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Статті в журналах з теми "Citrus Classification"

Автор: Grafiati

Опубліковано: 4 червня 2021

Оновлено: 28 січня 2023

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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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2

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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3

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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4

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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Анотація:

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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5

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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6

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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7

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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8

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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9

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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10

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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Анотація:

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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11

Mudholakar, Sunita, Kavitha G, Kanaya Kumari K T, and Shubha G V. "Automatic Detection of Citrus Fruit and Leaves Diseases Using Deep Neural Network." International Journal for Research in Applied Science and Engineering Technology 10, no. 7 (July 31, 2022): 4043–51. http://dx.doi.org/10.22214/ijraset.2022.45868.

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Abstract: Citrus fruit diseases are the major cause of extreme citrus fruit yield declines. Plant disease detection and classification are crucial long term agriculture. Manually monitoring citrus diseases is quite tough. As a result, image processing is used for designing an automated detection system for citrus plant diseases. Image acquisition, image preprocessing, image segmentation, feature extraction and classification are main processes in the citrus disease detection process. Deep learning methods have recently obtained promising results in a number of artificial intelligence issues, l

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12

Zia Ur Rehman, Muhammad, Fawad Ahmed, Muhammad Attique Khan, Usman Tariq, Sajjad Shaukat Jamal, Jawad Ahmad, and Iqtadar Hussain. "Classification of Citrus Plant Diseases Using Deep Transfer Learning." Computers, Materials & Continua 70, no. 1 (2022): 1401–17. http://dx.doi.org/10.32604/cmc.2022.019046.

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13

Shou Bo, Huang. "A Climatic Classification for Citrus Winter Survival in China." Journal of Climate 4, no. 5 (May 1991): 550–55. http://dx.doi.org/10.1175/1520-0442(1991)004<0550:accfcw>2.0.co;2.

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14

Steuer, B., H. Schulz, and E. Läger. "Classification and analysis of citrus oils by NIR spectroscopy." Food Chemistry 72, no. 1 (January 2001): 113–17. http://dx.doi.org/10.1016/s0308-8146(00)00209-0.

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15

Mittapelli, Suresh Reddy, Shailendar Kumar Maryada, Venkateswara Rao Khareedu, and Dashavantha Reddy Vudem. "Structural organization, classification and phylogenetic relationship of cytochrome P450 genes in Citrus clementina and Citrus sinensis." Tree Genetics & Genomes 10, no. 2 (January 5, 2014): 399–409. http://dx.doi.org/10.1007/s11295-013-0695-8.

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16

Rauf, Hafiz Tayyab, Basharat Ali Saleem, M. Ikram Ullah Lali, Muhammad Attique Khan, Muhammad Sharif, and Syed Ahmad Chan Bukhari. "A citrus fruits and leaves dataset for detection and classification of citrus diseases through machine learning." Data in Brief 26 (October 2019): 104340. http://dx.doi.org/10.1016/j.dib.2019.104340.

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17

Khanramaki, Morteza, Ezzatollah Askari Asli-Ardeh, and Ehsan Kozegar. "Citrus pests classification using an ensemble of deep learning models." Computers and Electronics in Agriculture 186 (July 2021): 106192. http://dx.doi.org/10.1016/j.compag.2021.106192.

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18

Janarthan, Sivasubramaniam, Selvarajah Thuseethan, Sutharshan Rajasegarar, Qiang Lyu, Yongqiang Zheng, and John Yearwood. "Deep Metric Learning Based Citrus Disease Classification With Sparse Data." IEEE Access 8 (2020): 162588–600. http://dx.doi.org/10.1109/access.2020.3021487.

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19

Lopez, Jose J., Maximo Cobos, and Emanuel Aguilera. "Computer-based detection and classification of flaws in citrus fruits." Neural Computing and Applications 20, no. 7 (June 20, 2010): 975–81. http://dx.doi.org/10.1007/s00521-010-0396-2.

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20

Yanto, Budi, Luth Fimawahib, Asep Supriyanto, B. Herawan Hayadi, and Rinanda Rizki Pratama. "Klasifikasi Tekstur Kematangan Buah Jeruk Manis Berdasarkan Tingkat Kecerahan Warna dengan Metode Deep Learning Convolutional Neural Network." INOVTEK Polbeng - Seri Informatika 6, no. 2 (November 27, 2021): 259. http://dx.doi.org/10.35314/isi.v6i2.2104.

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Анотація:

Sweet orange is very much consumed by humans because oranges are rich in vitamin C, sweet oranges can be consumed directly to drink. The classification carried out to determine proper (good) and unfit (rotten) oranges still uses manual methods, This classification has several weaknesses, namely the existence of human visual limitations, is influenced by the psychological condition of the observations and takes a long time. One of the classification methods for sweet orange fruit with a computerized system the Convolutional Neural Network (CNN) is algorithm deep learning to the development of t

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21

Horibata, Akira, and Tsuneo Kato. "Phylogenetic relationships among accessions in Citrus and related genera based on the insertion polymorphism of the CIRE1 retrotransposon." Open Agriculture 5, no. 1 (June 18, 2020): 243–51. http://dx.doi.org/10.1515/opag-2020-0026.

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AbstractA total of 145 accessions of the genus Citrus and related genera, maintained in the Conservation Garden for Citrus Germplasm at the Experimental Farm of Kindai University, Yuasa, Wakayama, Japan, were examined for their phylogenetic relationships. The present classification was conducted using an inter-retrotransposon amplified polymorphism (IRAP) method based on the insertion polymorphism of a retrotransposon, CIRE1, identified in C. sinensis. The objective of this study was to evaluate the applicability of the IRAP method for citrus classification. The constructed dendrogram showed t

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22

Wang, Xuefeng, Chunyan Wu, and Masayuki Hirafuji. "Visible Light Image-Based Method for Sugar Content Classification of Citrus." PLOS ONE 11, no. 1 (January 26, 2016): e0147419. http://dx.doi.org/10.1371/journal.pone.0147419.

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23

Reinhard, Hans, Fritz Sager, and Otmar Zoller. "Citrus juice classification by SPME-GC-MS and electronic nose measurements." LWT - Food Science and Technology 41, no. 10 (December 2008): 1906–12. http://dx.doi.org/10.1016/j.lwt.2007.11.012.

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24

Qadri, Salman, Syed Furqan Qadri, Mujtaba Husnain, Malik Muhammad Saad Missen, Dost Muhammad Khan, Muzammil-Ul-Rehman, Abdul Razzaq, and Saleem Ullah. "Machine vision approach for classification of citrus leaves using fused features." International Journal of Food Properties 22, no. 1 (January 1, 2019): 2072–89. http://dx.doi.org/10.1080/10942912.2019.1703738.

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25

Amorós López, J., E. Izquierdo Verdiguier, L. Gómez Chova, J. Muñoz Marí, J. Z. Rodríguez Barreiro, G. Camps Valls, and J. Calpe Maravilla. "Land cover classification of VHR airborne images for citrus grove identification." ISPRS Journal of Photogrammetry and Remote Sensing 66, no. 1 (January 2011): 115–23. http://dx.doi.org/10.1016/j.isprsjprs.2010.09.008.

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26

Deng, Xiaoling, Zixiao Huang, Zheng Zheng, Yubin Lan, and Fen Dai. "Field detection and classification of citrus Huanglongbing based on hyperspectral reflectance." Computers and Electronics in Agriculture 167 (December 2019): 105006. http://dx.doi.org/10.1016/j.compag.2019.105006.

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27

Abdulridha, Jaafar, Ozgur Batuman, and Yiannis Ampatzidis. "UAV-Based Remote Sensing Technique to Detect Citrus Canker Disease Utilizing Hyperspectral Imaging and Machine Learning." Remote Sensing 11, no. 11 (June 8, 2019): 1373. http://dx.doi.org/10.3390/rs11111373.

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Анотація:

A remote sensing technique was developed to detect citrus canker in laboratory conditions and was verified in the grove by utilizing an unmanned aerial vehicle (UAV). In the laboratory, a hyperspectral (400–1000 nm) imaging system was utilized for the detection of citrus canker in several disease development stages (i.e., asymptomatic, early, and late symptoms) on Sugar Belle leaves and immature (green) fruit by using two classification methods: (i) radial basis function (RBF) and (ii) K nearest neighbor (KNN). The same imaging system mounted on an UAV was used to detect citrus canker on tree

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28

Yang, Changcai, Zixuan Teng, Caixia Dong, Yaohai Lin, Riqing Chen, and Jian Wang. "In-Field Citrus Disease Classification via Convolutional Neural Network from Smartphone Images." Agriculture 12, no. 9 (September 16, 2022): 1487. http://dx.doi.org/10.3390/agriculture12091487.

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Анотація:

A high-efficiency, nondestructive, rapid, and automatic crop disease classification method is essential for the modernization of agriculture. To more accurately extract and fit citrus disease image features, we designed a new 13-layer convolutional neural network (CNN13) consisting of multiple convolutional layer stacks and dropout in this study. To address the problem created by the uneven number of disease images in each category, we used the VGG16 network module for transfer learning, which we combined with the proposed CNN13 to form a new joint network, which we called OplusVNet. To verify

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29

Petretto, Giacomo Luigi, Maria Enrica Di Pietro, Marzia Piroddi, Giorgio Pintore, and Alberto Mannu. "Classification of Pummelo (Citrus grandis) Extracts through UV-VIS-Based Chemical Fingerprint." Beverages 8, no. 2 (June 13, 2022): 34. http://dx.doi.org/10.3390/beverages8020034.

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Анотація:

Cold extraction methods with ethanol applied to the flavedo of Citrus fruits have been commonly applied for the preparation of several liquors. In order to obtain the extraction optimization and then the best ratio of functional ingredients in the extract, the flavedo of Citrus grandis Osbeck (pummelo) was subjected to a maceration with absolute ethanol at room temperature as well as at 40 °C. The kinetics of the extraction methods were monitored by UV–VIS spectroscopy, and a chemical fingerprint characteristic of each extract was determined by statistical multivariate analysis of the UV–VIS r

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30

Zhang, Haipeng, Huan Wen, Jiajing Chen, Zhaoxin Peng, Meiyan Shi, Mengjun Chen, Ziyu Yuan, Yuan Liu, Hongyan Zhang, and Juan Xu. "Volatile Compounds in Fruit Peels as Novel Biomarkers for the Identification of Four Citrus Species." Molecules 24, no. 24 (December 12, 2019): 4550. http://dx.doi.org/10.3390/molecules24244550.

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Анотація:

The aroma quality of citrus fruit is determined by volatile compounds, which bring about different notes to allow discrimination among different citrus species. However, the volatiles with various aromatic traits specific to different citrus species have not been identified. In this study, volatile profiles in the fruit peels of four citrus species collected from our previous studies were subjected to various analyses to mine volatile biomarkers. Principal component analysis results indicated that different citrus species could almost completely be separated. Thirty volatiles were identified a

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31

Morell-Monzó, Sergio, María-Teresa Sebastiá-Frasquet, and Javier Estornell. "Land Use Classification of VHR Images for Mapping Small-Sized Abandoned Citrus Plots by Using Spectral and Textural Information." Remote Sensing 13, no. 4 (February 13, 2021): 681. http://dx.doi.org/10.3390/rs13040681.

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Agricultural land abandonment is an increasing problem in Europe. The Comunitat Valenciana Region (Spain) is one of the most important citrus producers in Europe suffering this problem. This region characterizes by small sized citrus plots and high spatial fragmentation which makes necessary to use Very High-Resolution images to detect abandoned plots. In this paper spectral and Gray Level Co-Occurrence Matrix (GLCM)-based textural information derived from the Normalized Difference Vegetation Index (NDVI) are used to map abandoned citrus plots in Oliva municipality (eastern Spain). The propose

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32

Flamini, Guido, Laura Pistelli, Simona Nardoni, Valentina Ebani, Angela Zinnai, Francesca Mancianti, Roberta Ascrizzi, and Luisa Pistelli. "Essential Oil Composition and Biological Activity of “Pompia”, a Sardinian Citrus Ecotype." Molecules 24, no. 5 (March 5, 2019): 908. http://dx.doi.org/10.3390/molecules24050908.

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Pompia is a Sardinian citrus ecotype whose botanical classification is still being debated. In the present study, the composition of Pompia peel essential oil (EO) is reported for the first time, along with that of the leaf EO, as a phytochemical contribution to the classification of this ecotype. The peel EO was tested for its antioxidant ability (with both the 2,2-diphenyl-1-picarylhydrazyl (DPPH) and ferric reducing antioxidant power (FRAP) assays). Moreover, its antimicrobial activities were tested for the first time on dermatophytes (Microsporum canis, Microsporum gypseum, and Trichophyto

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33

Saddoud Debbabi, Olfa, Selma Ben Abdelaali, Rym Bouhlal, Sabrine Zneidi, Nasr Ben Abdelaali, and Massaoud Mars. "Genetic Characterization of Tunisian Lime Genotypes Using Pomological Traits." Journal of Horticultural Research 28, no. 1 (June 30, 2020): 65–76. http://dx.doi.org/10.2478/johr-2020-0004.

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AbstractCitrus genus includes a wide number of species that have been long cultivated and well adapted in Tunisia. It is represented by small number of plantations and considered as underutilized in Tunisia. Our goal was to genetically characterize Tunisian lime genotypes to obtain data useful for gene conservation and breeding purposes. The survey of genotypes was conducted in the Cap Bon region, where citrus cultivation is the most spread. Sixteen quantitative and 19 qualitative parameters were evaluated. The observed accessions belonged to three different species: Citrus limetta, Citrus lat

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34

Xiao, Deqin, Ruilin Zeng, Youfu Liu, Yigui Huang, Junbing Liu, Jianzhao Feng, and Xinglong Zhang. "Citrus greening disease recognition algorithm based on classification network using TRL-GAN." Computers and Electronics in Agriculture 200 (September 2022): 107206. http://dx.doi.org/10.1016/j.compag.2022.107206.

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35

X. Zhao, T. F. Burks, J. Qin, and M. A. Ritenour. "Digital Microscopic Imaging for Citrus Peel Disease Classification Using Color Texture Features." Applied Engineering in Agriculture 25, no. 5 (2009): 769–76. http://dx.doi.org/10.13031/2013.28845.

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36

Miller, William M. "Comparison of two classification approaches for automatic density separation of Florida citrus." Computers and Electronics in Agriculture 4, no. 3 (January 1990): 225–33. http://dx.doi.org/10.1016/0168-1699(90)90021-g.

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37

Shrivastava, Rahul J., and Jennifer L. Gebelein. "Land cover classification and economic assessment of citrus groves using remote sensing." ISPRS Journal of Photogrammetry and Remote Sensing 61, no. 5 (January 2007): 341–53. http://dx.doi.org/10.1016/j.isprsjprs.2006.10.003.

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38

Li, Xiuhua, Won Suk Lee, Minzan Li, Reza Ehsani, Ashish Ratn Mishra, Chenghai Yang, and Robert L. Mangan. "Spectral difference analysis and airborne imaging classification for citrus greening infected trees." Computers and Electronics in Agriculture 83 (April 2012): 32–46. http://dx.doi.org/10.1016/j.compag.2012.01.010.

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39

Iqbal, S. Md, A. Gopal, P. E. Sankaranarayanan, and Athira B. Nair. "Classification of Selected Citrus Fruits Based on Color Using Machine Vision System." International Journal of Food Properties 19, no. 2 (May 18, 2015): 272–88. http://dx.doi.org/10.1080/10942912.2015.1020439.

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40

Wang, Hui, Tie Cai, and Wei Cao. "Citrus Huanglongbing Recognition Algorithm Based on CKMOPSO." International Journal of Cognitive Informatics and Natural Intelligence 15, no. 4 (October 2021): 1–11. http://dx.doi.org/10.4018/ijcini.20211001.oa10.

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Анотація:

In view of the similarity of characteristics between the features of the disease images and the large dimension, and the features correlation of the disease images, this will lead to the generation of feature redundancy, and will introduce a serious impact on the recognition efficiency and accuracy of citrus Huanglongbing. In addition, they have the defects of high cost of detection algorithms and low detection accuracy. This will occur in the image cutting feature extraction stage, so this paper uses the citrus Huanglongbing recognition algorithm based on kriging model simplex crossover local

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41

Magalhães, Aida B., Giorgio S. Senesi, Anielle Ranulfi, Thiago Massaiti, Bruno S. Marangoni, Marina Nery da Silva, Paulino R. Villas Boas, et al. "Discrimination of Genetically Very Close Accessions of Sweet Orange (Citrus sinensis L. Osbeck) by Laser-Induced Breakdown Spectroscopy (LIBS)." Molecules 26, no. 11 (May 21, 2021): 3092. http://dx.doi.org/10.3390/molecules26113092.

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Анотація:

The correct recognition of sweet orange (Citrus sinensis L. Osbeck) variety accessions at the nursery stage of growth is a challenge for the productive sector as they do not show any difference in phenotype traits. Furthermore, there is no DNA marker able to distinguish orange accessions within a variety due to their narrow genetic trace. As different combinations of canopy and rootstock affect the uptake of elements from soil, each accession features a typical elemental concentration in the leaves. Thus, the main aim of this work was to analyze two sets of ten different accessions of very clo

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42

Harbi, Ahlem, Khaled Abbes, Beatriz Sabater-Muñoz, Francisco Beitia, and Brahim Chermiti. "Residual toxicity of insecticides used in Tunisian citrus orchards on the imported parasitoid Diachasmimorpha longicaudata (Hymenoptera: Braconidae): Implications for IPM program of Ceratitis capitata (Diptera: Tephritidae)." Spanish Journal of Agricultural Research 15, no. 3 (July 10, 2017): e1008. http://dx.doi.org/10.5424/sjar/2017153-10734.

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Citrus agro-industry is globally harshened mainly by Ceratitis capitata (Wiedemann), the most worldwide destructive tephritid fruit fly species. Citrus agro-industry is one of the pillars of Tunisia economy, and by hence, harshened by this species. Tunisia has established an Integrated Pest Management (IPM) programme against citrus pests, including C. capitata, that rely on the structured use of pesticides, on the application several trapping protocols, along with pilot-scale sterile insect technique program and, since 2013, with pilot-scale releases of the braconid parasitoid Diachasmimorpha

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Xing, Shuli, and Malrey Lee. "Classification Accuracy Improvement for Small-Size Citrus Pests and Diseases Using Bridge Connections in Deep Neural Networks." Sensors 20, no. 17 (September 3, 2020): 4992. http://dx.doi.org/10.3390/s20174992.

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Due to the rich vitamin content in citrus fruit, citrus is an important crop around the world. However, the yield of these citrus crops is often reduced due to the damage of various pests and diseases. In order to mitigate these problems, several convolutional neural networks were applied to detect them. It is of note that the performance of these selected models degraded as the size of the target object in the image decreased. To adapt to scale changes, a new feature reuse method named bridge connection was developed. With the help of bridge connections, the accuracy of baseline networks was

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Phi Bằng, Cao, and Trần Thị Thanh Huyền. "Identification, classification and chromosome mapping of the dehydrin gene family in clementine oranges (Citrus clementina)." Journal of Science, Natural Science 61, no. 4 (2016): 116–21. http://dx.doi.org/10.18173/2354-1059.2016-0018.

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Sun, Xiaopeng, Sai Xu, and Huazhong Lu. "Non-Destructive Identification and Estimation of Granulation in Honey Pomelo Using Visible and Near-Infrared Transmittance Spectroscopy Combined with Machine Vision Technology." Applied Sciences 10, no. 16 (August 5, 2020): 5399. http://dx.doi.org/10.3390/app10165399.

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Granulation is a physiological disorder of juice sacs in citrus fruit, causing juice sacs to become hard and dry and resulting in decreased internal quality of citrus fruit. Honey pomelo is a thick-skinned citrus fruit, and it is difficult to identify the extent of granulation by observation of the outer peel and fruit shape. In this study, a rapid and non-destructive testing method using visible and near-infrared transmittance spectroscopy combined with machine vision technology was applied to identify and estimate granulation inside fruit. A total of 600 samples in different growth periods w

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Franco, Mariane Ferreira, Eduardo Carvalho Marques, Carlos de Sousa Lucci, Bruno Leonardo Mendonça Ribeiro, Lucas Alencar Fernandes Beserra, Jeferson Carvalho da Silva, Gisela Gregoria Choque, and Lilian Gregory. "Estudo de diferentes proporções de milho x polpa cítrica x concentrado/volumoso na alimentação de ovinos da raça Suffolk." Revista Agraria Academica 5, no. 5 (September 1, 2022): 107–15. http://dx.doi.org/10.32406/v5n5/2022/107-115/agrariacad.

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Dehydrated citrus pulp (PC) has been used in animal feed in pellet form, as an energetic and highly digestible ingredient of the fibrous classification for growing and lactating animals. The purpose of this experiment is data on the introduction of citrus products in replacement, in diets with the possibility of a greater variety of products between concentrates and forages. To evaluate the research, rumen fluid was used to determine pH and ammonia dosage and a blood sample to determine blood glucose and urea. With this work, the change from corn ration to citrus pulp, in any of them, did not

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Ge Tu, Wang He Xi, and Bolormaa D. "Size based research on orange quality and classification." Mongolian Journal of Agricultural Sciences 25, no. 03 (December 28, 2018): 144–52. http://dx.doi.org/10.5564/mjas.v25i03.1184.

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Quality control and classification of agricultural products is an important component of agricultural production and sales. Use MATLAB 2010 software with image processing technology to create the quality classification of the oranges (diameter, perimeter, and field) based on the fruits. Prior to the quality classification, collecting orange tree images (taking photos from scratch), and then drawing a scratchy image. Comparing the ratio between the orange fruit diameter, perimeter, pixel and actual size, the fruit of the fruit is compared with the results of the Jeju Citrus Commission. The comp

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Furuta, Shu, Isao Hayakawa, and Yusaku Fujio. "Classification of the Constituents of Citrus Juice Residue by a Wet-Grinding Process." Journal of the Faculty of Agriculture, Kyushu University 34, no. 1/2 (November 1989): 101–6. http://dx.doi.org/10.5109/23892.

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FENG, Xinwei, Qinghua ZHANG, and Zhongliang ZHU. "Rapid Classification of Citrus Fruits Based on Raman Spectroscopy and Pattern Recognition Techniques." Food Science and Technology Research 19, no. 6 (2013): 1077–84. http://dx.doi.org/10.3136/fstr.19.1077.

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Befu, Mayumi, Akira Kitajima, and Kojiro Hasegawa. "Classification of the Citrus Chromosomes with Same Types of Chromomycin A Banding Patterns." Engei Gakkai zasshi 71, no. 3 (2002): 394–400. http://dx.doi.org/10.2503/jjshs.71.394.

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