Relevant bibliographies by topics / Computer vision technologies Disease detection

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  2. Dissertations / Theses
  3. Books
  4. Book chapters
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Academic literature on the topic 'Computer vision technologies Disease detection'

Author: Grafiati
Published: 5 June 2025
Last updated: 2 August 2025

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Journal articles on the topic "Computer vision technologies Disease detection"

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Somaiya, Kush Vijay. "PLANT LEAF DISEASE DETECTION." International Scientific Journal of Engineering and Management 04, no. 04 (2025): 1–7. https://doi.org/10.55041/isjem03128.

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ABSTRACT- Agriculture remains a fundamental pillar of many national economies, making the protection of crops from disease a top priority. Pathogens such as bacteria, fungi, and viruses can significantly reduce crop productivity, underscoring the need for timely and accurate disease detection. Recent innovations in computer vision and artificial intelligence have introduced powerful tools for recognizing plant diseases through image analysis, particularly using leaf imagery. This paper investigates the application of machine learning, deep learning, and few-shot learning models in automating d
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Alvarado, Joan, Juan Felipe Restrepo-Arias, David Velásquez, and Mikel Maiza. "Disease Detection on Cocoa Crops Based on Computer-Vision Techniques: A Systematic Literature Review." Agriculture 15, no. 10 (2025): 1032. https://doi.org/10.3390/agriculture15101032.

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Computer vision in the agriculture field aims to find solutions to guarantee and assure farmers the quality of their products. Therefore, studies to diagnose diseases and detect anomalies in crops, through computer vision, have been growing in recent years. However, crops such as cocoa required further attention to drive advances in computer vision to the detection of diseases. As a result, this paper aims to explore the computer vision methods used to diagnose diseases in crops, especially in cocoa. Therefore, the purpose of this paper is to provide answers to the following research questions
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B, Sowmiya, Saminathan K, and M. Chithra Devi. "A COMPREHENSIVE REVIEW ON DIAGNOSIS AND CLASSIFICATION OF PADDY LEAF DISEASES USING ADVANCED COMPUTER VISION TECHNOLOGIES." ICTACT Journal on Image and Video Processing 13, no. 4 (2023): 2973–86. http://dx.doi.org/10.21917/ijivp.2023.0424.

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Food is required for human survival. Paddy is a vital food crop serving 60% of the Indian population. Food quality is determined by the plant yield. Unfavorable environmental circumstances, soil fertility, bacteria, viruses, nematodes, fertilizer use, and the absence of nutritional shortages substantially influence plant yield. As a result, it is critical to protect the plants from illness. Crop yield must be improved to meet food scarcity of growing population. Although disease symptoms are apparent in various parts of plant like leaves, stem, fruits and stem, the infections are commonly obse
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Mudarisov, S. G., and I. R. Miftakhov. "Deep Learning Methods and UAV Technologies for Crop Disease Detection." Agricultural Machinery and Technologies 18, no. 4 (2024): 24–33. https://doi.org/10.22314/2073-7599-2024-18-4-24-33.

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The paper underscores the significant advancements in plant disease diagnostics achieved through the integration of remote sensing technologies and deep learning algorithms, particularly in aerial imagery interpretation. It focuses on evaluating deep learning techniques and unmanned aerial vehicles for crop disease detection. (Research purpose) The study aims to review and systemize scientific literature on the application of unmanned aerial vehicles, remote sensing technologies and deep learning 24 methods for the early detection and prediction of crop diseases. (Materials and methods) The pa
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Chaudhari, Tejas. "Fruit Scan - Disease Identification in Fruits Using Image Processing." International Journal for Research in Applied Science and Engineering Technology 12, no. 12 (2024): 3061–65. http://dx.doi.org/10.22214/ijraset.2024.59572.

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Abstract: The agricultural industry plays a crucial role in sustaining global food security, and the health of fruit crops is paramount in ensuring a steady food supply. Fruit diseases pose a significant threat to crop yield and quality, making their early detection and management essential. In recent years, the integration of technology and artificial intelligence has transformed fruit disease detection, offering more accurate and efficient solutions. This abstract provides an overview of the techniques and challenges associated with fruit disease detection. This review highlights various met
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Madan Mohan Mishra. and Pramod Singh. "Pattern Based Leaves Disease Classification Using AI." International Journal of Latest Technology in Engineering Management & Applied Science 14, no. 6 (2025): 908–14. https://doi.org/10.51583/ijltemas.2025.1406000100.

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Abstract- Artificial Intelligence (AI) is an overarching domain that integrates a variety of techniques, tools, and systems designed to enable machines to learn from data and perform predictive or decision-making tasks. Within this domain, computer vision stands out as a pivotal subfield, offering substantial contributions across multiple sectors, including agriculture. The integration of AI and computer vision has given rise to smart farming an advanced form of agriculture where traditional cultivation practices are optimized through intelligent technologies to enhance productivity, precision
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Akram, Hussain Khan. "Information Technology Usage in Skin Disease Detection." International Journal of Current Science Research and Review 06, no. 07 (2023): 4241–49. https://doi.org/10.5281/zenodo.8137628.

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Abstract : Millions of individuals of all ages are affected by skin diseases, a widespread problem worldwide. Early diagnosis and detection are essential for these diseases to be effectively treated and improve patient outcomes. Automated skin disease detection systems are a viable way to increase diagnostic accuracy and lighten the workload of dermatologists, by developments in machine learning and computer vision. These systems examine skin lesions and categorize them into several disease groups using various techniques, including feature extraction, deep learning, and image processing. Such
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Arcot, S. Chennakeshav, Charkraborty Dipayan, M. Ram Karthik, H. G. Skanda, and Janumala Tabitha. "Advanced Image Processing for Dermatological Disease Detection." Advancement in Image Processing and Pattern Recognition 7, no. 2 (2024): 70–77. https://doi.org/10.5281/zenodo.10700023.

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<em>Skin diseases pose a significant health concern globally, particularly in regions like Saudi Arabia, where desert climates contribute to their prevalence. Despite advancements in medical technology, the cost and accessibility of diagnosing such conditions remain significant barriers. Leveraging cutting-edge image processing techniques, our research aims to address this challenge by proposing a cost-effective and efficient method for skin disease detection. Our approach harnesses the power of digital image analysis, utilizing readily available equipment such as cameras and computers. By emp
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Teslenko, Denys, and Kyrylo Smelyakov. "ROLE AND EVOLUTION OF COMPUTER VISION IN MEDICINE." Grail of Science, no. 37 (March 22, 2024): 211–15. http://dx.doi.org/10.36074/grail-of-science.15.03.2024.030.

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As the technologies evolve, they become more and more applicable to such fields as medicine and sciences. This article explores the transformative role of computer vision in modern medicine. It delves into the evolution and diverse applications of computer vision technology, highlighting its profound impact on medical imaging, diagnostics, and surgical procedures. Through advanced imaging techniques and machine learning algorithms, computer vision enhances diagnostic accuracy, facilitates surgical navigation, and enables real-time analysis of medical data. The article discusses key insights fr
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Wang, Wenqi, and Ye Kang. "A Review of Computer Vision Technologies in Precision Agriculture: From Crop Disease Detection to Farm Management." Theoretical and Natural Science 101, no. 1 (2025): 34–39. https://doi.org/10.54254/2753-8818/2025.ch22224.

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Precision agriculture offers a promising solution to enhance crop productivity and sustainability amidst global agricultural challenges. This paper reviews the development and application of computer vision technologies in modern farming, with a focus on deep learning techniques such as Convolutional Neural Networks (CNNs), including Residual Network (ResNet), You Only Look Once (YOLO), and Segmentation Network (SegNet), applied to disease detection, weed classification, and crop health monitoring. The integration of Unmanned Aerial Vehicles (UAVs), robotics, and the Internet of Things (IoT) h
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Dissertations / Theses on the topic "Computer vision technologies Disease detection"

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Vandi, Mattia. "Detecting Face Masks and Social Distancing Against COVID-19 with Embedded Systems and Deep Learning Technologies." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021.

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Social distancing and face mask wearing have been proven as effective measures against the spread of the infectious COronaVIrus Disease 2019 (COVID-19). However, individuals are still adapting to COVID-19 regulations. In fact, you can often see people in public places wearing face masks incorrectly or not wearing face masks at all, besides not tracking the required two meters (6 feet) distance between themselves and their surroundings. An active surveillance system that can both determine whether or not a person is wearing a face mask and tracking distances between individuals would be able
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Sethunath, Deepak. "Detection of histological features in liver biopsy images to help identify Non-Alcoholic Fatty Liver Disease." Thesis, 2018. https://doi.org/10.7912/C2V36H.

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Indiana University-Purdue University Indianapolis (IUPUI)<br>This thesis explores a minimally invasive approach of diagnosing Non-Alcoholic Fatty Liver disease (NAFLD) on mice and humans which can be useful for pathologists while performing their diagnosis. NAFLD is a spectrum of diseases going from least severe to most severe – steatosis, steatohepatitis, fibrosis and finally cirrhosis. This disease primarily results from fat deposition in the liver which is unrelated to alcohol or viral causes. In general, it affects individuals having a combination of at least three of the five metabolic sy
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Ali, Syed Musharaf. "Fusion of Stationary Monocular and Stereo Camera Technologies for Traffic Parameters Estimation." Doctoral thesis, 2017. https://repositorium.ub.uni-osnabrueck.de/handle/urn:nbn:de:gbv:700-2017030715611.

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Modern day intelligent transportation system (ITS) relies on reliable and accurate estimated traffic parameters. Travel speed, traffic flow, and traffic state classification are the main traffic parameters of interest. These parameters can be estimated through efficient vision-based algorithms and appropriate camera sensor technology. With the advances in camera technologies and increasing computing power, use of monocular vision, stereo vision, and camera sensor fusion technologies have been an active research area in the field of ITS. In this thesis, we investigated stationary monocular and
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De, Feudis Irio. "Enabling technologies for Human-centered Industry 4.0 and Healthcare 4.0." Doctoral thesis, 2022. http://hdl.handle.net/11589/241900.

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Industry 4.0 has transformed the manufacturing industry into a new paradigm causing numerous changes in the models of business and process automation. The profound change in the context of production has brought the issue of efficiency. Some of the key technologies that emerged to tackle this issue are Big Data, Internet of Things (IoT), Digital Twins, Artificial Intelligence, Machine Learning, Augmented Reality and Additive Manufacturing. This revolution has not remained within the borders of the manufacturing field but it pushes changes in a lot of fields; in particular, it has introduced h
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Books on the topic "Computer vision technologies Disease detection"

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Madhu, G., Sandeep Kautish, A. Govardhan, and Avinash Sharma, eds. Emerging Computational Approaches in Telehealth and Telemedicine: A Look at The Post-COVID-19 Landscape. BENTHAM SCIENCE PUBLISHERS, 2022. http://dx.doi.org/10.2174/97898150792721220101.

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This book gives an overview of innovative approaches in telehealth and telemedicine. The Goal of the content is to inform readers about recent computer applications in e-health, including Internet of Things (IoT) and Internet of Medical Things (IoMT) technology. The 9 chapters will guide readers to determine the urgency to intervene in specific medical cases, and to assess risk to healthcare workers. The focus on telehealth along with telemedicine, encompasses a broader spectrum of remote healthcare services for the reader to understand. Chapters cover the following topics: - A COVID-19 care s
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Book chapters on the topic "Computer vision technologies Disease detection"

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Pandey, Sakshi, Kuldeep Kumar Yogi, and Ayush Ranjan. "A Review of Disease Detection Emerging Technologies of Pre and Post harvest Plant Diseases: Recent Developments and Future Prospects." In Computer Vision and Robotics. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-7892-0_3.

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Shrotriya, Neha, and Parthivi Thakore. "Computer Vision (CV)-Aided Medical Diagnosis for Cardiovascular Disease Detection." In Computer Vision and AI-Integrated IoT Technologies in the Medical Ecosystem. CRC Press, 2024. http://dx.doi.org/10.1201/9781003429609-4.

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Patil, Rajesh V., Abhishek M. Thote, and Sandip T. Chavan. "Pre-Detection and Classification of Coronavirus Disease by Artificial Intelligence and Computer Vision." In Pandemic Detection and Analysis Through Smart Computing Technologies. Apple Academic Press, 2022. http://dx.doi.org/10.1201/9781003281610-8.

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Khandelwal, Shekhar, and Rik Das. "Phishing Detection Using Computer Vision." In Computer Networks and Inventive Communication Technologies. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-3728-5_9.

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Chen, Quansheng, Hao Lin, and Jiewen Zhao. "Computer Vision Technology in Food." In Advanced Nondestructive Detection Technologies in Food. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-3360-7_4.

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Bhosale, Jyoti D., and Santosh S. Lomte. "Rice Crop Disease Detection Using Machine Learning Algorithms." In Computer Vision and Robotics. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-4577-1_33.

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Thakur, Poornima Singh, Pritee Khanna, Tanuja Sheorey, and Aparajita Ojha. "Vision Transformer for Plant Disease Detection: PlantViT." In Communications in Computer and Information Science. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-11346-8_43.

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Ghesquiere, Matisse, and Mkhuseli Ngxande. "Deep Learning for Plant Disease Detection." In Advances in Computer Vision and Computational Biology. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-71051-4_5.

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Keerthan Bhat, H., Aashish Mukund, S. Nagaraj, and R. Prakash. "LeafViT: Vision Transformers-Based Leaf Disease Detection." In Innovations in Computational Intelligence and Computer Vision. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-2602-2_8.

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Zhongzhi, Han. "Using Deep Learning for Image-Based Plant Disease Detection." In Computer Vision-Based Agriculture Engineering. CRC Press, 2019. http://dx.doi.org/10.1201/9780429289460-24.

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Conference papers on the topic "Computer vision technologies Disease detection"

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Banerjee, Joydeep, Amiya Kumar Das, Ketan Sabale, and Ahmed Alkhayyat. "A Hybrid Computer Vision enabled GAN Model for Pest and Disease Detection." In 2024 International Conference on Intelligent Computing and Emerging Communication Technologies (ICEC). IEEE, 2024. https://doi.org/10.1109/icec59683.2024.10837494.

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De Morais, Maurício Herche Fófano, João Mendes, Murillo Ferreira Dos Santos, Fernanda Mara Fernandes, José Lima, and Ana Isabel Pereira. "A YOLO-Based Approach for Detection of Olive Knot Disease through UAV and Computer Vision Technologies." In 2025 26th International Carpathian Control Conference (ICCC). IEEE, 2025. https://doi.org/10.1109/iccc65605.2025.11022835.

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Prince, Nayem Uddin, Md Abdullah Al Mamun, Md Tanvir Miah Shagar, Md Rezaul Karim Emon, and Md Sahadat Hossen Sajib. "Lychee Leaf Disease Detection by Vision Transformer and Computer Vision." In 2024 IEEE International Conference on Blockchain and Distributed Systems Security (ICBDS). IEEE, 2024. https://doi.org/10.1109/icbds61829.2024.10837439.

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Guo, Lifeng, Yuzhu Wu, Jiahao Zhao, et al. "Rice Disease Detection Based on Improved YOLOv8n." In 2025 6th International Conference on Computer Vision, Image and Deep Learning (CVIDL). IEEE, 2025. https://doi.org/10.1109/cvidl65390.2025.11085630.

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Zhou, Peng, Liqing Geng, Genghuang Yang, and Liming Chen. "Potato Disease Detection Algorithm Based on Improved YOLOv8." In 2024 2nd International Conference on Computer, Vision and Intelligent Technology (ICCVIT). IEEE, 2024. https://doi.org/10.1109/iccvit63928.2024.10872482.

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Guo, Jinbo, Fenghua Xu, Shenghuai Wang, Xiaohui Chen, Chen Wang, and Wei Zhang. "Pavement disease object detection for UAVs based on improved YOLOv8." In Fourth International Conference on Computer Vision and Pattern Analysis (ICCPA 2024), edited by Ji Zhao and Yonghui Yang. SPIE, 2024. http://dx.doi.org/10.1117/12.3037809.

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Islam, Tarequl, Nahida Sultana Riya, Md Irfan Abrar, and Tanvir Azhar. "Advancing Coconut Tree Disease Detection Using Integrated CNN And Hybrid Vision Transformer." In 2025 International Conference on Electrical, Computer and Communication Engineering (ECCE). IEEE, 2025. https://doi.org/10.1109/ecce64574.2025.11013140.

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Zhao, Xin, Qi Ye, Mingtao Ma, and Lifen Wang. "Apple Leaf Disease Detection Algorithm Based on an Improved YOLOv8." In 2024 International Conference on Image Processing, Computer Vision and Machine Learning (ICICML). IEEE, 2024. https://doi.org/10.1109/icicml63543.2024.10958088.

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Gupta, Sankalap, Piyush Kumar, and Mohammad Khalid Pandit. "Performance Analysis of Machine Learning Techniques in Plant Leaf Disease Detection." In 2024 IEEE International Conference on Computer Vision and Machine Intelligence (CVMI). IEEE, 2024. https://doi.org/10.1109/cvmi61877.2024.10782740.

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Chandrakantha, T. S., Basavaraj N. Jagadale, G. R. Madhuri, and T. E. Abhishek. "AI in Ophthalmic Imaging: Enhancing Retina Analysis for Early Disease Detection." In 2024 IEEE International Conference on Computer Vision and Machine Intelligence (CVMI). IEEE, 2024. https://doi.org/10.1109/cvmi61877.2024.10782114.

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Reports on the topic "Computer vision technologies Disease detection"

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Moshura, Mariia, Yana Terleeva, and Olena Nesterova. Evaluating the performance of computer detection software implementation in triaging chest X-ray images in TB screening program in Ukraine. Public Health Center of the Ministry of Health of Ukraine, 2024. https://doi.org/10.63263/tb0000001.

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Modern technologies for diagnosing tuberculosis and effective treatment regimens contribute to the fight against the disease in various ways. In general, modern diagnostic and treatment technologies increase the chances of early detection and treatment of tuberculosis, reducing morbidity and improving quality of life. This study aimed to determine the optimal model for the implementation of computer-aided diagnostic (CAD) systems in Ukrainian TB facilities in Lviv and Sumy oblasts. The study had the following components: Data collection from TB facilities included an assessment of the current
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Pasupuleti, Murali Krishna. Next-Generation Extended Reality (XR): A Unified Framework for Integrating AR, VR, and AI-driven Immersive Technologies. National Education Services, 2025. https://doi.org/10.62311/nesx/rrv325.

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Abstract: Extended Reality (XR), encompassing Augmented Reality (AR), Virtual Reality (VR), and Mixed Reality (MR), is evolving into a transformative technology with applications in healthcare, education, industrial training, smart cities, and entertainment. This research presents a unified framework integrating AI-driven XR technologies with computer vision, deep learning, cloud computing, and 5G connectivity to enhance immersion, interactivity, and scalability. AI-powered neural rendering, real-time physics simulation, spatial computing, and gesture recognition enable more realistic and adap
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Anderson, Donald M., Lorraine C. Backer, Keith Bouma-Gregson, et al. Harmful Algal Research & Response: A National Environmental Science Strategy (HARRNESS), 2024-2034. Woods Hole Oceanographic Institution, 2024. http://dx.doi.org/10.1575/1912/69773.

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Harmful and toxic algal blooms (HABs) are a well-established and severe threat to human health, economies, and marine and freshwater ecosystems on all coasts of the United States and its inland waters. HABs can comprise microalgae, cyanobacteria, and macroalgae (seaweeds). Their impacts, intensity, and geographic range have increased over past decades due to both human-induced and natural changes. In this report, HABs refers to both marine algal and freshwater cyanobacterial events. This Harmful Algal Research and Response: A National Environmental Science Strategy (HARRNESS) 2024-2034 plan bu
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