Littérature scientifique sur le sujet « Citrus Classification »
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Articles de revues sur le sujet "Citrus Classification"
Yang, Taeyang, et Oh-Sang Kwon. « Sequential Effect on Visual Classification : The Citrus Classification Paradigm ». Journal of Vision 16, no 12 (1 septembre 2016) : 548. http://dx.doi.org/10.1167/16.12.548.
Texte intégralWAKATA, Tadayuki, et Miho SAITO. « Psychological classification of the citrus fragrance. » Proceedings of the Annual Convention of the Japanese Psychological Association 76 (11 septembre 2012) : 1AMA01. http://dx.doi.org/10.4992/pacjpa.76.0_1ama01.
Texte intégralHiri, A., M. De Luca, G. Ioele, A. Balouki, M. Basbassi, F. Kzaiber, A. Oussama et G. Ragno. « Chemometric classification of citrus juices of Moroccan cultivars by infrared spectroscopy ». Czech Journal of Food Sciences 33, No. 2 (3 juin 2016) : 137–42. http://dx.doi.org/10.17221/284/2014-cjfs.
Texte intégralDhiman, Poonam. « Contemporary Study on Citrus Disease Classification System ». ECS Transactions 107, no 1 (24 avril 2022) : 10035–43. http://dx.doi.org/10.1149/10701.10035ecst.
Texte intégralSchaad, Norman W., Elena Postnikova, George Lacy, Aaron Sechler, Irina Agarkova, Paul E. Stromberg, Verlyn K. Stromberg et Anne K. Vidaver. « Emended classification of xanthomonad pathogens on citrus ». Systematic and Applied Microbiology 29, no 8 (décembre 2006) : 690–95. http://dx.doi.org/10.1016/j.syapm.2006.08.001.
Texte intégralSilva, Alessandra F., Ana Paula Barbosa, Célia R. L. Zimback et Paulo M. B. Landim. « Geostatistics and remote sensing methods in the classification of images of areas cultivated with citrus ». Engenharia Agrícola 33, no 6 (décembre 2013) : 1245–56. http://dx.doi.org/10.1590/s0100-69162013000600017.
Texte intégralDorj, Ulzii-Orshikh, Uranbaigal Dejidbal, Hongseok Chae, Lkhagvadorj Batsambuu, Altanchimeg Badarch et Shinebayar Dalkhaa. « CITRUS FRUIT QUALITY CLASSIFICATION BASED ON SIZE USING DIGITAL IMAGE PROCESSING ». Siberian Herald of Agricultural Science 48, no 5 (9 janvier 2019) : 95–101. http://dx.doi.org/10.26898/0370-8799-2018-5-12.
Texte intégralElaraby, Ahmed, Walid Hamdy et Saad Alanazi. « Classification of Citrus Diseases Using Optimization Deep Learning Approach ». Computational Intelligence and Neuroscience 2022 (10 février 2022) : 1–10. http://dx.doi.org/10.1155/2022/9153207.
Texte intégralVarjão, Jonatha Oliveira Reis, Glenda Michele Botelho, Tiago da Silva Almeida, Glêndara Aparecida de Souza Martins et Warley Gramacho da Silva. « Citrus Fruit Quality Classification using Support Vector Machines ». International Journal of Advanced Engineering Research and Science 6, no 7 (2019) : 59–65. http://dx.doi.org/10.22161/ijaers.678.
Texte intégralLee, Saebom, Gyuho Choi, Hyun-Cheol Park et Chang Choi. « Automatic Classification Service System for Citrus Pest Recognition Based on Deep Learning ». Sensors 22, no 22 (18 novembre 2022) : 8911. http://dx.doi.org/10.3390/s22228911.
Texte intégralThèses sur le sujet "Citrus Classification"
Ashari, Ir Sumeru. « Discrimination between citrus genotypes ». Title page, contents and summary only, 1989. http://web4.library.adelaide.edu.au/theses/09A/09aa819.pdf.
Texte intégralLe, Thanh Toan, Trong Ky Vo et Huy Hoang Nguyen. « Evaluation of two eco-friendly botanical extracts on fruit rot pathogens of orange (Citrus sinesis (L.) Osbeck) ». Technische Universität Dresden, 2018. https://tud.qucosa.de/id/qucosa%3A33345.
Texte intégralThối trái bởi Aspergillus niger và Colletotrichum sp. gây ra các thiệt hại nghiêm trọng trên cam. Biện pháp phòng trừ bệnh trên trái cam hiện nay chủ yếu dựa vào thuốc hóa học, dẫn đến tồn dư thuốc trên trái cây, ô nhiễm môi trường và gây độc cho con người. Một trong các phương pháp thay thế giúp giảm sử dụng thuốc hóa học là sử dụng dịch trích thực vật. Nghiên cứu này đã được thưc hiện để đánh giá hiệu quả in vivo của dịch trích ở nồng độ 6% của neem hoặc lược vàng đối với A. niger và Colletotrichum sp. Các trái cam đã lây nhiễm nhân tạo tác nhân gây thối trái thì được nhúng vào dịch trích ở nồng độ 6% của neem hoặc lược vàng trong 30 giây, và giữ đến 11 ngày để ghi nhận chiều dài vết bệnh ở nhiệt độ phòng. Cái trái cam được nhúng vào nước cất thì dùng như nghiệm thức đối chứng. Kết quả cho thấy ở 11 ngày sau khi chủng bệnh, dịch trích neem và lược vàng làm giảm đáng kể vết thối Aspergillus lần lượt là 109,08 và 124,00 mm. Bên cạnh đó, vết bệnh thán thư trên trái cam đã bị ức chế có ý nghĩa thống kê bởi các dịch trích neem và lược vàng, với đường kính trung bình các vết bệnh lần lượt là 160,00 và 154,75 mm, ở ngày 11 của thí nghiệm. Kết quả của nghiên cứu này đã chỉ ra rằng dịch trích neem và lược vàng ở nồng độ 6% có thể sử dụng như một biện pháp thay thế tự nhiên trong việc phòng trừ sự phát triển của tác nhân gây thối trái cam. Các loại dịch trích này có tương lai trong bảo vệ thực vật hiện đại, thay thế các loại thuốc hóa học tổng hợp truyền thống trong hệ sinh thái nông nghiệp.
von, Suffrin Dana. « Irit Amit-Cohen : Zionism and Free Enterprise. The Story of Private Entrepreneurs in Citrus Plantations in Palestine in the 1920s and 1930s ». HATiKVA e.V. – Die Hoffnung Bildungs- und Begegnungsstätte für Jüdische Geschichte und Kultur Sachsen, 2014. https://slub.qucosa.de/id/qucosa%3A35090.
Texte intégralSaldivar-Sali, Artessa Niccola D. 1980. « A global typology of cities : classification tree analysis of urban resource consumption ». Thesis, Massachusetts Institute of Technology, 2010. http://hdl.handle.net/1721.1/61558.
Texte intégralCataloged from PDF version of thesis.
Includes bibliographical references (p. 101-103).
A study was carried out to develop a typology of urban metabolic (or resource consumption) profiles for 155 globally representative cities. Classification tree analysis was used to develop a model for determining how certain predictor (or independent) variables are related to levels of resource consumption. These predictor variables are: climate, city GDP, population, and population density. Classification trees and their corresponding decision rules were produced for the following major categories of material and energy resources: Total Energy, Electricity, Fossil fuels, Industrial Minerals & Ores, Construction Minerals, Biomass, Water, and Total Domestic Material Consumption. A tree was also generated for carbon dioxide emissions. Data at the city level was insufficient to include municipal solid waste generation in the analysis. Beyond just providing insight into the effects of the predictor variables on the consumption of different types of resources, the classification trees can also be used to predict consumption levels for cities that were not used in the model training data set. Urban metabolic profiles were also developed for each of the 155 cities, resulting in 15 metabolic types containing cities with identical or almost identical levels of consumption for all of the 8 resources and identical levels of carbon dioxide emissions. The important drivers of the differences in profile for each type include the dominant industries in the cities, as well as the presence of abundant natural resources in the countries in which the cities are the main economic centers.
by Artessa Niccola D. Saldivar-Sali.
S.M.
Alsouda, Yasser. « An IoT Solution for Urban Noise Identification in Smart Cities : Noise Measurement and Classification ». Thesis, Linnéuniversitetet, Institutionen för fysik och elektroteknik (IFE), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-80858.
Texte intégralMokrenko, Valeria Igorevna. « Machine Learning Enabled Surface Classification and Knowledge Transfer for Accessible Route Generation for Wheelchair Users ». Miami University / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=miami1596030215568784.
Texte intégralYang, Shiqi [Verfasser], Andreas [Akademischer Betreuer] Matzarakis et Rüdiger [Akademischer Betreuer] Glaser. « Analysis and evaluation of human thermal comfort conditions for Chinese cities, based on updated Köppen-Geiger classification ». Freiburg : Universität, 2017. http://d-nb.info/1136567194/34.
Texte intégralLuus, Martin. « Economic specialisation and diversity in South African cities / by Martin Luus ». Thesis, North-West University, 2005. http://hdl.handle.net/10394/803.
Texte intégralThesis (M.Com. (Economics))--North-West University, Potchefstroom Campus, 2006.
HUANG, KUAN-YU. « Fractal or Scaling Analysis of Natural Cities Extracted from Open Geographic Data Sources ». Thesis, Högskolan i Gävle, Avdelningen för Industriell utveckling, IT och Samhällsbyggnad, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-19386.
Texte intégralPapsdorf, Christian. « Chemnitzer Internet- und Techniksoziologie (CITS) : Working Papers ». Technische Universität Chemnitz, 2016. https://monarch.qucosa.de/id/qucosa%3A20442.
Texte intégralThe Working Paper Series „Chemnitz Sociology of the Internet and Technology“ focusses on current research issues in de realm of Internet studies and Sociology of Technology. Both theoretical and empirical contributions in the analysis of current technology use, technology development and computer-mediated communication are published. The focus is particularly on the the relationship between humans and technology while using qualitative social research methods.
Livres sur le sujet "Citrus Classification"
Massachusetts. Dept. of Education. A New classification scheme for communities in Massachusetts. [Quincy, Mass.] : Massachusetts Dept. of Education, 1985.
Trouver le texte intégralWilson Sampaio de Azevedo Filho. Cigarrinhas de citros no Rio Grande do Sul : Taxonomia. Porto Alegre : EDIPUCRS, 2006.
Trouver le texte intégralBasṭ, Salīm. Dalīl al-taṣnīf al-ʻashrī lil-mudun wa-al-qurá al-Filasṭīnīyah. al-Quds : Jamʻīyat al-Dirāsāt al-ʻArabīyah, Markaz al-Tawthīq wa-al-Maʻlūmāt, 1993.
Trouver le texte intégralSzymańska, Daniela. Problemy klasyfikacji i typologii miast w geografii radzieckiej = : The classification and the typology of cities in Soviet Union geography. Toruń : TNT, 1989.
Trouver le texte intégralK, Jain M. Functional classification of urban agglomerations/towns of India, 1991. New Delhi : Social Studies Division, Office of the Registrar General, India, Ministry of Home Affairs, 1994.
Trouver le texte intégralMukherji, Shekhar. Functional classification of Indian towns by factor-cluster method, 1981 and 1991. Bombay, India : International Institute for Population Sciences, 1994.
Trouver le texte intégralWilson Sampaio de Azevedo Filho. Guia para coleta & identificação de cigarrinhas em pomares de citros no Rio Grande do Sul. Porto Alegre : EDIPUCRS, 2004.
Trouver le texte intégralWilson Sampaio de Azevedo Filho. Guia para coleta & identificação de cigarrinhas em pomares de citros no Rio Grande do Sul. Porto Alegre : EDIPUCRS, 2004.
Trouver le texte intégralWilson Sampaio de Azevedo Filho. Guia para coleta & identificação de cigarrinhas em pomares de citros no Rio Grande do Sul. Porto Alegre : EDIPUCRS, 2004.
Trouver le texte intégralTōkeikyoku, Japan Sōmuchō. Toshi bunrui. Tōkyō : Nihon Tōkei Kyōkai, 1990.
Trouver le texte intégralChapitres de livres sur le sujet "Citrus Classification"
Kato, Shigeru, Tomomichi Kagawa, Naoki Wada, Takanori Hino et Hajime Nobuhara. « Citrus Brand Classification by CNN Considering Load and Sound ». Dans Advances in Intelligent Systems and Computing, 1239–49. Cham : Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-44038-1_113.
Texte intégralLopez, Jose J., Emanuel Aguilera et Maximo Cobos. « Defect Detection and Classification in Citrus Using Computer Vision ». Dans Neural Information Processing, 11–18. Berlin, Heidelberg : Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-10684-2_2.
Texte intégralTorrens, Francisco, et Gloria Castellano. « Classification of Citrus : Principal Components, Cluster, and Meta-Analyses ». Dans Applied Physical Chemistry with Multidisciplinary Approaches, 217–34. Toronto : Apple Academic Press, 2018. | Series : Innovations in physical chemistry. Monograph series : Apple Academic Press, 2018. http://dx.doi.org/10.1201/9781315169415-9.
Texte intégralNegi, Alok, et Krishan Kumar. « Classification and Detection of Citrus Diseases Using Deep Learning ». Dans Data Science and Its Applications, 63–85. Boca Raton : Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781003102380-4.
Texte intégralSingh, Harpreet, Rajneesh Rani et Shilpa Mahajan. « Detection and Classification of Citrus Leaf Disease Using Hybrid Features ». Dans Advances in Intelligent Systems and Computing, 737–45. Singapore : Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-0751-9_67.
Texte intégralSharma, Parul, et Pawanesh Abrol. « Analysis of Multiple Component Based CNN for Similar Citrus Species Classification ». Dans Studies in Computational Intelligence, 221–32. Cham : Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-96634-8_20.
Texte intégralSenthilkumar, C., et M. Kamarasan. « An Effective Kapur’s Segmentation Based Detection and Classification Model for Citrus Diseases Diagnosis System ». Dans Proceeding of the International Conference on Computer Networks, Big Data and IoT (ICCBI - 2019), 232–39. Cham : Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-43192-1_26.
Texte intégralRoy, Kyamelia, Sheli Sinha Chaudhuri, Soumi Bhattacharjee et Srijita Manna. « Classification of Citrus Fruits and Prediction of Their Largest Producer Based on Deep Learning Architectures ». Dans Advances in Smart Communication Technology and Information Processing, 147–55. Singapore : Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-9433-5_15.
Texte intégralSayed, Gehad Ismail, Aboul Ella Hassanien et Mincong Tang. « A Novel Optimized Convolutional Neural Network Based on Marine Predators Algorithm for Citrus Fruit Quality Classification ». Dans Lecture Notes in Operations Research, 682–92. Singapore : Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8656-6_60.
Texte intégralIbrahim, Israa Saeed, et Furkan Rabee. « Smart Cities Population Classification Using Hadoop MapReduce ». Dans Proceedings of Third Doctoral Symposium on Computational Intelligence, 165–79. Singapore : Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-3148-2_14.
Texte intégralActes de conférences sur le sujet "Citrus Classification"
Arivazhagan, S., R. Newlin Shebiah, S. Selva Nidhyanandhan et L. Ganesan. « Classification of citrus and non-citrus fruits using texture features ». Dans 2010 International Conference on Computing, Communication and Networking Technologies (ICCCNT'10). IEEE, 2010. http://dx.doi.org/10.1109/icccnt.2010.5591562.
Texte intégralMiller, William M. « Automated Inspection/Classification of Fruits and Vegetables ». Dans ASME 1987 Citrus Engineering Conference. American Society of Mechanical Engineers, 1987. http://dx.doi.org/10.1115/cec1987-3305.
Texte intégralNuno-Maganda, Marco Aurelio, Yahir Hernandez-Mier, Cesar Torres-Huitzil et Josue Jimenez-Arteaga. « FPGA-based real-time citrus classification system ». Dans 2014 IEEE 5th Latin American Symposium on Circuits and Systems (LASCAS). IEEE, 2014. http://dx.doi.org/10.1109/lascas.2014.6820292.
Texte intégralKhan, Ejaz, Muhammad Zia Ur Rehman, Fawad Ahmed et Muhammad Attique Khan. « Classification of Diseases in Citrus Fruits using SqueezeNet ». Dans 2021 International Conference on Applied and Engineering Mathematics (ICAEM). IEEE, 2021. http://dx.doi.org/10.1109/icaem53552.2021.9547133.
Texte intégralSudharshan Duth, P., et Shreeharsha Gopalkrishna Bhat. « Disease Classification in Citrus Leaf using Deep Learning ». Dans 2022 IEEE International Conference on Data Science and Information System (ICDSIS). IEEE, 2022. http://dx.doi.org/10.1109/icdsis55133.2022.9915847.
Texte intégralKawashita Kobayashi, Felipe, Andrea Britto Mattos, Bruno H. Gemignani et Maysa M. G. Macedo. « Experimental Analysis of Citrus Tree Classification from UAV Images ». Dans 2019 IEEE International Symposium on Multimedia (ISM). IEEE, 2019. http://dx.doi.org/10.1109/ism46123.2019.00014.
Texte intégralSaini, Ashok Kumar, Roheet Bhatnagar et Devesh Kumar Srivastava. « Citrus Fruits Diseases Detection and Classification Using Transfer Learning ». Dans DSMLAI '21' : International Conference on Data Science, Machine Learning and Artificial Intelligence. New York, NY, USA : ACM, 2021. http://dx.doi.org/10.1145/3484824.3484893.
Texte intégralJianwei Qin, Thomas F Burks, Dae Gwan Kim et Duke M Bulanon. « Classification of Citrus Peel Diseases Using Color Texture Feature Analysis ». Dans Food Processing Automation Conference Proceedings, 28-29 June 2008, Providence, Rhode Island. St. Joseph, MI : American Society of Agricultural and Biological Engineers, 2008. http://dx.doi.org/10.13031/2013.24555.
Texte intégralKobayashi, Felipe Kawashita, Andrea Britto Mattos, Maysa M. G. Macedo et Bruno H. Gemignani. « Citrus Tree Classification from UAV Images : Analysis and Experimental Results ». Dans XV Workshop de Visão Computacional. Sociedade Brasileira de Computação - SBC, 2019. http://dx.doi.org/10.5753/wvc.2019.7624.
Texte intégralDang-Ngoc, Hanh, Trang N. M. Cao et Chau Dang-Nguyen. « Citrus Leaf Disease Detection and Classification Using Hierarchical Support Vector Machine ». Dans 2021 International Symposium on Electrical and Electronics Engineering (ISEE). IEEE, 2021. http://dx.doi.org/10.1109/isee51682.2021.9418680.
Texte intégralRapports d'organisations sur le sujet "Citrus Classification"
Lee, W. S., Victor Alchanatis et Asher Levi. Innovative yield mapping system using hyperspectral and thermal imaging for precision tree crop management. United States Department of Agriculture, janvier 2014. http://dx.doi.org/10.32747/2014.7598158.bard.
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