Bibliografías temáticas / Citrus Classification

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Literatura académica sobre el tema "Citrus Classification"

Autor: Grafiati
Publicado: 4 de junio de 2021
Última modificación: 28 de enero de 2023

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Artículos de revistas sobre el tema "Citrus Classification"

1

Yang, Taeyang, and Oh-Sang Kwon. "Sequential Effect on Visual Classification: The Citrus Classification Paradigm." Journal of Vision 16, no. 12 (September 1, 2016): 548. http://dx.doi.org/10.1167/16.12.548.

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WAKATA, Tadayuki, and Miho SAITO. "Psychological classification of the citrus fragrance." Proceedings of the Annual Convention of the Japanese Psychological Association 76 (September 11, 2012): 1AMA01. http://dx.doi.org/10.4992/pacjpa.76.0_1ama01.

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Hiri, A., M. De Luca, G. Ioele, A. Balouki, M. Basbassi, F. Kzaiber, A. Oussama, and G. Ragno. "Chemometric classification of citrus juices of Moroccan cultivars by infrared spectroscopy." Czech Journal of Food Sciences 33, No. 2 (June 3, 2016): 137–42. http://dx.doi.org/10.17221/284/2014-cjfs.

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Dhiman, Poonam. "Contemporary Study on Citrus Disease Classification System." ECS Transactions 107, no. 1 (April 24, 2022): 10035–43. http://dx.doi.org/10.1149/10701.10035ecst.

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Machine vision systems enable many applications in all important fields of life like medical healthcare, agriculture, fruit and vegetable industry, etc. One of the application fields is disease detection of fruit. The disease identification of fruits is a critical issue and advanced automatic detection systems need to be developed. In the recent years, image processing techniques have been employed for the quality evaluation of the fruits. This paper presents the current advancement in image processing techniques used by the disease recognition system of the citrus fruits. In past few years, d
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Schaad, Norman W., Elena Postnikova, George Lacy, Aaron Sechler, Irina Agarkova, Paul E. Stromberg, Verlyn K. Stromberg, and Anne K. Vidaver. "Emended classification of xanthomonad pathogens on citrus." Systematic and Applied Microbiology 29, no. 8 (December 2006): 690–95. http://dx.doi.org/10.1016/j.syapm.2006.08.001.

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Silva, Alessandra F., Ana Paula Barbosa, Célia R. L. Zimback, and Paulo M. B. Landim. "Geostatistics and remote sensing methods in the classification of images of areas cultivated with citrus." Engenharia Agrícola 33, no. 6 (December 2013): 1245–56. http://dx.doi.org/10.1590/s0100-69162013000600017.

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This study compares the precision of three image classification methods, two of remote sensing and one of geostatistics applied to areas cultivated with citrus. The 5,296.52ha area of study is located in the city of Araraquara - central region of the state of São Paulo (SP), Brazil. The multispectral image from the CCD/CBERS-2B satellite was acquired in 2009 and processed through the Geographic Information System (GIS) SPRING. Three classification methods were used, one unsupervised (Cluster), and two supervised (Indicator Kriging/IK and Maximum Likelihood/Maxver), in addition to the screen cl
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Dorj, Ulzii-Orshikh, Uranbaigal Dejidbal, Hongseok Chae, Lkhagvadorj Batsambuu, Altanchimeg Badarch, and Shinebayar Dalkhaa. "CITRUS FRUIT QUALITY CLASSIFICATION BASED ON SIZE USING DIGITAL IMAGE PROCESSING." Siberian Herald of Agricultural Science 48, no. 5 (January 9, 2019): 95–101. http://dx.doi.org/10.26898/0370-8799-2018-5-12.

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A new computer vision algorithm for citrus fruit quality classification based on the size of a single tree fruits was developed in this study. The image properties of area, perimeter, and diameter for the citrus fruits were measured by pixels. In order to estimate citrus fruit size in a realistic manner, the ratios of diameter, perimeter and area in pixel values in relation to the actual size of one fruit were determined. The total of 1860 citrus fruits were grouped based on diameter, perimeter, and area in pixels. The results of the grouping of citrus fruits by diameter, perimeter and area we
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Elaraby, Ahmed, Walid Hamdy, and Saad Alanazi. "Classification of Citrus Diseases Using Optimization Deep Learning Approach." Computational Intelligence and Neuroscience 2022 (February 10, 2022): 1–10. http://dx.doi.org/10.1155/2022/9153207.

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Most plant diseases have apparent signs, and today’s recognized method is for an expert plant pathologist to identify the disease by looking at infected plant leaves using a microscope. The fact is that manually diagnosing diseases is time consuming and that the effectiveness of the diagnosis is related to the pathologist’s talents, making this a great application area for computer-aided diagnostic systems. The proposed work describes an approach for detecting and classifying diseases in citrus plants using deep learning and image processing. The main cause of decreased productivity is conside
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Varjão, Jonatha Oliveira Reis, Glenda Michele Botelho, Tiago da Silva Almeida, Glêndara Aparecida de Souza Martins, and 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.

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Lee, Saebom, Gyuho Choi, Hyun-Cheol Park, and Chang Choi. "Automatic Classification Service System for Citrus Pest Recognition Based on Deep Learning." Sensors 22, no. 22 (November 18, 2022): 8911. http://dx.doi.org/10.3390/s22228911.

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Plant diseases are a major cause of reduction in agricultural output, which leads to severe economic losses and unstable food supply. The citrus plant is an economically important fruit crop grown and produced worldwide. However, citrus plants are easily affected by various factors, such as climate change, pests, and diseases, resulting in reduced yield and quality. Advances in computer vision in recent years have been widely used for plant disease detection and classification, providing opportunities for early disease detection, and resulting in improvements in agriculture. Particularly, the
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Tesis sobre el tema "Citrus Classification"

1

Ashari, Ir Sumeru. "Discrimination between citrus genotypes." Title page, contents and summary only, 1989. http://web4.library.adelaide.edu.au/theses/09A/09aa819.pdf.

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Le, Thanh Toan, Trong Ky Vo, and 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.

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Fruit rot caused by Aspergillus niger and Colletotrichum sp. could cause rapid and severe damage on orange fruits. Current control method of orange fruits is mainly applied by usage of harmful pesticides, leading to chemical residues on fruits, environmental pollution and human poisoning. One of alternative methods of reducing pesticides is to use botanical extracts. This study was conducted to evaluate the in vivo antifungal efficacy of aqueous extracts from the leaves of neem and basket plants against A. niger and Colletotrichum sp. Orange fruits artificially inoculated by fruit rot pathogen
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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.

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Saldivar-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.

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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Architecture, 2010.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (p. 101-103).<br>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
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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.

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Noise is defined as any undesired sound. Urban noise and its effect on citizens area significant environmental problem, and the increasing level of noise has become a critical problem in some cities. Fortunately, noise pollution can be mitigated by better planning of urban areas or controlled by administrative regulations. However, the execution of such actions requires well-established systems for noise monitoring. In this thesis, we present a solution for noise measurement and classification using a low-power and inexpensive IoT unit. To measure the noise level, we implement an algorithm for
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Mokrenko, 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.

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Yang, Shiqi [Verfasser], Andreas [Akademischer Betreuer] Matzarakis, and 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.

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Luus, Martin. "Economic specialisation and diversity in South African cities / by Martin Luus." Thesis, North-West University, 2005. http://hdl.handle.net/10394/803.

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According to Naudé and Krugell (2003a) South Africa's cities are too small, dispersed, and over concentrated. In South Africa, households in the country's urban areas have average incomes almost thrice as high as the households in rural areas. More than 70% of South Africa's GDP is produced in only 19 urban areas (Naudé and Krugell 2003b). In Naudé and Krugell (2003a) it is stated that the rank-size rule shows that South Africa's urban agglomerations are too small and the cities mainly offer urbanization economies rather than localization economies. The main focus of this study will be looking
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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.

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A city consists of many elements such as humans, buildings, and roads. The complexity of cities is difficult to measure using Euclidean geometry. In this study, we use fractal geometry (scaling analysis) to measure the complexity of urban areas. We observe urban development from different perspectives using the bottom-up approach. In a bottom-up approach, we observe an urban region from a basic to higher level from our daily life perspective to an overall view. Furthermore, an urban environment is not constant, but it is complex; cities with greater complexity are more prosperous. There are ma
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Papsdorf, Christian. "Chemnitzer Internet- und Techniksoziologie (CITS) : Working Papers." Technische Universität Chemnitz, 2016. https://monarch.qucosa.de/id/qucosa%3A20442.

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Die Working Papers-Reihe „Chemnitzer Internet- und Techniksoziologie“ widmet sich aktuellen Forschungsfragen aus dem Bereich der Internetforschung und Techniksoziologie. Es werden empirische wie theoretische Beiträge zu unterschiedlichen Aspekten gegenwärtiger Mediennutzung, Technikentwicklung und Internetkommunikation publiziert. Besonders im Fokus steht hierbei das, in der Regel mit Methoden der qualitativen Sozialforschung untersuchte, Verhältnis von Mensch und Technik.<br>The Working Paper Series „Chemnitz Sociology of the Internet and Technology“ focusses on current research issues in de
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Libros sobre el tema "Citrus Classification"

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Massachusetts. Dept. of Education. A New classification scheme for communities in Massachusetts. [Quincy, Mass.]: Massachusetts Dept. of Education, 1985.

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2

Wilson Sampaio de Azevedo Filho. Cigarrinhas de citros no Rio Grande do Sul: Taxonomia. Porto Alegre: EDIPUCRS, 2006.

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Basṭ, 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.

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Szymańska, Daniela. Problemy klasyfikacji i typologii miast w geografii radzieckiej =: The classification and the typology of cities in Soviet Union geography. Toruń: TNT, 1989.

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K, 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.

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Mukherji, Shekhar. Functional classification of Indian towns by factor-cluster method, 1981 and 1991. Bombay, India: International Institute for Population Sciences, 1994.

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Wilson Sampaio de Azevedo Filho. Guia para coleta & identificação de cigarrinhas em pomares de citros no Rio Grande do Sul. Porto Alegre: EDIPUCRS, 2004.

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Wilson Sampaio de Azevedo Filho. Guia para coleta & identificação de cigarrinhas em pomares de citros no Rio Grande do Sul. Porto Alegre: EDIPUCRS, 2004.

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Wilson Sampaio de Azevedo Filho. Guia para coleta & identificação de cigarrinhas em pomares de citros no Rio Grande do Sul. Porto Alegre: EDIPUCRS, 2004.

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Tōkeikyoku, Japan Sōmuchō. Toshi bunrui. Tōkyō: Nihon Tōkei Kyōkai, 1990.

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Capítulos de libros sobre el tema "Citrus Classification"

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Kato, Shigeru, Tomomichi Kagawa, Naoki Wada, Takanori Hino, and Hajime Nobuhara. "Citrus Brand Classification by CNN Considering Load and Sound." In 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.

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Lopez, Jose J., Emanuel Aguilera, and Maximo Cobos. "Defect Detection and Classification in Citrus Using Computer Vision." In Neural Information Processing, 11–18. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-10684-2_2.

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Torrens, Francisco, and Gloria Castellano. "Classification of Citrus: Principal Components, Cluster, and Meta-Analyses." In 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.

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Negi, Alok, and Krishan Kumar. "Classification and Detection of Citrus Diseases Using Deep Learning." In Data Science and Its Applications, 63–85. Boca Raton: Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781003102380-4.

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Singh, Harpreet, Rajneesh Rani, and Shilpa Mahajan. "Detection and Classification of Citrus Leaf Disease Using Hybrid Features." In Advances in Intelligent Systems and Computing, 737–45. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-0751-9_67.

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Sharma, Parul, and Pawanesh Abrol. "Analysis of Multiple Component Based CNN for Similar Citrus Species Classification." In Studies in Computational Intelligence, 221–32. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-96634-8_20.

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Senthilkumar, C., and M. Kamarasan. "An Effective Kapur’s Segmentation Based Detection and Classification Model for Citrus Diseases Diagnosis System." In 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.

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Roy, Kyamelia, Sheli Sinha Chaudhuri, Soumi Bhattacharjee, and Srijita Manna. "Classification of Citrus Fruits and Prediction of Their Largest Producer Based on Deep Learning Architectures." In 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.

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Sayed, Gehad Ismail, Aboul Ella Hassanien, and Mincong Tang. "A Novel Optimized Convolutional Neural Network Based on Marine Predators Algorithm for Citrus Fruit Quality Classification." In Lecture Notes in Operations Research, 682–92. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8656-6_60.

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Ibrahim, Israa Saeed, and Furkan Rabee. "Smart Cities Population Classification Using Hadoop MapReduce." In 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.

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Actas de conferencias sobre el tema "Citrus Classification"

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Arivazhagan, S., R. Newlin Shebiah, S. Selva Nidhyanandhan, and L. Ganesan. "Classification of citrus and non-citrus fruits using texture features." In 2010 International Conference on Computing, Communication and Networking Technologies (ICCCNT'10). IEEE, 2010. http://dx.doi.org/10.1109/icccnt.2010.5591562.

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Miller, William M. "Automated Inspection/Classification of Fruits and Vegetables." In ASME 1987 Citrus Engineering Conference. American Society of Mechanical Engineers, 1987. http://dx.doi.org/10.1115/cec1987-3305.

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Rapid advances in the electronic industry have generated high interest in automated grading technology tor fresh fruits and vegetables. During the last two decades, packaging and container handling have become significantly mechanized. However, sorting remains a labor intensive operation in many fresh produce industries. The amount of fruit removed can be quite significant. In Florida citrus packing, an average of 30% of the fruit is diverted to processing. Such high removal rates coupled with limited grading tables areas can diminish human grading performance and the production capacity of a
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Nuno-Maganda, Marco Aurelio, Yahir Hernandez-Mier, Cesar Torres-Huitzil, and Josue Jimenez-Arteaga. "FPGA-based real-time citrus classification system." In 2014 IEEE 5th Latin American Symposium on Circuits and Systems (LASCAS). IEEE, 2014. http://dx.doi.org/10.1109/lascas.2014.6820292.

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Khan, Ejaz, Muhammad Zia Ur Rehman, Fawad Ahmed, and Muhammad Attique Khan. "Classification of Diseases in Citrus Fruits using SqueezeNet." In 2021 International Conference on Applied and Engineering Mathematics (ICAEM). IEEE, 2021. http://dx.doi.org/10.1109/icaem53552.2021.9547133.

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Sudharshan Duth, P., and Shreeharsha Gopalkrishna Bhat. "Disease Classification in Citrus Leaf using Deep Learning." In 2022 IEEE International Conference on Data Science and Information System (ICDSIS). IEEE, 2022. http://dx.doi.org/10.1109/icdsis55133.2022.9915847.

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Kawashita Kobayashi, Felipe, Andrea Britto Mattos, Bruno H. Gemignani, and Maysa M. G. Macedo. "Experimental Analysis of Citrus Tree Classification from UAV Images." In 2019 IEEE International Symposium on Multimedia (ISM). IEEE, 2019. http://dx.doi.org/10.1109/ism46123.2019.00014.

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Saini, Ashok Kumar, Roheet Bhatnagar, and Devesh Kumar Srivastava. "Citrus Fruits Diseases Detection and Classification Using Transfer Learning." In 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.

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Jianwei Qin, Thomas F Burks, Dae Gwan Kim, and Duke M Bulanon. "Classification of Citrus Peel Diseases Using Color Texture Feature Analysis." In 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.

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Kobayashi, Felipe Kawashita, Andrea Britto Mattos, Maysa M. G. Macedo, and Bruno H. Gemignani. "Citrus Tree Classification from UAV Images: Analysis and Experimental Results." In XV Workshop de Visão Computacional. Sociedade Brasileira de Computação - SBC, 2019. http://dx.doi.org/10.5753/wvc.2019.7624.

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The use of unmanned aerial vehicles (UAVs) and computer vision for automating farm operations is growing rapidly: time-consuming tasks such as crop monitoring may be solved in a more efficient, precise, and less error-prone manner. In particular, for estimating productivity and managing pests, it is fundamental to characterize crop regions into four classes: (i) full-grown trees, (ii) tree seedlings, (iii) tree gaps, and (iv) background. In this paper, we address the classification of images from citrus plantations, acquired by UAVs, into the previously mentioned categories. While Deep learning-
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Dang-Ngoc, Hanh, Trang N. M. Cao, and Chau Dang-Nguyen. "Citrus Leaf Disease Detection and Classification Using Hierarchical Support Vector Machine." In 2021 International Symposium on Electrical and Electronics Engineering (ISEE). IEEE, 2021. http://dx.doi.org/10.1109/isee51682.2021.9418680.

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Informes sobre el tema "Citrus Classification"

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Lee, W. S., Victor Alchanatis, and Asher Levi. Innovative yield mapping system using hyperspectral and thermal imaging for precision tree crop management. United States Department of Agriculture, January 2014. http://dx.doi.org/10.32747/2014.7598158.bard.

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Original objectives and revisions – The original overall objective was to develop, test and validate a prototype yield mapping system for unit area to increase yield and profit for tree crops. Specific objectives were: (1) to develop a yield mapping system for a static situation, using hyperspectral and thermal imaging independently, (2) to integrate hyperspectral and thermal imaging for improved yield estimation by combining thermal images with hyperspectral images to improve fruit detection, and (3) to expand the system to a mobile platform for a stop-measure- and-go situation. There were no
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