Relevant bibliographies by topics / Leaf disease detection

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Leaf disease detection'

Author: Grafiati
Published: 4 June 2025
Last updated: 15 July 2025

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Journal articles on the topic "Leaf disease detection"

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Shendye, Yogeshwar. "Cassava Plant Leaf Disease Detection." International Journal of Science and Research (IJSR) 10, no. 7 (2021): 907–10. https://doi.org/10.21275/sr21716223603.

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Pore, Prof Yogita, Suraj Teli, Swaraj Ghuge, and Nikhil Patil. "Leaf Disease Detection." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (2023): 1767–70. http://dx.doi.org/10.22214/ijraset.2023.51405.

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Abstract: Early disease identification is crucial for productive crop production in agriculture. illnesses such as bacterial spot, late blight, Septoria leaf spot, and yellow curved leaf the quality of the tomato harvest. Automatic classification techniques of plant diseases also assist in taking action once they are discovered diseased leaf symptoms Presented below is a Convolutional Learning Vector Quantization and Neural Network (CNN) model Method for detecting tomato leaf disease based on the (LVQ) algorithm and categorization. There are 500 tomato photos in the dataset. leaves that displa
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Akhtar, Afrin, and Mithlesh Kumar. "Plant Leaf Disease Detection: Review Report." International Journal of Science and Research (IJSR) 14, no. 4 (2025): 450–52. https://doi.org/10.21275/sr25403132253.

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Mohan, Dr K. Madan. "LEAF DISEASE PREDICTION." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 12 (2023): 1–6. http://dx.doi.org/10.55041/ijsrem27703.

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Leaf diseases are a major problem in agriculture, causing significant losses in crop yield and quality. Early detection of leaf diseases is essential for effective management, but it can be difficult and time- consuming to do manually. In recent years, there has been growing interest in the use of machine learning and computer vision techniques for leaf disease prediction. These techniques can be used to automatically extract features from leaf images that are indicative of disease, and then use these features to train a classifier that can distinguish between healthy and diseased leaves. Seve
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Gupta, Sahil, Vivek Pandey, Pravesh Pandey, Mukul Verma, and Hasib Shaikh. "Leaf Disease Detection System." International Journal for Research in Applied Science and Engineering Technology 11, no. 4 (2023): 1603–12. http://dx.doi.org/10.22214/ijraset.2023.50439.

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Abstract: Agricultural productivity is something on which economy highly depends. This is the one of the reasons that disease detection in plants plays an important role in agriculture field, as having disease in plants are quite natural. If proper care is not taken in this area then it causes serious effects on plants and due to which respective product quality, quantity or productivity is affected. For instance a disease named little leaf disease is a hazardous disease found in pine trees in United States. Detection of plant disease through some automatic technique is beneficial as it reduce
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M, Megha. "Tomato Leaf Disease Detection and Monitoring System." International Journal of Science and Research (IJSR) 11, no. 7 (2022): 1746–49. http://dx.doi.org/10.21275/sr22719075340.

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Tharkar, Rushikesh. "PLANT LEAF DISEASE DETECTION." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem31382.

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Plant diseases affect the growth of their respective species, therefore their early identification is very important. Many Machine Learning (ML) models have been employed for the detection and classification of plant diseases but, after the advancements in a subset of ML, that is, Deep Learning (DL), this area of research appears to have great potential in terms of increased accuracy. Many developed/modified DL architectures are implemented along with several visualization techniques to detect and classify the symptoms of plant diseases. Moreover, several performance metrics are used for the e
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Kamble, Ms D. R., Ms Snehal Krishna Gadale, Ms Dhanashri Nitin Pawar, Ms Gauri Dhanaji Shedage, and Ms Mukti Mahajan. "Leaf Disease Detection System." International Journal for Research in Applied Science and Engineering Technology 12, no. 3 (2024): 160–64. http://dx.doi.org/10.22214/ijraset.2024.58699.

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Abstract: The agricultural sector plays a crucial role in sustaining the world's growing population. However, the prevalence of plant leaf diseases can significantly impact crop yields and quality. This project leverages the power of deep learning techniques to develop an automated system for the early detection of plant leaf diseases. By utilizing a large dataset of annotated leaf images and state-of-the-art convolutional neural networks (CNNs), this research aims to accurately identify and classify various plant leaf diseases, including but not limited to fungal, bacterial, and viral infecti
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Singh, Shivangi. "Leaf Disease Detection." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (2021): 3324–29. http://dx.doi.org/10.22214/ijraset.2021.36836.

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Agriculture is a key source of livelihood. Agriculture provides employment opportunities for village people on a large scale in developing countries like India. India's agriculture consists of the many crops and consistent with survey nearly 70% population is depends on agriculture. Most of Indian farmers are adopting manual cultivation thanks to lagging of technical knowledge. Farmers are unaware of what quite crops that grows well on their land. When plants are suffering from heterogeneous diseases through their leaves which will effect on the production of agriculture and profitable loss, a
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Monigari, Vaishnavi. "Plant Leaf Disease Prediction." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (2021): 1295–305. http://dx.doi.org/10.22214/ijraset.2021.36582.

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The Indian economy relies heavily on agriculture productivity. A lot is at stake when a plant is struck with a disease that causes a significant loss in production, economic losses, and a reduction in the quality and quantity of agricultural products. It is crucial to identify plant diseases in order to prevent the loss of agricultural yield and quantity. Currently, more and more attention has been paid to plant diseases detection in monitoring the large acres of crops. Monitoring the health of the plants and detecting diseases is crucial for sustainable agriculture. Plant diseases are challen
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Dissertations / Theses on the topic "Leaf disease detection"

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Wallace, Sara Elizabeth. "Detection and Diagnosis of Red Leaf Diseases of Grapes ( Vitis spp.) in Oklahoma." Thesis, Oklahoma State University, 2019. http://pqdtopen.proquest.com/#viewpdf?dispub=10846056.

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<p> The grape industry in Oklahoma was valued at $98 million in 2010. In 2015, symptoms resembling Grapevine Leafroll disease were observed, but Grapevine Leafroll-associated Viruses were not detected using enzyme-linked immunosorbent assay (ELISA). A 2-year Cooperative Agricultural Pest Survey was initiated to determine the etiology of the red leaf symptoms in Oklahoma vineyards. In 2016, a total of 121 symptomatic grapevines from 13 counties were sampled and 96 symptomatic grapevines from 14 counties were sampled in 2017. Each sample was tested for Grapevine Red Blotch Virus (GRBV), <i>Xylel
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Khethisa, Joang Adolf. "A highly accessible application for detection and classification of maize foliar diseases from leaf images." Master's thesis, University of Cape Town, 2017. http://hdl.handle.net/11427/25359.

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Crop diseases are a major impediment to food security in the developing world. The development of cheap and accurate crop diagnosis software would thus be of great benefit to the farming community. A number of previous studies, utilizing computer vision and machine-learning algorithms, have successfully developed applications that can diagnose crop diseases. However, these studies have primarily focussed either on developing large scale remote sensing applications more suited for large scale farming or on developing desktop/laptop applications and a few others on developing high end smartphone
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Haaning, Allison M. "Detection of odontoglossum ringspot virus in inoculated orchid leaf tissue using SYBR green real-time RT-PCR." Virtual Press, 2007. http://liblink.bsu.edu/uhtbin/catkey/1371842.

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Odontoglossum ringspot virus (ORSV) is one of the most prevalent orchid viruses that infects greenhouse-grown orchids worldwide. In order to prevent the spread of viruses in greenhouses and to cultivate clones from virus-free mother plants, it is necessary to develop a more sensitive technique for the detection of viruses in orchids. SYBR green real-time RT-PCR is a highly sensitive technique that can specifically detect ORSV in orchid tissue. By harvesting tissue at the inoculation site and at specific distances from the inoculation site at different times past inoculation, this technique can
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Dhau, Inos. "Detection, identification, and mapping of maize streak virus and grey leaf spot diseases of maize using different remote sensing techniques." Thesis, University of Limpopo, 2019. http://hdl.handle.net/10386/2866.

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Thesis (PhD. (Geography)) --University of Limpopo, 2019<br>Of late climate change and consequently, the spread of crop diseases has been identified as one of the major threat to crop production and food security in subSaharan Africa. This research, therefore, aims to evaluate the role of in situ hyperspectral and new generation multispectral data in detecting maize crop viral and fungal diseases, that is maize streak virus and grey leaf spot respectively. To accomplish this objective; a comparison of two variable selection techniques (Random Forest’s Forward Variable, (FVS) and Guided Regulari
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Mkhize, Thokozani M. "The detection of cherry leaf-roll nepovirus and the use of molecular markers for germplasm identification in walnuts (Juglans regia L.)." Thesis, Stellenbosch : Stellenbosch University, 2003. http://hdl.handle.net/10019.1/53624.

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Thesis (MSc)--Stellenbosch University, 2003.<br>ENGLISH ABSTRACT: The aim of this study was to combine two common diagnostic tools: serological kits and genetic fingerprinting to identify cherry leaf-roll nepovirus (CLRV), and to establish a marker system to characterize walnut germplasm. The detection of plant viruses is difficult. Restrictions are imposed for quarantine purposes on the importation of plant material from foreign countries. Modern techniques such as a PCR based screening method for CLRV are required to ensure material do not harbour viruses. A primer pair was designed t
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Gaetani, Robin Andréa. "Techniques de mesure à distance par laser de facteurs physiologiques chez les plantes." Electronic Thesis or Diss., Lyon, INSA, 2023. http://www.theses.fr/2023ISAL0122.

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La prolifération des parasites agricoles est la principale raison de l’utilisation des traitements phytosanitaires. Un nombre croissant d’études ont montré leur impact négatif sur l’environnement et la santé. C’est pourquoi la transition vers une agriculture durable a commencé. La détection précoce des maladies des plantes est donc prometteuse afin de réduire leur utilisation. En outre, la détection précoce des conditions nécessaires au développement de la maladie est encore meilleure. Les maladies cryptogamiques ont généralement besoin d’eau pour proliférer, comme le mildiou dans les vignoble
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Cheng, Huai-Jung, and 鄭淮嶸. "Preliminary Studies on Development of Inoculation and Detection Method and Control Strategies for Citrus Tatter Leaf Disease, Guava Fruit Spot Disease and Grapevine Leafroll Disease." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/g55t24.

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碩士<br>國立臺灣大學<br>植物醫學碩士學位學程<br>105<br>Fruit trees are the most important crops for the agriculture production in Taiwan. Many fruit varieties are cultivated in Taiwan geographically located in the tropical and subtropical areas with plenty of rain, high temperature and high moisture. The major problems for the fruit production in Taiwan are diseases and insect pests. In this thesis, we conducted three case studies of important fruit tree diseases based on the targets of plant medicine. The first case is citrus tatter leaf disease, which has occurred for a long time in Taiwan and considered to b
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Wang, Yu-Sheng, and 王昱勝. "Detection and Classification of Leaf Diseases using Texture and Color Feature." Thesis, 2019. http://ndltd.ncl.edu.tw/cgi-bin/gs32/gsweb.cgi/login?o=dnclcdr&s=id=%22107NCHU5441026%22.&searchmode=basic.

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碩士<br>國立中興大學<br>電機工程學系所<br>107<br>Using the leaf characteristics of plants to carry out disease analysis and detection, not only contributes to the development of agricultural automation, but also monitors the growth of plants in an instant, and early detection of pests and diseases, thereby increasing crop yields. Traditionally artificial It is very time-consuming and laborious to carry out crop diseases. Recently, in the research of automatic detection of diseases by plants, it is common practice to first cut out the lesions in the leaves, and the texture color is then passed through an appr
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Permaul, Kugenthiren. "Molecular characterisation and detection of xanthomonas albilineans, the sugarcane leaf scald pathogen." Thesis, 1994. http://hdl.handle.net/10413/5077.

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Tseng, Yi-Li, and 曾乙立. "Early Detection of Ischemic Heart Disease Using Multi-lead ECG and Heart Sounds." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/87080356466389715644.

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博士<br>國立臺灣大學<br>醫學工程學研究所<br>100<br>Ischemic heart disease has become the first place of ten leading causes of death for many years. According to the statistic results from WHO, up to 16% of mortality is due to ischemic heart disease. The main reason of high death rate is its lack of early symptoms. Patients suffer from sudden death only after a short period of the occurring of acute coronary syndromes. Some even die without any early symptoms. Therefore, early detection of myocardial ischemia has become an important issue recently. In this study, we implemented a non-invasive 12-lead electroca
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Books on the topic "Leaf disease detection"

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Clement, Jan, and Piet Maes. Hantaviral infections. Edited by Vivekanand Jha. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780199592548.003.0188_update_001.

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Hantavirus disease is a viral zoonosis, caused by inhalation of infectious aerosolized excreta from chronically infected rodents, which are both the reservoir and the vector of different hantavirus species. Hantavirus infections manifest mainly as haemorrhagic fever with renal syndrome and hantavirus pulmonary syndrome, which traditionally but incorrectly were thought to be caused by exclusively Old World hantaviruses and New World hantaviruses, respectively.Hantavirus diseases are characterized by non-specific flu-like symptoms, followed by a sometimes lethal capillary leak syndrome, haemorrh
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Chang, Jung-San, and Hung-Chun Chen. Dengue and other viral haemorrhagic fevers. Edited by Vivekanand Jha. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780199592548.003.0189_update_001.

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Human infections of dengue viruses are mediated by mosquito bites. The disease spectrum ranges from asymptomatic infection to severe dengue haemorrhagic fever, and dengue shock syndrome. Dengue virus infects a wide range of immune cells that lead to plasma leakage and dengue haemorrhagic fever. Dengue haemorrhagic fever can be complicated with renal involvement, including haematuria, proteinuria, glomerulonephritis, acute kidney injury, and haemolytic uraemic syndrome. Diagnosis can be made by serology, detection of viral proteins by Western blot assay, or detection of the dengue viral genome
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Abdallah, Roshan. Evaluation of DNA hybridization probes for detecting Xanthomonas campestris pv. vesicatoria and analysis of genomic diversity by RFLP techniques. 1993.

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Lung Cancer. Exon Publications, 2024. https://doi.org/10.36255/lung-cancer.

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Lung cancer is a serious and common disease that begins in the lungs, often caused by smoking, environmental exposures, or genetic factors. This article discusses lung cancer, offering information for patients, caregivers, and the general public. It begins by explaining what lung cancer is, its types, and the risk factors associated with its development. The article explores symptoms and how the disease progresses, along with the challenges of early detection. Key sections cover the pathophysiology of lung cancer, detailing how genetic mutations and environmental factors lead to tumor growth.
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Kaushik, Sanket, and Nagendra Singh, eds. Current Developments in the Detection and Control of Multi Drug Resistance. BENTHAM SCIENCE PUBLISHERS, 2022. http://dx.doi.org/10.2174/97898150498791220101.

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The rise in the incidence of infections is caused by multi drug resistant (MDR) bacteria, it is essential to elucidate the basic mechanism of antibiotic resistance to discover effective methods for diagnosis and treatment of infections. The use of pathogen-specific probes offers a faster alternative for pathogen detection and could improve the diagnosis of infection. High resolution melting analysis techniques are useful for the detection of multi drug resistant pathogens. Rational Structural Based Drug Design is a common method to identify a lead compound and take it forward for further devel
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van Gaalen, Floris, Désirée van der Heijde, and Maxime Dougados. Diagnosis and classification of axial spondyloarthritis. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780198734444.003.0003.

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Axial spondyloarthritis (axSpA) is a potentially disabling chronic inflammatory disease affecting the spine and sacroiliac (SI) joints. Lead symptoms are chronic back pain and stiffness. The disease is called radiographic axSpA or ankylosing spondylitis (AS) when, on plain radiographs, bone changes consistent with sacroiliitis are present. When no evidence of sacroiliitis is seen on radiographs, it is called non-radiographic axSpA. In such cases, diagnosis is made based on evidence of active inflammation of SI joints on magnetic resonance imaging (MRI) and clinical and laboratory features, or
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Eckert, J., P. Deplazes, and P. Kern. Alveolar echinococcosis (Echinococcus multilocularis). Oxford University Press, 2011. http://dx.doi.org/10.1093/med/9780198570028.003.0061.

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In this chapter three forms of echinococcosis in humans are described that are caused by a larval stage (metacestode) of Echinococcus multilocularis Leuckart, 1863, Echinococcus oligarthrus (Diesing, 1863) or Echinococcus vogeli Rausch and Bernstein, 1972. E. multilocularis is the causative agent of alveolar echinococcosis (AE). In the human host the metacestode of E. multilocularis behaves like a malignant tumour, characterized by infiltrative proliferation and the potential to induce serious disease. The liver is nearly exclusively the primary site of metacestode development, but metastases
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Mason, Peggy. The Vestibular Sense. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190237493.003.0018.

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The vestibular system contains semicircular canals that respond to angular acceleration and otoconial organs that respond to linear acceleration of the head. Information is sent to the motor system and, under normal circumstances, does not lead to conscious perception. Yet damage to the vestibular system can result in disequilibrium or vertigo, disturbing perceptions that dominate conscious experience. The shared residence of the cochlear and vestibular end organs in the inner ear can give rise to inner ear disorders such as Ménière’s disease. The effect of gravity on the otoconial masses in t
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Harper, Lorraine, and David Jayne. The patient with vasculitis. Edited by Giuseppe Remuzzi. Oxford University Press, 2015. http://dx.doi.org/10.1093/med/9780199592548.003.0160.

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The goals of treatment in renal vasculitis are to stop vasculitic activity and recover renal function. Subsequent strategies are required to prevent vasculitis returning and to address longer-term co-morbidities caused by tissue damage, drug toxicity, and increased cardiovascular and malignancy risk.Cyclophosphamide and high-dose glucocorticoids remain the standard induction therapy with alternative immunosuppressives, such as azathioprine, to prevent relapse. Plasma exchange improves renal recovery in severe presentations. Refractory disease resulting from a failure of induction or remission
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Book chapters on the topic "Leaf disease detection"

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Indrani, P., Ganesh B. Regulwar, Md Sohel Ahmed, and K. Sneha Reddy. "Leaf Disease Detection and Correction." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-8031-0_42.

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Sesha Talpa Sai, P. H. V., M. Sucharitha, Nithul Prakash, et al. "Plant Leaf Disease Detection System." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-97-9926-8_45.

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Bade, Ashwini V., and M. Suresh Kumar. "Crop Leaf Disease Detection Using DCCN." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-0763-8_5.

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Saharan, Mohit, and Ghanapriya Singh. "Leaf Disease Detection Using Transfer Learning." In Communications in Computer and Information Science. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-47997-7_4.

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Srinivasa Ravi Kiran, T., D. Sri Naga Prasanna, and A. Srisaila. "Leaf Disease Detection Using Deep Learning." In Smart Innovation, Systems and Technologies. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-6810-3_3.

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Kumar, Amit, Manish Kumar Priyanshu, Rani Singh, and Snigdha Sen. "Plant Leaf Disease Detection Using ResNet." In Soft Computing: Theories and Applications. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-9858-4_74.

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Jeswani, Jaya, Ansari Saud Ahmed, Khan Zaid, and Russel Fernandes. "Leaf Disease Detection Using Deep Learning." In Advances in Computer Science Research. Atlantis Press International BV, 2023. http://dx.doi.org/10.2991/978-94-6463-136-4_87.

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Sudharshan, B. Sai, S. V. Deepan Avinaas, U. Sujith, and G. Anitha. "Rice Leaf Disease Detection using YOLOv9." In Advances in Engineering Research. Atlantis Press International BV, 2025. https://doi.org/10.2991/978-94-6463-762-5_6.

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Accotto, Gian Paolo, and Emanuela Noris. "Detection methods for TYLCV and TYLCSV." In Tomato Yellow Leaf Curl Virus Disease. Springer Netherlands, 2007. http://dx.doi.org/10.1007/978-1-4020-4769-5_14.

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Shah, Vrishani, Mustafa Poonawala, Apoorva Shastri, and Sujit Naik. "Leaf Disease Detection Using Fuzzy Edge Detection and Clustering." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-97-9839-1_2.

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Conference papers on the topic "Leaf disease detection"

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Bhaskar, J., V. Nidheesh Kumar, L. Vineetha Naidu, R. Divya Reddy, T. Bhargavi, and Sumathi D. "Paddy Leaf Disease Detection." In 2024 10th International Conference on Advanced Computing and Communication Systems (ICACCS). IEEE, 2024. http://dx.doi.org/10.1109/icaccs60874.2024.10717015.

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Baurai, Ayush, Priyansha Rawat, Abhay Narayan Singh, Vihan Singh Bhakuni, Ayush, and Manika Manwal. "Leaf Disease Detection Using KNN." In 2024 4th International Conference on Technological Advancements in Computational Sciences (ICTACS). IEEE, 2024. https://doi.org/10.1109/ictacs62700.2024.10840461.

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V, Mekala, Muhilan R, Ragul R K, and Rithika C. "Mulberry Leaf Disease Detection System." In 2025 3rd International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT). IEEE, 2025. https://doi.org/10.1109/idciot64235.2025.10915157.

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Saraswathi, S., J. Lekha, D. Suryaprabha, and Noel Mathew Thomas. "Leaf Disease Detection using Machine Learning." In 2024 International Conference on Trends in Quantum Computing and Emerging Business Technologies (TQCEBT). IEEE, 2024. http://dx.doi.org/10.1109/tqcebt59414.2024.10545161.

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Kumar, D. Hemanth, and Samundiswary Punniakodi. "Rice Leaf Disease Detection Using ResNet50." In 2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication (IConSCEPT). IEEE, 2024. http://dx.doi.org/10.1109/iconscept61884.2024.10627835.

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A, Abirami, Prabhu K, Kaliraj V, Mahesh Vaijainthymala Krishnamoorthy, and Dharaniya R. "Plant Leaf Disease Detection Using CNN." In 2024 International Conference on Smart Technologies for Sustainable Development Goals (ICSTSDG). IEEE, 2024. https://doi.org/10.1109/icstsdg61998.2024.11026396.

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Lamani, Gagan Deep, T. M. Gagan, Shankaraling Halemani, K. Fayaz, and Meenaxi M. Raikar. "Tomato Leaf Disease Detection using Federated Learning." In 2024 4th International Conference on Intelligent Technologies (CONIT). IEEE, 2024. http://dx.doi.org/10.1109/conit61985.2024.10626775.

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Garba, Ahmad Ahmad, Vihsal Jain, and Kanika Singla. "Tomato Leaf Disease Detection Using CNN Models." In 2024 4th International Conference on Technological Advancements in Computational Sciences (ICTACS). IEEE, 2024. https://doi.org/10.1109/ictacs62700.2024.10840660.

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Prashanth, R., K. Deepthi, Puthran Roshan Ravi, M. B. Sharath, and K. Aditya Shastry. "Cotton Leaf Disease Detection Using Transfer Learning." In 2024 Second International Conference on Networks, Multimedia and Information Technology (NMITCON). IEEE, 2024. http://dx.doi.org/10.1109/nmitcon62075.2024.10698916.

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Nanthini, P., M. Jagadeesan, T. M. Saravanan, P. A. Selvaraj, E. Mohamed Farque, and V. Yuvan Shankar. "Brinjal Leaf Disease Detection using Deep Learning." In 2024 International Conference on Expert Clouds and Applications (ICOECA). IEEE, 2024. http://dx.doi.org/10.1109/icoeca62351.2024.00134.

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Reports on the topic "Leaf disease detection"

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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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Davidson, Irit, Hsing-Jien Kung, and Richard L. Witter. Molecular Interactions between Herpes and Retroviruses in Dually Infected Chickens and Turkeys. United States Department of Agriculture, 2002. http://dx.doi.org/10.32747/2002.7575275.bard.

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Tumors in commercial poultry are caused mainly by infection with avian herpes and retroviruses, the herpesvirus Marek's disease virus (MDV) and the retroviruses, reticuloendotheliosis (REV), lymphoid leukosis, subgroups A-I and J (ALV and ALV-J) in chickens, or Iymphoprolipherative disease (LPDV) in turkeys. Infection with one virus aggravates the clinical outcome of birds that are already infected by another oncogenic virus. As these viruses do not interfere for infection, MDV and one or more retroviruses can infect the same flock, the same bird and the same cell. While infecting the same cel
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3

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, 2017. http://dx.doi.org/10.32747/2017.7207241.aphis.

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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 d
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