sustainable Utilization Chiang Mai UniversityChiang Mai 50200Thailand Department of Biology Faculty of ScienceChiang Mai UniversityChiang Mai 50200Thailand Biotechnology and Ecology Institute Ministry of Agriculture and ForestryP.O.Box:811Vientiane CapitalLao PDR Department of Botany and Microbiology Faculty of ScienceSohag UniversitySohag 82524Egypt Beijing Key Laboratory of Environment-Friendly Management on Fruit Diseases and Pests in North China Institute of Plant ProtectionBeijing Academy of Agriculture and Forestry SciencesBeijing 100097China Departamento de Fitopatologia Universidade Federal de ViçosaViçosaMinas GeraisBrazil Laboratório de Botânica/Liquenologia Instituto de BiociênciasUniversidade Federal de Mato Grosso do SulAvenida Costa e Silva S/NBairro UniversitárioCampo GrandeMato Grosso do Sul CEP 79070-900Brazil College of Agriculture and Biological Sciences Dali UniversityDali 671003YunnanChina Department of Entomology and Plant Pathology Faculty of AgricultureChiang Mai UniversityChiang Mai 50200Thailand Institute of Plant Science and Microbiology Universität HamburgOrganismic Botany and MycologyOhnhorststr.1822609 HamburgGermany CEFE CNRSUniv.MontpellierEPHEIRDINSERM1919Route de Mende34293 Montpellier Cedex 5France Setor de Micologia Departamento de Biociências e TecnologiaInstituto de Patologia Tropical e Saúde PúblicaUniversidade Federal de GoiásRua 235S/NSetor UniversitárioGoiâniaGO CEP:74605-050Brazil Division of Biological Sciences College of Arts and SciencesUniversity of the Philippines Visayas5023 MiagaoIloiloPhilippines CA

This article is the 14th in the Fungal Diversity Notes series,wherein we report 98 taxa distributed in two phyla,seven classes,26 orders and 50 families which are described and *** in this study were collected from Australia,Brazil,Burkina Faso,Chile,China,Cyprus,Egypt,France,French Guiana,India,Indonesia,Italy,Laos,Mexico,Russia,Sri Lanka,Thailand,and *** are 59 new taxa,39 new hosts and new geographical distributions with one new *** 59 new species comprise Angustimassarina kunmingense,Asterina lopi,Asterina brigadeirensis,Bartalinia bidenticola,Bartalinia caryotae,Buellia pruinocalcarea,Coltricia insularis,Colletotrichum fexuosum,Colletotrichum thasutense,Coniochaeta caraganae,Coniothyrium yuccicola,Dematipyriforma aquatic,Dematipyriforma globispora,Dematipyriforma nilotica,Distoseptispora bambusicola,Fulvifomes jawadhuvensis,Fulvifomes malaiyanurensis,Fulvifomes thiruvannamalaiensis,Fusarium purpurea,Gerronema atrovirens,Gerronema favum,Gerronema keralense,Gerronema kuruvense,Grammothele taiwanensis,Hongkongmyces changchunensis,Hypoxylon inaequale,Kirschsteiniothelia acutisporum,Kirschsteiniothelia crustaceum,Kirschsteiniothelia extensum,Kirschsteiniothelia septemseptatum,Kirschsteiniothelia spatiosum,Lecanora immersocalcarea,Lepiota subthailandica,Lindgomyces guizhouensis,Marthe asmius pallidoaurantiacus,Marasmius tangerinus,Neovaginatispora mangiferae,Pararamichloridium aquisubtropicum,Pestalotiopsis piraubensis,Phacidium chinaum,Phaeoisaria goiasensis,Phaeoseptum thailandicum,Pleurothecium aquisubtropicum,Pseudocercospora vernoniae,Pyrenophora verruculosa,Rhachomyces cruralis,Rhachomyces hyperommae,Rhachomyces magrinii,Rhachomyces platyprosophi,Rhizomarasmius cunninghamietorum,Skeletocutis cangshanensis,Skeletocutis subchrysella,Sporisorium anadelphiae-leptocomae,Tetraploa dashaoensis,Tomentella exiguelata,Tomentella fuscoaraneosa,Tricholomopsis lechatii,Vaginatispora favispora and Wetmoreana *** new combin

关键词： 59 New taxa 39 New records Ascomycota Basidiomycota Dothideomycetes Lecanoromycetes Leotiomycetes Sordariomycetes Ustilaginomycotina

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Batik Pemalang Organic Wastewater Composition and Simple Electrocoagulation-Filtration Treatment

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IOP Conference Series: Earth and Environmental science 2020年第1期448卷

作者： N I Pratiwi S R Sari F Arifan A T Wulandari I Alkian B Mustasjar M B F B Aji Department of Physics Faculty of Science and Mathematics Diponegoro University Indonesia Department of Engineering Architectural Engineering Diponegoro University Indonesia Department of Industrial Chemical Engineering Technology Vocational School Diponegoro University Indonesia Nursing Science Study Program Faculty of Health Sciences Muhammadiyah University Purwokerto Indonesia Department of History Faculty of Humanities Diponegoro University Indonesia

Wastewater produced by batik industry in Cibelok Village, Pemalang that is disposed straight into the sewage will increase the concentration of Biochemical Oxygen Demand (BOD), ammonia (NH3), Chemical Oxygen Demand (COD), chromium (Cr), lead (Pb), watercolour, acidity (pH), and dissolved oxygen (DO). Disposed batik wastewater without treatment will contaminate the environment, mainly its water and soil. The main purpose of this research is to reduce the concentration of Chemical Oxygen Demand (COD), Biochemical Oxygen Demand (BOD), ammonia (NH3), chromium (Cr), lead (Pb), watercolour, acidity, and dissolved oxygen (DO) by treating batik Cibelok's waste and not contaminate the surrounding environment. Laboratory test results on batik waste in COD, BOD, DO, ammonia, chromium, lead, colour, and acidity (pH). These results are not in accordance with the 2014 Waste Water Quality Standard Number 5 by the Regulation of the Minister of Environment of the Republic of Indonesia. Alternatives that have been done to treat Cibelok Village's batik waste is to lower dangerous substances concentration through electrocoagulation and filtration. The processes could lower COD level around 57.6%, ammonia 98%, chromium 85%, and BOD 67%. Reducing the concentration of the substance makes the batik waste safer and environmentally friendly when it's finally disposed to the sewage.

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Preliminary study on Acoustical Characteristics of Sago Midrib Composite

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IOP Conference Series: Materials science and engineering 2019年第1期619卷

作者： Asniawaty Kusno Sherly Asriani Kusno Kamil Nurul Jamala Department of Architecture Faculty of Engineering Hasanuddin University Indonesia Study Program of Architecture Engineering Faculty Khairun University Indonesia Mechanical Engineering Department Engineering Faculty Universitas Muslim Indonesia Indonesia

This research is a preliminary study on the use of sago midrib as an acoustic material. This study aims to determine the sago as acoustic material by measuring the absorption coefficient using (impedance tube method) of 100 Hz-1.6 KHz frequencies. The results of measurement were analyzed and compared with common acoustic material available on market as reference. From the measurement of the sago midrib composite, absorption coefficient has fluctuated from low to high frequencies as the absorption coefficient of reference material is found to be more stable.

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Three-dimensional Tomography of Coseismic Ionospheric Disturbances from the 2016 West Sumatera Earthquake

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IOP Conference Series: Earth and Environmental science 2021年第1期936卷

作者： R W Rahayu M N Cahyadi B Muslim I M Anjasmara E Y Handoko I N Muafiry Department of Geomatics Engineering Faculty of Civil Engineering Environment and Geo-Engineering Institut Teknologi Sepuluh Nopember Surabaya Indonesia Research Center of Science-Technology of Marine and Earth Directorate of Research and Community Service Sepuluh Nopember Institute of Technology (ITS) Surabaya Indonesia Indonesian National Institute of Aeronautics and Space (LAPAN) Bandung 40173 Indonesia Survey and Mapping Study Program Sinar Mas Polytechnic Berau Coal Indonesia

Global Navigation Satellite System (GNSS) is a navigation system that uses satellite signals to determine its position, which consists of several satellites arranged in a constellation system. GNSS transmits signals to receivers on Earth. The GNSS receiver determines the user's position, speed, and time by processing the signals transmitted by the satellites. The initial purpose of launching the GNSS was for navigation purposes, but along with its development, GNSS can be used for the purposes of observing deformation of the earth's crust and in studying the atmosphere. The delayed wave data when passing through the ionosphere can be used to obtain Total Electron Content (TEC) values which then used to study ionospheric disturbances. Ionospheric disturbances are caused by various phenomena, the most common one is the ionospheric disturbances caused by the induction of acoustic and gravitational waves excited by co seismic crustal motions from large earthquakes. Ionospheric disturbances that happened before an earthquake are called Pre-seismic Ionospheric Disturbances and those that occur after an earthquake are called Co-seismic Ionospheric Disturbances (CID). Most studies of ionospheric disturbances still provide information on the timing and value of TEC anomalies in 2D form. Therefore, in this study, a 3D ionosphere profile modelling using computed 3D tomography will be carried out. The 3D information provided is in the form of time, ionosphere altitude and TEC anomaly value by utilizing GNSS data. The TEC anomaly value is obtained from the calculation of linear combination of the ionosphere. This study aims to obtain a spatial and temporal analysis of the CID caused by the West Sumatra Earthquake on March 2, 2016.

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Assessing Trustworthy AI in Times of COVID-19: Deep Learning for Predicting a Multiregional Score Conveying the Degree of Lung Compromise in COVID-19 Patients

IEEE Transactions on Technology and Society

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IEEE Transactions on Technology and Society 2022年第4期3卷 272-289页

作者： Allahabadi, Himanshi Amann, Julia Balot, Isabelle Beretta, Andrea Binkley, Charles Bozenhard, Jonas Bruneault, Frederick Brusseau, James Candemir, Sema Cappellini, Luca Alessandro Chakraborty, Subrata Cherciu, Nicoleta Cociancig, Christina Coffee, Megan Ek, Irene Espinosa-Leal, Leonardo Farina, Davide Fieux-Castagnet, Genevieve Frauenfelder, Thomas Gallucci, Alessio Giuliani, Guya Golda, Adam Van Halem, Irmhild Hildt, Elisabeth Holm, Sune Kararigas, Georgios Krier, Sebastien A. Kuhne, Ulrich Lizzi, Francesca Madai, Vince I. Markus, Aniek F. Masis, Serg Mathez, Emilie Wiinblad Mureddu, Francesco Neri, Emanuele Osika, Walter Ozols, Matiss Panigutti, Cecilia Parent, Brendan Pratesi, Francesca Moreno-Sanchez, Pedro A. Sartor, Giovanni Savardi, Mattia Signoroni, Alberto Sormunen, Hanna-Maria Spezzatti, Andy Srivastava, Adarsh Stephansen, Annette F. Theng, Lau Bee Tithi, Jesmin Jahan Tuominen, Jarno Umbrello, Steven Vaccher, Filippo Vetter, Dennis Westerlund, Magnus Wurth, Renee Zicari, Roberto V. Ey Netherlands Enterprise Intelligence Department Amsterdam1083 HP Netherlands Eth Zurich Health Ethics and Policy Lab Department of Health Sciences and Technology Zürich8092 Switzerland Center for Diplomatic and Strategic Studies Postgraduate Studies in Diplomacy and International Relations Paris75015 France Pisa56124 Italy Hackensack Meridian Health Bioethics Center EdisonNJ08820 United States University of Oxford Faculty of Philosophy OxfordOX2 6GG United Kingdom Collège André- Laurendeau Philosophie Department MontrealQCH8N 2J4 Canada Université du Québec À Montréal École des Médias MontrealQCH2L 2C4 Canada Pace University Philosophy Department New YorkNY10038 United States The Ohio State University Wexner Medical Center Department of Radiology ColumbusOH43210 United States Humanitas Research Hospital Department of Radiology Milan20089 Italy Humanitas University Department of Biomedical Sciences Milan20089 Italy University of New England Faculty of Science Agriculture Business and Law ArmidaleNSW2351 Australia University of Technology Sydney Faculty of Engineering and Information Technology SydneyNSW2007 Australia Scuola Superiore Sant'Anna European Centre of Excellence on the Regulation of Robotics and Ai Pisa56127 Italy University of Bremen Group of Computer Architecture Bremen28359 Germany New York University Grossman School of Medicine Division of Infectious Diseases and Immunology Department of Medicine New YorkNY10016 United States Digital Institute Ai Research Section Stockholm16731 Sweden Arcada University of Applied Sciences Department of Business Management and Analytics Helsinki00550 Finland University of Brescia Radiological Sciences and Public Health Department of Medical and Surgical Specialties Brescia25121 Italy Sncf Reseau Sa Ethique Groupe La Plaine93418 France Institute of Diagnostic and Interventional Radiology University Hospital Zurich Zürich8091 Switzerland Eindhoven University of Tech

This article's main contributions are twofold: 1) to demonstrate how to apply the general European Union's High-Level Expert Group's (EU HLEG) guidelines for trustworthy AI in practice for the domain of healthcare and 2) to investigate the research question of what does 'trustworthy AI' mean at the time of the COVID-19 pandemic. To this end, we present the results of a post-hoc self-assessment to evaluate the trustworthiness of an AI system for predicting a multiregional score conveying the degree of lung compromise in COVID-19 patients, developed and verified by an interdisciplinary team with members from academia, public hospitals, and industry in time of pandemic. The AI system aims to help radiologists to estimate and communicate the severity of damage in a patient's lung from Chest X-rays. It has been experimentally deployed in the radiology department of the ASST Spedali Civili clinic in Brescia, Italy, since December 2020 during pandemic time. The methodology we have applied for our post-hoc assessment, called Z-Inspection®, uses sociotechnical scenarios to identify ethical, technical, and domain-specific issues in the use of the AI system in the context of the pandemic. © 2020 IEEE.

关键词： COVID-19

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The stability of calibration model in measuring and mapping soil organic matter in a dry climatic area

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IOP Conference Series: Earth and Environmental science 2021年第1期824卷

作者： B H Kusumo M H Idris Sukartono Mulyati L E Susilowati L A A Bakti Baharudin Y A Purwanto Bustan Department of Soil Science Faculty of Agriculture University of Mataram JL. Majapahit 62 Mataram Lombok NTB Indonesia Postgraduate Studies University of Mataram JL. Pendidikan 37 Mataram Lombok NTB Indonesia Forestry Study Program Faculty of Agriculture University of Mataram JL. Pendidikan 37 Mataram Lombok NTB Indonesia Department of Mechanical and Biosystem Engineering IPB University Indonesia

The increase of atmospheric CO2 concentration from soil organic matter (SOM) decomposition may contribute to the global warming and climate change. So, sequestering this greenhouse gas into SOM may be used to mitigate climate change. However, tedious procedures in measuring and mapping SOM need to be replaced with a method which works based on the reliability of calibration model developed. This research aimed to test the reliability of the calibration model that was built from a separate soil sample group to be used to measure and map SOM on other validation soil sample group, in the mostly dry climatic area of Kayangan Sub-district, North Lombok Indonesia. For this purpose, 300 soil samples were collected from the area using grid method, which were then dried, ground, sieved, analysed for SOM content using the Walkley and Black method, and scanned using Near Infrared Spectroscopy. The model built using calibration sample group was able to reliably measure and map the SOM content of the spectral data collected from the validation sample set. This is shown by the coefficient of determination (R2V = 0.79), root mean square error (RMSEV = 0.246%) and the ratio prediction to deviation (RPDV = 2.09). SOM maps generated from both laboratory and near infrared method can show very low, low and medium SOM content. These maps can be further used as a reference for applying organic fertilizers, avoiding excessive use of fertilizers, and monitoring soil carbon sequestration in mitigating climate change.

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BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

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Nature methods 2023年第6期20卷 824-835页

作者： Linus Manubens-Gil Zhi Zhou Hanbo Chen Arvind Ramanathan Xiaoxiao Liu Yufeng Liu Alessandro Bria Todd Gillette Zongcai Ruan Jian Yang Miroslav Radojević Ting Zhao Li Cheng Lei Qu Siqi Liu Kristofer E Bouchard Lin Gu Weidong Cai Shuiwang Ji Badrinath Roysam Ching-Wei Wang Hongchuan Yu Amos Sironi Daniel Maxim Iascone Jie Zhou Erhan Bas Eduardo Conde-Sousa Paulo Aguiar Xiang Li Yujie Li Sumit Nanda Yuan Wang Leila Muresan Pascal Fua Bing Ye Hai-Yan He Jochen F Staiger Manuel Peter Daniel N Cox Michel Simonneau Marcel Oberlaender Gregory Jefferis Kei Ito Paloma Gonzalez-Bellido Jinhyun Kim Edwin Rubel Hollis T Cline Hongkui Zeng Aljoscha Nern Ann-Shyn Chiang Jianhua Yao Jane Roskams Rick Livesey Janine Stevens Tianming Liu Chinh Dang Yike Guo Ning Zhong Georgia Tourassi Sean Hill Michael Hawrylycz Christof Koch Erik Meijering Giorgio A Ascoli Hanchuan Peng Institute for Brain and Intelligence Southeast University Nanjing China. Microsoft Corporation Redmond WA USA. Tencent AI Lab Bellevue WA USA. Computing Environment and Life Sciences Directorate Argonne National Laboratory Lemont IL USA. Kaya Medical Seattle WA USA. University of Cassino and Southern Lazio Cassino Italy. Center for Neural Informatics Structures and Plasticity Krasnow Institute for Advanced Study George Mason University Fairfax VA USA. Faculty of Information Technology Beijing University of Technology Beijing China. Beijing International Collaboration Base on Brain Informatics and Wisdom Services Beijing China. Nuctech Netherlands Rotterdam the Netherlands. Janelia Research Campus Howard Hughes Medical Institute Ashburn VA USA. Department of Electrical and Computer Engineering University of Alberta Edmonton Alberta Canada. Ministry of Education Key Laboratory of Intelligent Computation and Signal Processing Anhui University Hefei China. Paige AI New York NY USA. Scientific Data Division and Biological Systems and Engineering Division Lawrence Berkeley National Lab Berkeley CA USA. Helen Wills Neuroscience Institute and Redwood Center for Theoretical Neuroscience UC Berkeley Berkeley CA USA. RIKEN AIP Tokyo Japan. Research Center for Advanced Science and Technology (RCAST) The University of Tokyo Tokyo Japan. School of Computer Science University of Sydney Sydney New South Wales Australia. Texas A&M University College Station TX USA. Cullen College of Engineering University of Houston Houston TX USA. Graduate Institute of Biomedical Engineering National Taiwan University of Science and Technology Taipei Taiwan. National Centre for Computer Animation Bournemouth University Poole UK. PROPHESEE Paris France. Department of Neuroscience Columbia University New York NY USA. Mortimer B. Zuckerman Mind Brain Behavior Institute Columbia University New York NY USA. Department of Computer Science Northern Illinois Universit

BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is representative of the data obtained in many neuroscience laboratories interested in neuron tracing. Here, we report generated gold standard manual annotations for a subset of the available imaging datasets and quantified tracing quality for 35 automatic tracing algorithms. The goal of generating such a hand-curated diverse dataset is to advance the development of tracing algorithms and enable generalizable benchmarking. Together with image quality features, we pooled the data in an interactive web application that enables users and developers to perform principal component analysis, t-distributed stochastic neighbor embedding, correlation and clustering, visualization of imaging and tracing data, and benchmarking of automatic tracing algorithms in user-defined data subsets. The image quality metrics explain most of the variance in the data, followed by neuromorphological features related to neuron size. We observed that diverse algorithms can provide complementary information to obtain accurate results and developed a method to iteratively combine methods and generate consensus reconstructions. The consensus trees obtained provide estimates of the neuron structure ground truth that typically outperform single algorithms in noisy datasets. However, specific algorithms may outperform the consensus tree strategy in specific imaging conditions. Finally, to aid users in predicting the most accurate automatic tracing results without manual annotations for comparison, we used support vector machine regression to predict reconstruction quality given an image volume and a set of automatic tracings.

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Retraction notice to "A deep learning approach based on graphs to detect plantation lines" [Heliyon Volume 10, Issue 11, 15 June 2024, e31730]

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Heliyon 2024年第23期10卷 e40689页

作者： Diogo Nunes Gonçalves José Marcato Junior Mauro Dos Santos de Arruda Vanessa Jordão Marcato Fernandes Ana Paula Marques Ramos Danielle Elis Garcia Furuya Lucas Prado Osco Hongjie He Lucio André de Castro Jorge Jonathan Li Farid Melgani Hemerson Pistori Wesley Nunes Gonçalves Faculty of Computer Science Federal University of Mato Grosso do Sul Av. Costa e Silva Campo Grande 79070-900 MS Brazil. Faculty of Engineering Architecture and Urbanism and Geography Federal University of Mato Grosso do Sul Av. Costa e Silva Campo Grande 79070-900 MS Brazil. Faculty of Agricultural Sciences Federal University of Grande Dourados Av. Costa e Silva Dourados 79070-900 MS Brazil. Faculty of Science and Technology São Paulo State University (UNESP) R. Roberto Simonsen 305 Presidente Prudente 19060-900 SP Brazil. Program of Environment and Regional Developement University of Western São Paulo Raposo Tavares km 572 Presidente Prudente 19067-175 SP Brazil. Department of Geography and Environmental Management University of Waterloo Waterloo ON N2L 3G1 Canada. National Research Center of Development of Agricultural Instrumentation Brazilian Agricultural Research Agency (EMBRAPA) 13560-970 R. XV de Novembro 1452 São Carlos SP Brazil. Department of Information Engineering and Computer Science University of Trento Trento 38122 Italy. INOVISAO Dom Bosco Catholic University Avenida Tamandaré 6000 Campo Grande 79117-900 MS Brazil.

[This retracts the article DOI: 10.1016/***.2024.e31730.].

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A deep learning approach based on graphs to detect plantation lines

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Heliyon 2024年第11期10卷 e31730页

Identifying plantation lines in aerial images of agricultural landscapes is re-quired for many automatic farming processes. Deep learning-based networks are among the most prominent methods to learn such patterns and extract this type of information from diverse imagery conditions. However, even state-of-the-art methods may stumble in complex plantation patterns. Here, we propose a deep learning approach based on graphs to detect plantation lines in UAV-based RGB imagery, presenting a challenging scenario containing spaced plants. The first module of our method extracts a feature map throughout the backbone, which consists of the initial layers of the VGG16. This feature map is used as an input to the Knowledge Estimation Module (KEM), organized in three concatenated branches for detecting 1) the plant positions, 2) the plantation lines, and 3) the displacement vectors between the plants. A graph modeling is applied considering each plant position on the image as vertices, and edges are formed between two vertices (i.e. plants). Finally, the edge is classified as pertaining to a certain plantation line based on three probabilities (higher than 0.5): i) in visual features obtained from the backbone; ii) a chance that the edge pixels belong to a line, from the KEM step; and iii) an alignment of the displacement vectors with the edge, also from the KEM step. Experiments were conducted initially in corn plantations with different growth stages and patterns with aerial RGB imagery to present the advantages of adopting each module. We assessed the generalization capability in the other two cultures (orange and eucalyptus) datasets. The proposed method was compared against state-of-the-art deep learning methods and achieved superior performance with a significant margin considering all three datasets. This approach is useful in extracting lines with spaced plantation patterns and could be implemented in scenarios where plantation gaps occur, generating lines with few-to-no

关键词： Aerial imagery Convolutional neural networks Object detection Remote sensing UAV

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Weather prediction system and recommendation of plant varieties as an effort to minimize harvest failure with android-based Backpropagation Artificial Neural Networks

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IOP Conference Series: Materials science and engineering 2021年第3期1098卷

作者： M Ahsan W Setiyaningsih B Rinanto M Susilowati I Sulistiyowati Informatics Study Program Faculty of Science and Technology Universitas Kanjuruhan Malang Jl. S. Supriadi No. 48 Malang Jawa Timur Indonesia Program Studi Sistem Informasi Fakultas Sain dan Teknologi Universitas Ma Chung Malang Jl. Villa PuncakTidar Blok N No. 1 Dau Malang Jawa Timur Indonesia Electrical Engineering Department Universitas Muhammadiyah Sidoarjo

Based on Malang Regency agriculture data in 2017 presented by the Head of the Agriculture and Food Crops and Horticulture Office (DPTPH), there was a 2.5% decrease in yields. One hectare of paddy land can produce 7.71 tons during normal weather, but currently only produces 6.9 tons per hectare. Three farmers' groups in Malang Regency (2018), are of the opinion that the current weather greatly influences crop failure. Weather prediction used so far using conventional benchmarks for planting, is no longer relevant to current reality. These estimates are often incorrect, resulting in crop failures, which have an impact on the farmer's economy. This study aims to make the application of weather prediction systems and recommendations for suitable planting varieties based on data on temperature, humidity, time of sun exposure, wind speed, and rainfall using Backpropagation ANN method. Data used for Karangploso District weather prediction from 2009-2018. While the variables used in the application of Backpropagation ANN: temperature, humidity, time of sun exposure, wind speed, and rainfall. For output, the prediction of rainfall in the next 12 months and planting of varieties in accordance with the predicted rainfall, OPT and land type. From the calculation results of this application the MSE error value of 0.0299 is obtained.

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