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Bibliografias temáticas / Citrus Classification

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Literatura científica selecionada sobre o tema "Citrus Classification"

Autor: Grafiati

Publicado: 4 de junho de 2021

Última modificação: 28 de janeiro de 2023

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Artigos de revistas sobre o assunto "Citrus Classification"

1

Yang, Taeyang, e Oh-Sang Kwon. "Sequential Effect on Visual Classification: The Citrus Classification Paradigm". Journal of Vision 16, n.º 12 (1 de setembro de 2016): 548. http://dx.doi.org/10.1167/16.12.548.

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

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3

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

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4

Dhiman, Poonam. "Contemporary Study on Citrus Disease Classification System". ECS Transactions 107, n.º 1 (24 de abril de 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, different approaches are applied for grading the citrus fruits using machine vision system. The paper presents the overview of different techniques like pre-processing, segmentation, and classification that is used by the disease detection system of citrus fruits. This paper also presents the detailed description of the different state of art disease detection system proposed by the researcher for attending identifying the disease present in citrus fruit. The detailed survey of the disease detection technique present in citrus fruit has been presented to investigate the usage of recent approaches employed in machine vision systems.

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5

Schaad, Norman W., Elena Postnikova, George Lacy, Aaron Sechler, Irina Agarkova, Paul E. Stromberg, Verlyn K. Stromberg e Anne K. Vidaver. "Emended classification of xanthomonad pathogens on citrus". Systematic and Applied Microbiology 29, n.º 8 (dezembro de 2006): 690–95. http://dx.doi.org/10.1016/j.syapm.2006.08.001.

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6

Silva, Alessandra F., Ana Paula Barbosa, Célia R. L. Zimback e Paulo M. B. Landim. "Geostatistics and remote sensing methods in the classification of images of areas cultivated with citrus". Engenharia Agrícola 33, n.º 6 (dezembro de 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 classification taken as field checking.. Reliability of classifications was evaluated by Kappa index. In accordance with the Kappa index, the Indicator kriging method obtained the highest degree of reliability for bands 2 and 4. Moreover the Cluster method applied to band 2 (green) was the best quality classification between all the methods. Indicator Kriging was the classifier that presented the citrus total area closest to the field check estimated by -3.01%, whereas Maxver overestimated the total citrus area by 42.94%.

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7

Dorj, Ulzii-Orshikh, Uranbaigal Dejidbal, Hongseok Chae, Lkhagvadorj Batsambuu, Altanchimeg Badarch e Shinebayar Dalkhaa. "CITRUS FRUIT QUALITY CLASSIFICATION BASED ON SIZE USING DIGITAL IMAGE PROCESSING". Siberian Herald of Agricultural Science 48, n.º 5 (9 de janeiro de 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 were compared with the results of the survey research into citrus fruit size as conducted by the Jeju Citrus Commission. Comparative results reveal that the image of the citrus fruit diameter in pixels demonstrate a more accurate size than the other two pixel values, i.e. perimeter and area.

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8

Elaraby, Ahmed, Walid Hamdy e Saad Alanazi. "Classification of Citrus Diseases Using Optimization Deep Learning Approach". Computational Intelligence and Neuroscience 2022 (10 de fevereiro de 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 considered to be plant diseases, which results in financial losses. Citrus is an important source of nutrients such as vitamin C all around the world. On the contrary, citrus diseases have a negative impact on the citrus fruit and quality. In the recent decade, computer vision and image processing techniques have become increasingly popular for the detection and classification of plant diseases. The suggested approach is evaluated on the citrus disease image gallery dataset and the combined dataset (citrus image datasets of infested scale and plant village). These datasets were used to identify and classify citrus diseases such as anthracnose, black spot, canker, scab, greening, and melanose. AlexNet and VGG19 are two kinds of convolutional neural networks that were used to build and test the proposed approach. The system’s total performance reached 94% at its best. The proposed approach outperforms the existing methods.

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9

Varjão, Jonatha Oliveira Reis, Glenda Michele Botelho, Tiago da Silva Almeida, Glêndara Aparecida de Souza Martins e Warley Gramacho da Silva. "Citrus Fruit Quality Classification using Support Vector Machines". International Journal of Advanced Engineering Research and Science 6, n.º 7 (2019): 59–65. http://dx.doi.org/10.22161/ijaers.678.

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10

Lee, Saebom, Gyuho Choi, Hyun-Cheol Park e Chang Choi. "Automatic Classification Service System for Citrus Pest Recognition Based on Deep Learning". Sensors 22, n.º 22 (18 de novembro de 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 early and accurate detection of citrus diseases, which are vulnerable to pests, is very important to prevent the spread of pests and reduce crop damage. Research on citrus pest disease is ongoing, but it is difficult to apply research results to cultivation owing to a lack of datasets for research and limited types of pests. In this study, we built a dataset by self-collecting a total of 20,000 citrus pest images, including fruits and leaves, from actual cultivation sites. The constructed dataset was trained, verified, and tested using a model that had undergone five transfer learning steps. All models used in the experiment had an average accuracy of 97% or more and an average f1 score of 96% or more. We built a web application server using the EfficientNet-b0 model, which exhibited the best performance among the five learning models. The built web application tested citrus pest disease using image samples collected from websites other than the self-collected image samples and prepared data, and both samples correctly classified the disease. The citrus pest automatic diagnosis web system using the model proposed in this study plays a useful auxiliary role in recognizing and classifying citrus diseases. This can, in turn, help improve the overall quality of citrus fruits.

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Teses / dissertações sobre o assunto "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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2

Le, Thanh Toan, Trong Ky Vo e 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 pathogens were immersed into leaf extracts of 6% (w/v) neem or basket plants for 30 s, and kept for 11 days to record lesion length at room temperature. Orange fruits immersed into sterile distilled water were used as the control treatment. The results showed that at 11 days after inoculation, extracts of neem and basket plants significantly reduced the Aspergillus rot lesions by 109.08 and 124.00 mm, respectively. In addition, anthracnose lesions on orange fruits were statistically inhibited by treatments of neem and basket plants, with the average lesion diameters approximately 160.00 and 154.75 mm, respectively, at day 11 of the conducting experiment. The results of this study showed that leaf extracts of neem and basket plant at the concentration of 6% could be used as a natural alternative to control the in vivo growth of rot pathogens of orange fruits. These extracts have a bright future in modern plant protection to replace conventional synthetic pesticides in agro-ecosystem.
Thối 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.

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3

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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4

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.
Cataloged 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.

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5

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 calculating the sound pressure level in dB. We achieve a measurement error of less than 1 dB. Our machine learning-based method for noise classification uses Mel-frequency cepstral coefficients for audio feature extraction and four supervised classification algorithms (that is, support vector machine, k-nearest neighbors, bootstrap aggregating, and random forest). We evaluate our approach experimentally with a dataset of about 3000 sound samples grouped in eight sound classes (such as car horn, jackhammer, or street music). We explore the parameter space of the four algorithms to estimate the optimal parameter values for the classification of sound samples in the dataset under study. We achieve noise classification accuracy in the range of 88% – 94%.

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6

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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7

Yang, Shiqi [Verfasser], Andreas [Akademischer Betreuer] Matzarakis e 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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8

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 at the specialization and diversity of South African cities. The aim is to determine whether certain cities should specialise in certain sectors, which they are currently involved in or should they add to their city and become more diverse and specialize in other sectors in order to promote economic growth. Many believe that a city which is more diverse would grow faster than a city specialising in a certain and thus be more beneficial to the economy than a specialized city would. This paper would like to address this phenomenon with regard to South African cities
Thesis (M.Com. (Economics))--North-West University, Potchefstroom Campus, 2006.

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9

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 many disciplines that analyze changes in the Earth’s surface, such as urban planning, detection of melting ice, and deforestation management. Moreover, these disciplines can take advantage of remote sensing for research. This study not only uses satellite imaging to analyze urban areas but also uses check-in and points of interest (POI) data. It uses straightforward means to observe an urban environment using the bottom-up approach and measure its complexity using fractal geometry. Web 2.0, which has many volunteers who share their information on different platforms, was one of the most important tools in this study. We can easily obtain rough data from various platforms such as the Stanford Large Network Dataset Collection (SLNDC), the Earth Observation Group (EOG), and CloudMade. The check-in data in this thesis were downloaded from SLNDC, the POI data were obtained from CloudMade, and the nighttime lights imaging data were collected from EOG. In this study, we used these three types of data to derive natural cities representing city regions using a bottom-up approach. Natural cities were derived from open geographic data without human manipulation. After refining data, we used rough data to derive natural cities. This study used a triangulated irregular network to derive natural cities from check-in and POI data. In this study, we focus on the four largest US natural cities regions: Chicago, New York, San Francisco, and Los Angeles. The result is that the New York City region is the most complex area in the United States. Box-counting fractal dimension, lacunarity, and ht-index (head/tail breaks index) can be used to explain this. Box-counting fractal dimension is used to represent the New York City region as the most prosperous of the four city regions. Lacunarity indicates the New York City region as the most compact area in the United States. Ht-index shows the New York City region having the highest hierarchy of the four city regions. This conforms to central place theory: higher-level cities have better service than lower-level cities. In addition, ht-index cannot represent hierarchy clearly when data distribution does not fit a long-tail distribution exactly. However, the ht-index is the only method that can analyze the complexity of natural cities without using images.

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10

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.
The 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.

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Mais fontes

Livros sobre o assunto "Citrus Classification"

1

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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

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Capítulos de livros sobre o assunto "Citrus Classification"

1

Kato, Shigeru, Tomomichi Kagawa, Naoki Wada, Takanori Hino e 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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2

Lopez, Jose J., Emanuel Aguilera e 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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3

Torrens, Francisco, e 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, e 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 e 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, e 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., e 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 e 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 e 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, e 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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Trabalhos de conferências sobre o assunto "Citrus Classification"

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Arivazhagan, S., R. Newlin Shebiah, S. Selva Nidhyanandhan e 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 packing plant. Furthermore, the cullage removal rates will probably increase with further mechanization of harvesting and field handling. Paper published with permission.

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Nuno-Maganda, Marco Aurelio, Yahir Hernandez-Mier, Cesar Torres-Huitzil e 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 e 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., e 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 e 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 e 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 e 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 e 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-based methods allow to achieve high accuracy values for classiﬁcation, explainability remains an issue. Therefore, our approach is to run an experimental analysis that allows to derive the effects of different parametrizations (involving descriptors, classiﬁers, and sampling methods) when applied to our citrus dataset.

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Dang-Ngoc, Hanh, Trang N. M. Cao e 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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Relatórios de organizações sobre o assunto "Citrus Classification"

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Lee, W. S., Victor Alchanatis e Asher Levi. Innovative yield mapping system using hyperspectral and thermal imaging for precision tree crop management. United States Department of Agriculture, janeiro de 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 major revisions in the overall objective, however, several revisions were made on the specific objectives. The revised specific objectives were: (1) to develop a yield mapping system for a static situation, using color and thermal imaging independently, (2) to integrate color and thermal imaging for improved yield estimation by combining thermal images with color images to improve fruit detection, and (3) to expand the system to an autonomous mobile platform for a continuous-measure situation. Background, major conclusions, solutions and achievements -- Yield mapping is considered as an initial step for applying precision agriculture technologies. Although many yield mapping systems have been developed for agronomic crops, it remains a difficult task for mapping yield of tree crops. In this project, an autonomous immature fruit yield mapping system was developed. The system could detect and count the number of fruit at early growth stages of citrus fruit so that farmers could apply site-specific management based on the maps. There were two sub-systems, a navigation system and an imaging system. Robot Operating System (ROS) was the backbone for developing the navigation system using an unmanned ground vehicle (UGV). An inertial measurement unit (IMU), wheel encoders and a GPS were integrated using an extended Kalman filter to provide reliable and accurate localization information. A LiDAR was added to support simultaneous localization and mapping (SLAM) algorithms. The color camera on a Microsoft Kinect was used to detect citrus trees and a new machine vision algorithm was developed to enable autonomous navigations in the citrus grove. A multimodal imaging system, which consisted of two color cameras and a thermal camera, was carried by the vehicle for video acquisitions. A novel image registration method was developed for combining color and thermal images and matching fruit in both images which achieved pixel-level accuracy. A new Color- Thermal Combined Probability (CTCP) algorithm was created to effectively fuse information from the color and thermal images to classify potential image regions into fruit and non-fruit classes. Algorithms were also developed to integrate image registration, information fusion and fruit classification and detection into a single step for real-time processing. The imaging system achieved a precision rate of 95.5% and a recall rate of 90.4% on immature green citrus fruit detection which was a great improvement compared to previous studies. Implications – The development of the immature green fruit yield mapping system will help farmers make early decisions for planning operations and marketing so high yield and profit can be achieved.

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