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Relevant bibliographies by topics / Cotton disease

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Academic literature on the topic 'Cotton disease'

Author: Grafiati

Published: 4 June 2021

Last updated: 16 February 2022

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Journal articles on the topic "Cotton disease"

1

Kirkby, K. A., P. A. Lonergan, and S. J. Allen. "Three decades of cotton disease surveys in NSW, Australia." Crop and Pasture Science 64, no. 8 (2013): 774. http://dx.doi.org/10.1071/cp13143.

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Three decades of disease survey data have shown Verticillium wilt was one of the first major diseases of cotton recorded in the 1984–85 season. Survey reports the mean incidence was 4.1% in the 1984–85 season and rose to 16.6% in the 1989–90 season. Prior to 1984 all commercial varieties of cotton available in Australia were susceptible to bacterial blight and the disease was common. The adoption of the resistant varieties contributed to a dramatic decline in the incidence of bacterial blight and the removal of bacterial blight as a significant pathogen to Australian cotton crops by 1992. Survey results showed the incidence of black root rot increased on farms with a long history of growing cotton during the 1990s. Fusarium wilt of cotton was first reported in New South Wales (NSW) in 1994. The disease is now widespread, being confirmed on 86 NSW farms in six of the eight cotton production areas in NSW. These four significant plant disease ‘problems’ have challenged the cotton industry in NSW. Data provided by the surveys have indicated the relative importance of each of the diseases present and the impact of cultural practices and the adoption of new varieties on disease distribution, incidence and severity. The results have therefore been used to support and justify requests for research funding and have contributed to the development of Integrated Disease Management strategies. The NSW Department of Primary Industries continues to monitor the distribution of disease and the incidence and severity present in commercial cotton crops in all production areas of NSW. The aim of this paper is to highlight four significant cotton diseases in Australia and show relationships between cultural practices and declining and increasing incidence of disease.

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Chen, Fu Gui, Bao Jian Zhang, and Jun Hui Fu. "Application of Artificial Neural Network for Cotton Boil Spoiling Disease Prediction." Advanced Materials Research 143-144 (October 2010): 233–37. http://dx.doi.org/10.4028/www.scientific.net/amr.143-144.233.

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Based on the database of cotton boil spoiling disease in Xinxiang, a computerized intelligent expert system was established by using the Reverse Model of artificial neural network. With its speediness, robustness and 100%predicting accuracy, the system can be used as an effective method to predict the trend of cotton diseases. In recent years, we have seem some reports for which use artificial neural network system to forecast the disease of crops, but the artificial neural network using for predicting cotton boil spoiling disease have not been seen yet. Xinxiang is a city of Henan province of china, according to the survey materials of 10 years, the high output cotton boil spoiling disease break out every 4 years, the average quantity is 1.53, the rate of boil spoiling disease is 11.84%, so the loss is 168.28 . In order to prevent the cotton boil spoiling disease, we should forecast the disease, by doing this, it can increase quantity and quality of the cotton.

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Bag, Sudeep, Phillip M. Roberts, and Robert C. Kemerait. "Cotton Leafroll Dwarf Disease: An Emerging Virus Disease on Cotton in the U.S." Crops & Soils 54, no. 2 (March 2021): 18–22. http://dx.doi.org/10.1002/crso.20105.

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Ramacharan, Dr S. "A 3-Stage Method for Disease Detection of Cotton Plant Leaf using Deep Learning CNN Algorithm." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (July 30, 2021): 2503–10. http://dx.doi.org/10.22214/ijraset.2021.36913.

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Agriculture is one of the significant occupation in various countries including India. As major part of the Indian financial system is reliant on agriculture production, the intense consideration to the concern of food production is essential. The nomenclature and recognition of crop infection got much significance in technical as well as economic in the Agricultural Industry. While keeping track of diseases in plants with the support of experts can be very expensive in agriculture region. There is a necessity for a method or system which can automatically identify the diseases as it can bring revolution in monitoring enormous fields of crop and then plant leaflet can be taken ca The detection of cotton leaf disease is a very important factor to prevent serious outbreak.re imme4diately after recognition of disease. The aim of this paper is to provide guidelines for the development of application which recognizes cotton plant leaf diseases. For availing this user need to upload the image of the cotton leaf and then with the help of image processing one can get a digitized colour image of a diseased leaf which can be further processed by applying CNN algorithm to predict the actual root cause for the cotton leaf disease.

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Briddon, R. W., and P. G. Markham. "Cotton leaf curl virus disease." Virus Research 71, no. 1-2 (November 2000): 151–59. http://dx.doi.org/10.1016/s0168-1702(00)00195-7.

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Reddall, A., A. Ali, J. A. Able, J. Stonor, L. Tesoriero, P. R. Wright, M. A. Rezaian, and L. J. Wilson. "Cotton bunchy top: an aphid and graft transmitted cotton disease." Australasian Plant Pathology 33, no. 2 (2004): 197. http://dx.doi.org/10.1071/ap03094.

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Pupim Junior, Osmério, Ivan Schuster, Ronald Barth Pinto, Ely Pires, Jean-Louis Belot, Pierre Silvie, Luiz Gonzaga Chitarra, Lúcia Vieira Hoffmann, and Paulo Barroso. "Inheritance of resistance to cotton blue disease." Pesquisa Agropecuária Brasileira 43, no. 5 (May 2008): 661–65. http://dx.doi.org/10.1590/s0100-204x2008000500015.

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The objective of this work was to determine the inheritance of cotton blue disease resistance by cotton plants. Populations derived from the CD 401 and Delta Opal resistant varieties were evaluated, through a greenhouse test with artificial inoculation by viruliferous aphids. Cotton blue disease resistance is conditioned by one dominant gene, both in CD 401 and Delta Opal varieties.

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Sattar, M. Naeem, Anders Kvarnheden, Muhammad Saeed, and Rob W. Briddon. "Cotton leaf curl disease – an emerging threat to cotton production worldwide." Journal of General Virology 94, no. 4 (April 1, 2013): 695–710. http://dx.doi.org/10.1099/vir.0.049627-0.

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Cotton leaf curl disease (CLCuD) is a serious disease of cotton which has characteristic symptoms, the most unusual of which is the formation of leaf-like enations on the undersides of leaves. The disease is caused by whitefly-transmitted geminiviruses (family Geminiviridae, genus Begomovirus) in association with specific, symptom-modulating satellites (betasatellites) and an evolutionarily distinct group of satellite-like molecules known as alphasatellites. CLCuD occurs across Africa as well as in Pakistan and north-western India. Over the past 25 years, Pakistan and India have experienced two epidemics of the disease, the most recent of which involved a virus and satellite that are resistance breaking. Loss of this conventional host–plant resistance, which saved the cotton growers from ruin in the late 1990s, leaves farmers with only relatively poor host plant tolerance to counter the extensive losses the disease causes. There has always been the fear that CLCuD could spread from the relatively limited geographical range it encompasses at present to other cotton-growing areas of the world where, although the disease is not present, the environmental conditions are suitable for its establishment and the whitefly vector occurs. Unfortunately recent events have shown this fear to be well founded, with CLCuD making its first appearance in China. Here, we outline recent advances made in understanding the molecular biology of the components of the disease complex, their interactions with host plants, as well as efforts being made to control CLCuD.

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Dr. Vijay Kumar Garg, Sandhya N. dhage,. "Role of Machine Learning Approach for Detection and Classification of Diseases in Cotton Plant." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 5 (April 11, 2021): 810–17. http://dx.doi.org/10.17762/turcomat.v12i5.1488.

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Qualitative and quantitative agricultural production leads to economic benefits which can be achieved by periodic monitoring of crop, detection and prevention of crop diseases and insects. Quality of crop production is reduced by pest infection and crop diseases. Existing measures involves manual detection of cotton diseases by farmers and experts which requires regular monitoring and detection manifest at middle to later stage of infection which causes many disadvantages such as becoming too late for diseases to be cured. Lack of early detection of diseases causes the diseases to be spread in nearby crops in the field and also spraying of pesticides is done on entire field for minimizing the infection of disease. The main goal of proposed research topic is to find the solution to the agriculture problem which involves detecting disease in cotton plant at early stage and classify the disease based on symptoms. Early detection of disease at an early stage prevent it from spreading to another area and preventive measures can be taken by farmers by spraying pesticides to control its growth which helps to increase the cotton yield production. Automatic identification of the different diseases affecting cotton crop will give many benefits to the farmers so that time, money will be saved and also gives healthy life to the crop. The contribution of this paper is to present the machine learning approach used for cotton crop disease diagnosis and classification.

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Çelik, Sadettin, Adem Bardak, Oktay Erdoğan, Döne Parlak, Rıdvan Uçar, Halil Tekerek, Ali Can Sever, and Khizer Bahatti Hayat. "Determination of the Response of Some Cotton Varieties to Cotton Wilt Disease Caused by Verticillium dahliae Kleb." Turkish Journal of Agriculture - Food Science and Technology 5, no. 12 (December 14, 2017): 1488. http://dx.doi.org/10.24925/turjaf.v5i12.1488-1492.1476.

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Verticillium Wilt Disease is one of the most important diseases affecting the rate of cotton yield. There is no economic chemical control for Verticillium wilt, but it is recommended to use resistant varieties to control this disease. This experiment was carried out in a randomized plot design with four replications in the growth chamber to determine the response of some cotton cultivars against a defoliating and non-defoliating pathotypes of Verticilllium dahliae Kleb. In the study, a total of twenty cotton cultivars i.e. the resistant control GIZA 75, the tolerant control CARMEN and the susceptible control ACALA SJ2, defoliating (PYDV6 isolate) and non-defoliating (Vd 11 isolate) pathotypes were used, and cotton varieties were tested using conidial suspension technique. Analysis of variance showed significantly (P

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Dissertations / Theses on the topic "Cotton disease"

1

Butler, G. D. Jr, T. J. Henneberry, and J. K. Brown. "Cotton Leaf Crumple Disease of Pima Cotton." College of Agriculture, University of Arizona (Tucson, AZ), 1985. http://hdl.handle.net/10150/204080.

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2

Olsen, Mary. "Cotton (Texas) Root Rot." College of Agriculture, University of Arizona (Tucson, AZ), 2015. http://hdl.handle.net/10150/346609.

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Revised 02/2015; Originally published: 2000.
The most important disease of woody dicotyledonous plants in Arizona is Phymatotrichopsis root rot (Cotton or Texas root rot) caused by a unique and widely distributed soil-borne fungus, Phymatotrichopsis omnivora. The fungus is indigenous to the alkaline, low-organic matter soils of the southwestern United States and central and northern Mexico.

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Mauk, P. A., and R. B. Hine. "A Predictive System for Disease Incidence of Black Root Rot of Cotton." College of Agriculture, University of Arizona (Tucson, AZ), 1987. http://hdl.handle.net/10150/204490.

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A quantitative technique has been developed to assay cotton soils for populations of Thielaviopsis basicola, a soil occurring fungus that causes the seedling disease of cotton known as Black Root Rot. The procedure utilizes a soil dilution technique with a carrot extract agar containing etridiazol, Mystatin, streptomycin sulfate, chlortetracycline, calcium carbonate and PCNB. Populations of the fungus have been monitored from April to December, 1986 in a heavily infested Pima S-6 field in cooperation with Bob Cockrill, a Coolidge grower. When field soils containing approximately 600 propagules of the fungus per gram of air dry soil were planted to Pima S-6 in the laboratory, 75-100% and 50-75% cortical decay occurred at 20 and 28 C, respectively. This seedling damage was related to subsequent reduced seedling vigor.

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4

Olsen, Mary W., and Jeffrey C. Silvertooth. "Diseases and Production Problems of Cotton in Arizona." College of Agriculture and Life Sciences, University of Arizona (Tucson, AZ), 2001. http://hdl.handle.net/10150/146706.

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Ahmad, Aftab, Muhammad Zia-Ur-Rehman, Usman Hameed, Rao Abdul Qayyum, Ammara Ahad, Aneela Yasmeen, Faheem Akram, et al. "Engineered Disease Resistance in Cotton Using RNA-Interference to Knock down Cotton leaf curl Kokhran virus-Burewala and Cotton leaf curl Multan betasatellite Expression." MDPI AG, 2017. http://hdl.handle.net/10150/626109.

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Cotton leaf curl virus disease (CLCuD) is caused by a suite of whitefly-transmitted begomovirus species and strains, resulting in extensive losses annually in India and Pakistan. RNA-interference (RNAi) is a proven technology used for knockdown of gene expression in higher organisms and viruses. In this study, a small interfering RNA (siRNA) construct was designed to target the AC1 gene of Cotton leaf curl Kokhran virus-Burewala (CLCuKoV-Bu) and the beta C1 gene and satellite conserved region of the Cotton leaf curl Multan betasatellite (CLCuMB). The AC1 gene and CLCuMB coding and non-coding regions function in replication initiation and suppression of the plant host defense pathway, respectively. The construct, V b, was transformed into cotton plants using the Agrobacterium-mediated embryo shoot apex cut method. Results from fluorescence in situ hybridization and karyotyping assays indicated that six of the 11 T-1 plants harbored a single copy of the V beta transgene. Transgenic cotton plants and non-transgenic (susceptible) test plants included as the positive control were challenge-inoculated using the viruliferous whitefly vector to transmit the CLCuKoV-Bu/ CLCuMB complex. Among the test plants, plant V beta-6 was asymptomatic, had the lowest amount of detectable virus, and harbored a single copy of the transgene on chromosome six. Absence of characteristic leaf curl symptom development in transgenic V beta-6 cotton plants, and significantly reduced begomoviral-betasatellite accumulation based on real-time polymerase chain reaction, indicated the successful knockdown of CLCuKoV-Bu and CLCuMB expression, resulting in leaf curl resistant plants.

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Mansoor, Shahid. "Cotton leaf curl disease in Pakistan : molecular characterisation, diagnostics, and genetically engineered virus resistance." Thesis, University of East Anglia, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.302196.

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Hellein, Kristen Nicole. "Leaf-epiphytic pseudomonads as diagnostic indicators of disease and stress in cotton (Gossypium spp.)." [Pensacola, Fla.] : University of West Florida, 2009. http://purl.fcla.edu/fcla/etd/WFE0000154.

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8

Albukhari, Fahad Mohammedsaleh. "Verticillium dahliae causes the fungal wilting disease of cotton plants grown on the Mississippi State North Farm." Thesis, Mississippi State University, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=1604185.

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The emergence and spread of Verticillium wilt were observed in cotton plants at the R.R. Foil Plant Science Research Center at Mississippi State during the late summer in 2013 and 2014. Several fungi with different morphology and growth characteristics were isolated from diseased cotton plants. Genomic DNA was extracted from the isolated fungal species and used in molecular typing via PCR amplification and DNA sequencing analysis of the ribosomal internal transcribed spacer (ITS) region. A total of five fungal genera were identified, and Verticillium sp. was the most frequently isolated genus. The isolated Verticillium strains could be Verticillium dahliae, Verticillium longisporum or even Verticillium albo-atrum. A PCR-based genotyping method using VTA2 (Verticillium transcription activator) gene specific primers confirmed that the isolated Verticillium strain was Verticillium dahliae, and it caused Verticillium wilt in Mississippi cotton plants. Pathogenicity tests (Koch’s postulates) confirmed the earlier qualitative identifications of Verticillium dahliae in the greenhouse.

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9

Weeks, James Michael Jr. "Perennial Grass Based Crop Rotations in Virginia: Effects on Soil Quality, Disease Incidence, and Cotton and Peanut Growth." Thesis, Virginia Tech, 2008. http://hdl.handle.net/10919/35394.

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In 2003 eight peanut and cotton crop rotations were established in southeastern Virginia, 4 of which included 2 or 3 years of tall fescue or orchardgrass grown as high-value hay crops. Each crop rotation was evaluated for changes in soil quality indicators including soil carbon and nitrogen, water stable soil aggregates, plant available water content, bulk density, cone index values, and soil moisture. Cotton and peanut growth and yield were also observed to evaluate changes in crop growth associated with differences in soil quality. Soilborne plant pathogens including root-knot nematode, stubby root nematode, ring nematode, stunt nematode, and Cylindrocladium parasiticum microsclerotia were measured in the spring and fall of each year to determine differences associated with crop rotations. Water stable soil aggregates in 2007 were higher in rotations with 3 years of either perennial grass. Soil moisture tended to be the highest at depths 30 - 60 cm in the 3-year tall fescue rotation in August and September 2007. Cotton in 2006 and peanut in 2007 had higher growth and yield where the annual crop directly followed a perennial grass. Root-knot nematode tended to decrease in all rotations over time. Stubby root nematode populations tended to increase in rotations with either duration of orchardgrass. Including perennial grasses in cotton and peanut rotations has the potential to increase growth and yield as demonstrated in this research.
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Mulesky, Melinda Anne. "Rhizosphere competence, antibiotic and siderophore biosynthesis in Pseudomonas chlororaphis: implications for the biological control of cotton seedling disease pathogens." Diss., Virginia Tech, 1995. http://hdl.handle.net/10919/40235.

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Books on the topic "Cotton disease"

1

Wilson, Floyd D. Innovations in the X-ray technique of evaluating cotton germplasm for resistance to pink bollworm. [Washington, D.C.?]: U.S. Dept. of Agriculture, Agricultural Research Service, 1985.

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Shoham, Ḥayim. Niṭur u-fiḳuaḥ maziḳim be-khutnah. [Bet Dagan]: Miśrad ha-ḥaḳlaʼut, Sherut ha-hadrakhah ṿeha-miḳtsoʻa, ha-Maḥlaḳah le-tsimḥe taʻaśiyah [ṿe]ha-Maḥlaḳah la-haganat ha-tsomeaḥ, 1988.

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Tolēs, Iōannēs D. Vamvaki: Echthroi, astheneies, zizania. [Greece]: I.D. Tolēs, 1986.

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Bebee, Charles N. The protection of cotton, January 1980-November 1984: Citation from Agricola concerning diseases and other environmental considerations. Beltsville, Md: U.S. Dept. of Agriculture, National Agricultural Library, 1985.

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5

Butler, George D. Bemisia tabaci (Gennadius): A pest of cotton in the southwestern United States. Washington: U.S. Dept. of Agriculture, Agricultural Research Service, 1986.

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Cueto, Marcos. Innovación en la agricultura: Fermín Tangüis y el algodón el el Perú. Lima: Universidad del Pacífico, Centro de Investigación, 1999.

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Matthews, G. A. Cotton insect pests and their management. Harlow, Essex, UK: Longman Scientific & Technical, 1989.

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Nikiphoroff, Basilio. El subdesarrollo rural paraguayo: La problemática algodonera : estrategias para el desarrollo. [Asunción]: Fundación Moisés Bertoni, 1994.

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Shoham, Ḥayim. Hamlatsot le-hadbarat pegaʻim be-khutnah. [Tel Aviv]: ha-Maḥlaḳah le-firsumim, 1985.

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Shoham, Ḥayim. Hamlatsot le-hadbarat pegaʻim be-khutnah. [Jerusalem?]: Miśrad ha-ḥaḳlaʼut, Sherut ha-hadrakhah ṿeha-miḳtsoʻa, ha-Maḥlaḳah la-haganat ha-tsomeaḥ, [ṿe-]ha-Maḥlaḳah le-tsimḥe taʻaśiyah, 1987.

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Book chapters on the topic "Cotton disease"

1

Wright, Robert J., Chen Niu, and Bay Nguyen. "Bridging Classical and Molecular Genetics of Cotton Disease Resistance." In Genetics and Genomics of Cotton, 313–36. New York, NY: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-70810-2_13.

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Rothrock, Craig S. "Cotton Diseases Incited by Rhizoctonia Solani." In Rhizoctonia Species: Taxonomy, Molecular Biology, Ecology, Pathology and Disease Control, 269–77. Dordrecht: Springer Netherlands, 1996. http://dx.doi.org/10.1007/978-94-017-2901-7_24.

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3

Fishwick, David. "Cotton, other bioaerosols, inhalation fevers and occupational organising pneumonia." In Occupational and Environmental Lung Disease, 211–26. Sheffield, United Kingdom: European Respiratory Society, 2020. http://dx.doi.org/10.1183/2312508x.10035119.

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Rajasekaran, K., T. J. Jacks, J. W. Cary, and T. E. Cleveland. "Disease Resistant Transgenic Cotton to Prevent Preharvest Aflatoxin Contamination." In Plant Biotechnology 2002 and Beyond, 147–50. Dordrecht: Springer Netherlands, 2003. http://dx.doi.org/10.1007/978-94-017-2679-5_24.

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Rothe, Prashant R., and Jyoti P. Rothe. "Intelligent Pattern Recognition System with Application to Cotton Leaf Disease Identification." In Innovations in Computer Science and Engineering, 19–27. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8201-6_3.

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Ali, Safdar, M. Aslam Khan, Shahbaz Talib Sahi, M. Atiq, and A. Hannan. "Cotton Leaf Curl Virus Disease Predictive Model Based on Environmental Variables." In Improvement of Crops in the Era of Climatic Changes, 323–35. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4614-8824-8_13.

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Adhao, Asmita Sarangdhar, and Vijaya Rahul Pawar. "Automatic Cotton Leaf Disease Diagnosis and Controlling Using Raspberry Pi and IoT." In Intelligent Communication and Computational Technologies, 157–67. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-5523-2_15.

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Chen, Bing, Ke-Ru Wang, Shao-Kun Li, Xue-Yan Sui, Fang-Yong Wang, and Jun-Hua Bai. "Spectral Characteristics of Cotton Infected with Verticillium Wilt and Severity Level of Disease Estimated Models." In Crop Modeling and Decision Support, 325–33. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01132-0_36.

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Nyvall, Robert F. "Diseases of Cotton." In Field Crop Diseases Handbook, 171–210. Boston, MA: Springer US, 1989. http://dx.doi.org/10.1007/978-1-4757-5221-2_5.

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Perry, Brian, Bernard Bett, Eric Fèvre, Delia Grace, and Thomas Fitz Randolph. "Veterinary epidemiology at ILRAD and ILRI, 1987-2018." In The impact of the International Livestock Research Institute, 208–38. Wallingford: CABI, 2020. http://dx.doi.org/10.1079/9781789241853.0208.

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Abstract This chapter describes the activities of the International Livestock Research Institute (ILRI) and its predecessor, the International Laboratory for Research on Animal Diseases (ILRAD) from 1987 to 2018. Topics include scientific impacts; economic impact assessment; developmental impacts; capacity development; partnerships; impacts on human resources capacity in veterinary epidemiology; impacts on national animal health departments and services; impacts on animal health constraints in developing countries; impacts on ILRI's research and strategy; the introduction of veterinary epidemiology and economics at ILRAD; field studies in Kenya; tick-borne disease dynamics in eastern and southern Africa; heartwater studies in Zimbabwe; economic impact assessments of tick-borne diseases; tick and tick-borne disease distribution modelling; modelling the infection dynamics of vector-borne diseases; economic impact of trypanosomiasis; the epidemiology of resistance to trypanocides; the development of a modelling technique for evaluating control options; sustainable trypanosomiasis control in Uganda and in the Ghibe Valley of Ethiopia; spatial modelling of tsetse distributions; preventing and containing trypanocide resistance in the cotton zone of West Africa; rabies research; the economic impacts of rinderpest control; applying economic impact assessment tools to foot and mouth disease (FMD) control, the southern Africa FMD economic impact study; economic impacts of FMD in Peru, Colombia and India; economic impacts of FMD control in endemic settings in low- and middle-income countries; the global FMD research alliance (GFRA); Rift Valley fever; economic impact assessment of control options and calculation of disability-adjusted life years (DALYs); RVF risk maps for eastern Africa; land-use change and RVF infection and disease dynamics; epidemiology of gastrointestinal parasites; priorities in animal health research for poverty reduction; the Wellcome Trust Epidemiology Initiatives; the broader economic impact contributions; the responses to highly pathogenic avian influenza; the International Symposium on Veterinary Epidemiology and Economics (ISVEE) experience, the role of epidemiology in ILRAD and ILRI and the impacts of ILRAD and ILRI's epidemiology; capacity development in veterinary epidemiology and impact assessment; impacts on national animal health departments and services; impacts on animal health constraints in developing countries and impacts on ILRI's research and strategy.

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Conference papers on the topic "Cotton disease"

1

Jenifa, A., R. Ramalakshmi, and V. Ramachandran. "Cotton Leaf Disease Classification using Deep Convolution Neural Network for Sustainable Cotton Production." In 2019 IEEE International Conference on Clean Energy and Energy Efficient Electronics Circuit for Sustainable Development (INCCES). IEEE, 2019. http://dx.doi.org/10.1109/incces47820.2019.9167715.

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Chopda, Jayraj, Hiral Raveshiya, Sagar Nakum, and Vivek Nakrani. "Cotton Crop Disease Detection using Decision Tree Classifier." In 2018 International Conference on Smart City and Emerging Technology (ICSCET). IEEE, 2018. http://dx.doi.org/10.1109/icscet.2018.8537336.

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Rothe, P. R., and R. V. Kshirsagar. "Cotton leaf disease identification using pattern recognition techniques." In 2015 International Conference on Pervasive Computing (ICPC). IEEE, 2015. http://dx.doi.org/10.1109/pervasive.2015.7086983.

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Shah, Nikhil, and Sarika Jain. "Detection of Disease in Cotton Leaf using Artificial Neural Network." In 2019 Amity International Conference on Artificial Intelligence (AICAI). IEEE, 2019. http://dx.doi.org/10.1109/aicai.2019.8701311.

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Jenifa, A., R. Ramalakshmi, and V. Ramachandran. "Classification of Cotton Leaf Disease Using Multi-Support Vector Machine." In 2019 IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing (INCOS). IEEE, 2019. http://dx.doi.org/10.1109/incos45849.2019.8951356.

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Parikh, Aditya, Mehul S. Raval, Chandrasinh Parmar, and Sanjay Chaudhary. "Disease Detection and Severity Estimation in Cotton Plant from Unconstrained Images." In 2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA). IEEE, 2016. http://dx.doi.org/10.1109/dsaa.2016.81.

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Thomasson, J. Alex, Xiwei Wang, Tianyi Wang, Chenghai Yang, Robert L. Nichols, and Ryan Collett. "Disease detection and mitigation in a cotton crop with UAV remote sensing." In Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping III, edited by J. Alex Thomasson, Mac McKee, and Robert J. Moorhead. SPIE, 2018. http://dx.doi.org/10.1117/12.2307018.

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Shakeel, Wajeeha, Mudassar Ahmad, and Nasir Mahmood. "Early Detection of Cercospora Cotton Plant Disease by Using Machine Learning Technique." In 2020 30th International Conference on Computer Theory and Applications (ICCTA). IEEE, 2020. http://dx.doi.org/10.1109/iccta52020.2020.9477693.

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Sarangdhar, Adhao Asmita, and V. R. Pawar. "Machine learning regression technique for cotton leaf disease detection and controlling using IoT." In 2017 International Conference of Electronics, Communication and Aerospace Technology (ICECA). IEEE, 2017. http://dx.doi.org/10.1109/iceca.2017.8212855.

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Yan-Cheng Zhang, Han-Ping Mao, Bo Hu, and Ming-Xi Li. "Features selection of cotton disease leaves image based on fuzzy feature selection techniques." In 2007 International Conference on Wavelet Analysis and Pattern Recognition. IEEE, 2007. http://dx.doi.org/10.1109/icwapr.2007.4420649.

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Reports on the topic "Cotton disease"

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Plant Protection and Quarantine: Helping U.S. Agriculture Thrive--Across the Country and Around the World, 2016 Annual Report. U.S. Department of Agriculture, Animal and Plant Health Inspection Service, March 2017. http://dx.doi.org/10.32747/2017.7207241.aphis.

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Abstract:

For Plant Protection and Quarantine (PPQ) and our partners, 2016 was a year of remarkable successes. Not only did we eradicate 10 fruit fly outbreaks, but we also achieved 4 years with zero detections of pink bollworm, moving us one step closer to eradicating this pest from all commercial cotton-growing areas of the continental United States. And when the U.S. corn industry faced the first-ever detection of bacterial leaf streak (Xanthomonas vasicular pv vasculorum), we devised a practical and scientific approach to manage the disease and protect valuable export markets. Our most significant domestic accomplishment this year, however, was achieving one of our agency’s top 10 goals: eliminating the European grapevine moth (EGVM) from the United States. On the world stage, PPQ helped U.S. agriculture thrive in the global market-place. We worked closely with our international trading partners to develop and promote science-based standards, helping to create a safe, fair, and predictable agricultural trade system that minimizes the spread of invasive plant pests and diseases. We reached critical plant health agreements and resolved plant health barriers to trade, which sustained and expanded U.S. export markets valued at more than $4 billion. And, we helped U.S. producers meet foreign market access requirements and certified the health of more than 650,000 exports, securing economic opportunities for U.S. products abroad. These successes underscore how PPQ is working every day to keep U.S. agriculture healthy and profitable.

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