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Relevant bibliographies by topics / Detection of arecanut diseases

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Academic literature on the topic 'Detection of arecanut diseases'

Author: Grafiati

Published: 5 June 2025

Last updated: 1 August 2025

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Journal articles on the topic "Detection of arecanut diseases"

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R N, Pushpa. "Review on Detection and Prediction of Diseases in Arecanut Trees." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 03 (2024): 1–3. http://dx.doi.org/10.55041/ijsrem29103.

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In this study, we propose a method for detecting and predicting diseases in arecanut plants using image processing. The proposed method consists of three main steps: image acquisition, image segmentation, and disease detection and prediction. The performance of the proposed method is evaluated using a dataset of arecanut leaf images with various diseases. The results show that the proposed method can accurately detect and predict the presence of diseases in the arecanut plants with high precision and recall rates. Keywords: Arecanut, Machine learning, Convolutional neural networks.

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B, Chethan. "Arecanut Disease Detection." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 01 (2025): 1–9. https://doi.org/10.55041/ijsrem41024.

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The tropical crop arecanut, sometimes referred to as betel nut, is primarily farmed in India. In terms of arecanut production and consumption, the nation ranks second in the world. The areca nut plant is vulnerable to numerous diseases that impact its roots, stem, leaves, and fruits throughout its life cycle. While some of these illnesses can be seen with the naked eye, others cannot. These illnesses are brought on by abrupt changes in temperature and other meteorological factors; early disease identification is crucial. In order to minimize losses for farmers, this work focuses on early and p

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Hegde, Ajit, Vijaya Shetty Sadanand, Chinmay Ganapati Hegde, Krishnamurthy Manjunath Naik, and Kanaad Deepak Shastri. "Identification and categorization of diseases in arecanut: a machine learning approach." Indonesian Journal of Electrical Engineering and Computer Science 31, no. 3 (2023): 1803. http://dx.doi.org/10.11591/ijeecs.v31.i3.pp1803-1810.

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Arecanut is one of the prominent commercial crops that are grown worldwide for traditional medicines, furniture, cosmetics, food, veterinary preparations, and textile industries. It experiences a variety of diseases during its existence, from the bottom to the tip. The conventional method for detection of diseases is through visual inspection and it is also necessary to have properly designed laboratories to check these harvests. It is a time consuming and tedious task to inspect these crops across wide acres of plantations. The proposed system has been developed that uses convolutional neural

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M U, Likhitha, and Dr Geetha M. "AI BASED ARECANUT PLANT DISEASE CLASSIFICATION SYSTEM." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 07 (2024): 1–10. http://dx.doi.org/10.55041/ijsrem36460.

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Arecanut, commonly known as betel nut, is a vital cash crop in many tropical regions, contributing significantly to the agricultural economy. However, like other crops, arecanut plants are susceptible to various diseases that can severely impact yield and quality. Early detection and accurate classification of these diseases are crucial for timely intervention and effective disease management. In this study, we propose an AI-based arecanut plant disease classification system that leverages deep learning techniques to automatically identify and classify different diseases affecting arecanut pla

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Puneeth, B. R., and P. S. Nethravathi. "A Literature Review of the Detection and Categorization of various Arecanut Diseases using Image Processing and Machine Learning Approaches." International Journal of Applied Engineering and Management Letters (IJAEML) 5, no. 2 (2021): 183–204. https://doi.org/10.5281/zenodo.5773853.

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<strong>Background/Purpose: </strong><em>Every scholarly research project starts with a survey of the literature, which acts as a springboard for new ideas. The purpose of this literature review is to become familiar with the study domain and to assess the </em><em>work's credibility. It also improves with the subject's integration and summary. This article briefly discusses the detection of disease and classification to achieve the objectives of the study.</em> <strong>Objective:</strong> <em>The main objective of this literature survey is to explore the different techniques applied t

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Hegde, Dinesh G. "Classification of Costal Area Diseases of Arecanut using Dual Convolutional Neural Network." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–9. https://doi.org/10.55041/ijsrem51073.

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This study presents an automated approach for the detection and classification of coastal area arecanut diseases using Dual Convolutional Neural Networks (DCNNs). A custom dataset consisting of 1,000 images, captured under expert supervision from Navilgon village in Honnavara taluk, Karnataka. This was developed to represent four classes like Healthy, Rot, Split, and Rot+Split. All these images were preprocessed by resizing them to 128×128 pixels and converted into numerical arrays to facilitate model training. The proposed DCNN model incorporates double convolutional blocks, batch normalizati

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Manimekalai, R., R. Sathish Kumar, V. P. Soumya, and G. V. Thomas. "Molecular Detection of Phytoplasma Associated with Yellow Leaf Disease in Areca Palms (Areca catechu) in India." Plant Disease 94, no. 11 (2010): 1376. http://dx.doi.org/10.1094/pdis-06-10-0440.

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The arecanut palm (Areca catechu L.), Arecaceae family, is one of the most important commercial crops in the world, which yields fruits called arecanut that are used as a medicine and chewing substance (1). Yellow leaf disease (YLD) is one of the most serious diseases in areca palms in India. It reduces the yield as much as 50% over a period of 3 years immediately following disease incidence. Foliar yellowing, the most conspicuous symptom, begins from the inner whorl and spreads to the outer parts of the crown. Chlorosis is observed on almost all leaves in the whorl from edges of the leaflet t

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H H, Shilpa, and Padma R. "Arecanut Status Detection Using Deep Learning." International Journal of Innovative Research in Advanced Engineering 11, no. 05 (2024): 616–24. http://dx.doi.org/10.26562/ijirae.2024.v1105.26.

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Arecanut, a significant cash crop in many tropical regions, undergoes distinct stages of ripening, posing challenges for timely harvest and market readiness. This research presents a comprehensive framework employing cutting-edge deep learning methodologies, specifically TensorFlow Lite, for accurate and real-time detection of Arecanut status, encompassing ripe, unripe, and dry stages. The integration of OpenCV for image preprocessing and deployment on Raspberry Pi enhances the system's accessibility and usability, enabling on-site detection using the Raspberry Pi camera module. The study begi

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JAMANAL, HANUMANTAPPA, and C. MURTHY. "Constraints faced in production and marketing of arecanut in Karnataka." Journal of Farm Sciences 37, no. 01 (2024): 54–58. http://dx.doi.org/10.61475/jfs.2024.v37i1.13.

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The arecanut is one of the most important crops grown in Karnataka and the state’s area under arecanut cultivation has nearly doubled in the last 15 years. Shivamogga, Davanagere, Chikkamagaluru, Dakshina Kannada. Tumkur and Uttara Kannada are the major arecanut producing districts of Karnataka, the accounting for a sizable share of 60 per cent of the area and 65 per cent of arecanut production in the state. The random sampling method was used for selection of arecanut growers and four districts were selected namely Dakshina Kannada, Chikkamagaluru, Davanagere and Shivamogga. Each districts tw

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Lei, Shuhan, Jianbiao Luo, Xiaojun Tao, and Zixuan Qiu. "Remote Sensing Detecting of Yellow Leaf Disease of Arecanut Based on UAV Multisource Sensors." Remote Sensing 13, no. 22 (2021): 4562. http://dx.doi.org/10.3390/rs13224562.

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Unmanned aerial vehicle (UAV) remote sensing technology can be used for fast and efficient monitoring of plant diseases and pests, but these techniques are qualitative expressions of plant diseases. However, the yellow leaf disease of arecanut in Hainan Province is similar to a plague, with an incidence rate of up to 90% in severely affected areas, and a qualitative expression is not conducive to the assessment of its severity and yield. Additionally, there exists a clear correlation between the damage caused by plant diseases and pests and the change in the living vegetation volume (LVV). How

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Dissertations / Theses on the topic "Detection of arecanut diseases"

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Boyd, M. T. "Detection of retroviral indicators in myeloproliferative diseases." Thesis, University of Southampton, 1989. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.234379.

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Kimani, Esther Wairimu. "Serological detection of Didymella lycopersici (Kleb.)." Thesis, University of British Columbia, 1990. http://hdl.handle.net/2429/29190.

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Polyclonal antisera produced against spores, soluble protein and the whole mycelium fractions of Didymella lycopersici reacted with the homologous and heterologous antigens. The most sensitive antiserum was that raised against the whole mycelium, the soluble protein and the spore, in decreasing order of sensitivity. Using the antiserum raised against the whole mycelium it was possible to detect D. lycopersici on diseased plants and infested seeds. Cross reactivity was observed between the antisera produced to D. lycopersici and D. applanata, D. bryoniae and other tomato fungal pathogens includ

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Leenaars, Petronella Engelina Maria. "Prevention and early detection of sexually transmitted diseases." Amsterdam : Maastricht : Thesis Publishers ; University Library, Maastricht University [Host], 1994. http://arno.unimaas.nl/show.cgi?fid=6593.

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Ho, Shek-yin, and 何碩然. "Detection of merkel cell polyomavirus in gynaecological diseases." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2013. http://hdl.handle.net/10722/193567.

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Merkel cell polyomavirus (MCPyV) is an oncogenic virus exist in about 80% of Merkel Cell Carcinoma (MCC), an aggressive human skin cancer. Evidence of MCPyV existing in other kind of skin neoplasms such as cutaneous squamous cell carcinomas (SCCs) has been reported. Since the major type of cervical cancer is SCCs, MCPyV may be associated with cervical cancer tumorigenesis. A Japanese research group has documented the presence of MCPyV DNA in both cervical SCCs and cervical adenocarcinomas (ACs) from Japanese patients. Nevertheless, the association between MCPyV and cervical cancer remains inco

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Ben, Aissa Soler Alejandra. "Rapid diagnostic test for the detection of communicable diseases." Doctoral thesis, TDX (Tesis Doctorals en Xarxa), 2020. http://hdl.handle.net/10803/670392.

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La prevenció i el control de les malalties transmissibles depenen, en gran mesura, de la detecció ràpida i eficaç. Els mètodes convencionals per a la detecció d'un patogen, com ara el cultiu microbiològic, generalment requereixen molt de temps, són laboriosos, necessiten personal qualificat i no són aptes com a eines de diagnòstic en el punt d'atenció. El desenvolupament de mètodes de diagnòstic ràpid en el marc dels criteris ASSURED, de l'anglès (A) Affordable, (SS) Sensitive i Didàctiques, (O) User-friendly, (R) Rapid and Robust, (I) Equipment free, and (d) Deliverable to those who need i

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Ghaffari, Reza. "Non-destructive detection of diseases using plant emitted volatiles." Thesis, University of Warwick, 2013. http://wrap.warwick.ac.uk/61777/.

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The detection of plant diseases is an important part of commercial greenhouse crop production and can enable continued disease and pest control which will ultimately lead to the economical benefit as well as the significant reduction in use of chemical and biochemical treatments. A plant subject to infection typically releases exclusive volatile organic compounds (VOCs) which may be detected by appropriate sensors. A set of experiments were designed, constructed and conducted at University of Warwick in which the state-of-the-art gas sensors namely Electronic Nose (EN) and Field Asymmetric Ion

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Macey, Richard John. "Detection of common dental diseases by dental hygiene-therapists." Thesis, University of Manchester, 2016. https://www.research.manchester.ac.uk/portal/en/theses/detection-of-common-dental-diseases-by-dental-hygienetherapists(2a09e2bb-4d00-4c88-9f42-15a17a62ca7a).html.

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Thesis submitted to the University of Manchester by Richard Macey for the Degree of Doctor of Philosophy entitled “Detection of Common Dental Diseases by Dental Hygiene-Therapists”, February 2016.Many adult patients that attend NHS dental practices on a regular basis are asymptomatic and do not need any further treatment other than a routine dental examination (“check-up”). As the oral health of the adult population is predicted to improve further, using the General Dental Practitioner to undertake the “check-up” on regular “low risk” patients represents a substantial and potentially unnecessa

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Benjeddou, Mongi. "Molecular detection and genetic manipulation of the Black Queen Cell Virus." Thesis, University of the Western Cape, 2002. http://etd.uwc.ac.za/index.php?module=etd&amp.

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The South African isolate of the Black Queen-Cell Virus (BQCV), a honeybee virus, was previously found to have an 8550 nucleotide genome excluding the poly(A) tail. Its genome contained two ORFs, a 5'-proximal ORF encoding a putative replicase protein and a 3'-proximal ORF encoding a capsid polyprotein.<br /> <br /> A reverse transcriptase PCR (RT -PCR) assay was developed for the detection of BQCV and acute bee-paralysis virus (ABPV). Complete genomes sequences w ere used to design unique PCR primers within a l-kb region from the 3' end of both genomes to amplify a fragment of 70.0 bp from BQ

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Ouellette, Eric. "Volatile profiles for disease detection in stored carrots and potatoes." Thesis, McGill University, 1988. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=63898.

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Owen, Helen Clare. "Detection and characterization of rickettsiae in Western Australia." Thesis, Owen, Helen Clare (2007) Detection and characterization of rickettsiae in Western Australia. PhD thesis, Murdoch University, 2007. https://researchrepository.murdoch.edu.au/id/eprint/240/.

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The aim of this study was to address the shortfall in current, in-depth knowledge of Western Australian rickettsiae investigating in particular, the role of native and feral animals as reservoir hosts. Two novel species of rickettsiae were detected in ticks collected from native and feral animals and humans, these were characterised using molecular methods. A preliminary investigation into the pathogenic potential of one of the isolates was also undertaken. To facilitate investigation, ectoparasites (principally ticks) were opportunistically collected from across the State, with an emphas

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Books on the topic "Detection of arecanut diseases"

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Gellerich, Frank Norbert, and Stephan Zierz, eds. Detection of Mitochondrial Diseases. Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-6111-8.

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N, Gellerich Frank, Zierz S, and Colloquium on Mitochondria and Myopathies (1st : 1995 : Halle an der Saale, Germany), eds. Detection of mitochondrial diseases. Kluwer Academic, 1997.

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Organization, World Health, ed. Early detection of occupational diseases. World Health Organization, 1986.

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American Society of Health-System Pharmacists, ed. Drug-induced diseases: Prevention, detection, and management. 2nd ed. American Society of Health-System Pharmacists, 2010.

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L, Wiedbrauk Danny, and Farkas Daniel H, eds. Molecular methods for virus detection. Academic Press, 1995.

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North Central Forest Experiment Station (Saint Paul, Minn.), ed. Prevention, detection, and control of nursery tree diseases. U.S. Dept. of Agriculture, Forest Service, North Central Forest Experiment Station, 1989.

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V, Avioli Louis, ed. The Osteoporotic syndrome: Detection, prevention, and treatment. 3rd ed. Wiley-Liss, 1993.

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Liu, Dongyou. Molecular detection of human viral pathogens. CRC Press, 2011.

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Liu, Dongyou. Molecular detection of human viral pathogens. CRC Press, 2011.

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Narayanasamy, P. Plant pathogen detection and disease diagnosis. Marcel Dekker, 1997.

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Book chapters on the topic "Detection of arecanut diseases"

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Parthasarathy, S., R. Kalaivanan, R. Sangavi, and P. Lakshmidevi. "Arecanut." In Pests and Diseases in Spices, Plantation and Tuber Crops. CRC Press, 2025. https://doi.org/10.1201/9781003623779-2.

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Berger, R. "Substrate Detection." In Inborn Metabolic Diseases. Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/978-3-662-02613-7_3.

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Heuck, F. H. W. "Radiological Detection of Osteoporosis." In Generalized Bone Diseases. Springer Berlin Heidelberg, 1987. http://dx.doi.org/10.1007/978-3-642-73346-8_2.

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Xi, Hui, and Yang Zhang. "Aptamer Detection of Neurodegenerative." In Neurodegenerative Diseases Biomarkers. Springer US, 2021. http://dx.doi.org/10.1007/978-1-0716-1712-0_15.

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Seth, Vishal, Rajeev Paulus, Mayur Kumar, and Anil Kumar. "Tomato Leaf Diseases Detection." In Lecture Notes in Electrical Engineering. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-1677-9_5.

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Chaturvedi, Saumya, Khushboo Arya, Sana Akhtar Usmani, et al. "Mucormycosis Infection and Methods of Detection." In Human Fungal Diseases. CRC Press, 2024. http://dx.doi.org/10.1201/9781032642864-9.

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Bank, William, and Britton Chance. "Diagnosis of defects in oxidative muscle metabolism by non-invasive tissue oximetry." In Detection of Mitochondrial Diseases. Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-6111-8_1.

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Mildaziene, Vida, Rasa Baniene, Ausra Marcinkeviciute, Zita Nauciene, Alvydas Kalvenas, and Aurelijus Zimkus. "Tetraphenylphosphonium inhibits oxidation of physiological substrates in heart mitochondria." In Detection of Mitochondrial Diseases. Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-6111-8_10.

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Sperl, Wolfgang, Daniela Skladal, Erich Gnaiger, et al. "High resolution respirometry of permeabilized skeletal muscle fibers in the diagnosis of neuromuscular disorders." In Detection of Mitochondrial Diseases. Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-6111-8_11.

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Kay, Laurence, André Rossi, and Valdur Saks. "Detection of early ischemic damage by analysis of mitochondrial function in skinned fibers." In Detection of Mitochondrial Diseases. Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-6111-8_12.

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Conference papers on the topic "Detection of arecanut diseases"

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G, Madhu B., Ram kumar G, Shreehari S. Rao, Chandrashekara M., and Amith Shetty A. R. "Detection Of Diseases In Arecanut Using Convolutional Neural Network." In 2024 Second International Conference on Advances in Information Technology (ICAIT). IEEE, 2024. http://dx.doi.org/10.1109/icait61638.2024.10690427.

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Manzoor, Er Suhail, Radhika Malhotra, Rahul Bhat, and Shivam Shekhar. "Apple Detection: A CNN Approach for Diseases Detection." In 2024 Second International Conference on Advances in Information Technology (ICAIT). IEEE, 2024. http://dx.doi.org/10.1109/icait61638.2024.10690497.

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Archana, P., Suhana Anjum S, Keerthana S M, Srusti P D, and Suhana Anjum S. "Detection of Thyroid Diseases Using Deep Learning." In 2024 Second International Conference on Advances in Information Technology (ICAIT). IEEE, 2024. http://dx.doi.org/10.1109/icait61638.2024.10690770.

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Sofiane, Abden, Bendjima Mostefa, Benkrama Soumia, Gasbaoui Mohammed El Amin, and Kourtiche Ikram. "Proposed Deep Learning Approach for Plant Diseases Detection." In 2024 4th International Conference on Embedded & Distributed Systems (EDiS). IEEE, 2024. https://doi.org/10.1109/edis63605.2024.10783192.

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Jain, Paras, Vishan Kumar Gupta, Garima Sharma, Gamini Dhiman, Jaishree Meena, and Mukesh Kumar Singh. "Detection of Cucumber Diseases using Deep Learning models." In 2024 5th IEEE Global Conference for Advancement in Technology (GCAT). IEEE, 2024. https://doi.org/10.1109/gcat62922.2024.10924118.

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Patil, Sayali, Sairaj Shinde, Ashran Pathan, and Harsha Bhute. "Detection of Animal Skin Diseases Lumpy Using CNN." In 2025 1st International Conference on AIML-Applications for Engineering & Technology (ICAET). IEEE, 2025. https://doi.org/10.1109/icaet63349.2025.10932239.

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Kumar, Ashwini Sudheer, Reza Roofegari Nejad, and Sahar Hooshmand. "Vision Transformers in Early Detection of Cardiovascular Diseases." In 2024 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2024. https://doi.org/10.1109/icmlc63072.2024.10935159.

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Kholiya, Deepak, Arnav Pandey, Neeraj Kumar Pandey, Ankur Dumka, Amit Kumar Mishra, and Piyush Paliwal. "Detection of Potato Plant Diseases Using Deep Learning." In 2024 International Conference on Computer, Electronics, Electrical Engineering & their Applications (IC2E3). IEEE, 2024. https://doi.org/10.1109/ic2e362166.2024.10826838.

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Aly, Moustafa H., Loaa Ahmed Mohamed, Wessam M. Salama, et al. "Skin Diseases Detection Based on Deep Learning Models." In 2023 33rd International Conference on Computer Theory and Applications (ICCTA). IEEE, 2023. https://doi.org/10.1109/iccta60978.2023.10969247.

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Ocampo, Kevin, and Maria Jose Londono. "Tomato Plant Diseases Detection Using Convolutional Neural Networks." In 2024 IEEE Central America and Panama Student Conference (CONESCAPAN). IEEE, 2024. https://doi.org/10.1109/conescapan62181.2024.10891119.

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Reports on the topic "Detection of arecanut diseases"

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Nicholls, Thomas H. Prevention, Detection, and Control of Nursery Tree Diseases. U.S. Department of Agriculture, Forest Service, North Central Forest Experiment Station, 1989. http://dx.doi.org/10.2737/nc-rn-348.

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Jordan, Ramon L., Abed Gera, Hei-Ti Hsu, Andre Franck, and Gad Loebenstein. Detection and Diagnosis of Virus Diseases of Pelargonium. United States Department of Agriculture, 1994. http://dx.doi.org/10.32747/1994.7568793.bard.

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Pelargonium (Geranium) is the number one pot plant in many areas of the United States and Europe. Israel and the U.S. send to Europe rooted cuttings, foundation stocks and finished plants to supply a certain share of the market. Geraniums are propagated mainly vegetatively from cuttings. Consequently, viral diseases have been and remain a major threat to the production and quality of the crop. Among the viruses isolated from naturally infected geraniums, 11 are not specific to Pelargonium and occur in other crops while 6 other viruses seem to be limited to geranium. However, several of these v

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Chilvers, Marin, Austin McCoy, Zachary Noel, et al. Detection and Prevalence of Oomycete Seedling Diseases on Soybean. Crop Protection Netework, 2020. http://dx.doi.org/10.31274/20200918-1.

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Alper, Miriam, Roger Lawson, Adina Stein, William Zettler, and Gad Loebenstein. Detection and Control of Viral Diseases in Gladiolus, Bulbous Irises and Lilies. United States Department of Agriculture, 1985. http://dx.doi.org/10.32747/1985.7598138.bard.

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Kamp, Jan, Pieter Blok, Gerrit Polder, Jan van der Wolf, and Henk Jalink. Smart disease detection seed potatoes 2015-2018 : Detection of virus and bacterial diseases using vision and sensor technology. Stichting Wageningen Research, Wageningen Plant Research, Business Unit Field Corps, 2020. http://dx.doi.org/10.18174/494707.

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Krishnadas, P., and N. Smith. An investigation of available medical imaging data for the early detection of neurodegenerative diseases. National Physical Laboratory, 2023. http://dx.doi.org/10.47120/npl.ms50.

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Kleman, Isabella. Onion storage diseases and their headspace volatiles. Faculty of Landscape Architecture, Horticulture and Crop Production Science, Swedish University of Agricultural Sciences, 2023. http://dx.doi.org/10.54612/a.602791tdo5.

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Onion, Allium cepa, is one of the world’s most commonly produced and consumed vegetables. In order to be available year round in temperate climates onions must be stored for several months. During this time parts of the harvested weight of bulbs are lost to storage diseases, sprouting and respiration leading to loss of carbohydrates and water. Diseases developing in storage may be difficult to spot at early stages as bulbs are typically stored in large bins. However, storage diseases can change the volatile metabolite profile of the infected onions. Electronic sensors that detect the concentra

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Davis, Cristina, Amots Hetzroni, Alexander Aksenov, Michael J. Delwiche, Victoria Soroker, and Victor Alchanatis. Development of a universal volatile compound detection technology for early recognition of pests and diseases in fruit trees. United States Department of Agriculture, 2015. http://dx.doi.org/10.32747/2015.7600016.bard.

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Davis, Robert E., Edna Tanne, James P. Prince, and Meir Klein. Yellow Disease of Grapevines: Impact, Pathogen Molecular Detection and Identification, Epidemiology, and Potential for Control. United States Department of Agriculture, 1994. http://dx.doi.org/10.32747/1994.7568792.bard.

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Abstract:

Grapevine yellows diseases characterized by similar symptoms have been reported in several countries including Israel, the United States, France, Italy, Spain, Germany and Australia. These diseases are among the most serious known in grapevine, but precise knowledge of the pathogens' identities and modes of their spread is needed to devise effective control stratgegies. The overall goals of this project were to develop improved molecular diagnostic procedures for detection and identification of the presumed mycoplasmalike organism (MLO) pathogens, now termed phytoplasmas, and to apply these pr

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Boronina, Liubov. Electronic course in bacteriology. Федеральное государственное бюджетное образовательное учреждение высшего образования "Уральский государственный медицинский университет" Министерства здравоохранения Российской Федерации, 2024. https://doi.org/10.12731/er0865.12122024.

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Microbiological diagnosis of the etiology of infections is part of the diagnosis of infectious diseases. The elective course presents lectures, methodological manuals, practical aspects of the detection of microorganisms that cause actual human infections, the detection of which with pathological material causes difficulties due to the peculiarities of the biological properties of pathogens.

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