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Journal articles on the topic 'Diseases detection system'

Author: Grafiati

Published: 28 May 2022

Last updated: 31 July 2025

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1

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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Awal, Md Abdul, Mohammad Jahangir Alam, and Md Nurul Mustafa. "Crops Diseases Detection and Solution System." International Journal of Informatics and Communication Technology (IJ-ICT) 6, no. 3 (2017): 209. http://dx.doi.org/10.11591/ijict.v6i3.pp209-217.

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<p>The technology based modern agriculture industries are today’s requirement in every part of agriculture in Bangladesh. In this technology, the disease of plants is precisely controlled. Due to the variable atmospheric circumstances these conditions sometimes the farmer doesn’t know what type of disease on the plant and which type of medicine provide them to avoid diseases. This research developed for crops diseases detection and to provides solution by using image processing techniques. We have used Android Studio to develop the system. The crops diseases detection and solution system

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Alam, Mohammad Jahangir, Md Abdul Awal, and Md Nurul Mustafa. "Crops diseases detection and solution system." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 3 (2019): 2112. http://dx.doi.org/10.11591/ijece.v9i3.pp2112-2120.

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<p>The technology based modern agriculture industries are today’s requirement in every part of agriculture in Bangladesh. In this technology, the disease of plants is precisely controlled. Due to the variable atmospheric circumstances these conditions sometimes the farmer doesn’t know what type of disease on the plant and which type of medicine provide them to avoid diseases. This research developed for crops diseases detection and to provide solutions by using image processing techniques. We have used Android Studio to develop the system. The crops diseases detection and solution system

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Mohammad, Jahangir Alam, Abdul Awal Md., and Nurul Mustafa Md. "Crops diseases detection and solution system." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 3 (2019): 2112–20. https://doi.org/10.11591/ijece.v9i3.pp2112-2120.

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The technology based modern agriculture industries are today’s requirement in every part of agriculture in Bangladesh. In this technology, the disease of plants is precisely controlled. Due to the variable atmospheric circumstances these conditions sometimes the farmer doesn’t know what type of disease on the plant and which type of medicine provide them to avoid diseases. This research developed for crops diseases detection and to provide solutions by using image processing techniques. We have used Android Studio to develop the system. The crops diseases detection and solution sys

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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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Geethu, James1 Amala Varghese2 Ann Mariya Baby3 Anu Varghese Kodiyan4 &. Binet Rose Devassy5. "CADID (CARDIAC DISEASES DETECTION)." GLOBAL JOURNAL OF ENGINEERING SCIENCE AND RESEARCHES [AIVESC-18] (April 26, 2018): 33–38. https://doi.org/10.5281/zenodo.1230364.

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Now a day’s Cardiac arrest is a global health concern. It is estimated that nearly half of all cardiovascular deaths worldwide are due to Cardiac arrest resulting in an estimated 4 to 6 million cases each year. Roughly half of cardiac arrest patients experience some warning signs in the week before. But it is misjudged like gastric problems or something like that. So our proposal CaDiD helps to monitor cardiac activity and detects cardiac diseases. The system displays the cardiac signal with details. So many people of India didn’t get the treatment of doctors. As half of the Indian

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Katkar, Aniruddha. "EYE DISEASE RECOGNITION SYSTEM." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem32078.

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This paper presents an innovative system for detecting eye diseases utilizing advanced machine learning techniques. Given the increasing prevalence of eye disorders, early detection and intervention are of utmost importance. The proposed system integrates a diverse dataset comprising medical images and patient information. Deep learning algorithms are employed to extract intricate features from the dataset. These features are then input into a predictive model, facilitating accurate identification of potential eye diseases. Rigorous testing and validation demonstrate the system's performance a

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M. R., Dr Sanghavi. "Tomato Leaf Disease Detection System." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem33787.

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Tomato is a widely cultivated crop with significant economic importance in the agro based industry. However, tomato plants are susceptible to various diseases that can severely impact yield and quality. Early and accurate detection of these diseases is crucial for effective disease management and ensuring optimal production. In this study, we propose a novel approach that a convolutional Neural Network (CNN) for the automated detection of tomato leaf diseases. First, Convolutions is employed to reduce the dimensionality of the input data, extracting the most relevant features for disease detec

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Parbate, Pranay, Dhairyashil Thombare, Tushar Yerkal, Adarsh Varpe, and Prof Suresh Reddy. "Multiple Disease Detection System." International Journal for Research in Applied Science and Engineering Technology 12, no. 4 (2024): 997–1003. http://dx.doi.org/10.22214/ijraset.2024.59966.

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Abstract: In the realm of healthcare, the early detection of multiple diseases presents a formidable challenge but holds immense potential for improving patient outcomes. This paper proposes an integrative approach for the simultaneous detection of four prevalent diseases: heart disease, Parkinson's disease, diabetes, and skin cancer. Leveraging advanced machine learning techniques, our framework encompasses data preprocessing methods for cleaning and normalization, feature selection strategies to extract discriminative features from heterogeneous medical datasets, and ensemble classification

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Tagare, Mohammad M., Urmila R. Pol, Parashuram S. Vadar, and Tejashree T. Moharekar. "Detection of Jackfruit Leaf Disease Using Machine Learning and Deep Learning." International Journal for Research in Applied Science and Engineering Technology 13, no. 4 (2025): 1871–76. https://doi.org/10.22214/ijraset.2025.68592.

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Abstract: The detection of diseases in agricultural crops plays a critical role in maintaining healthy yields. Jackfruit (Artocarpus heterophyllus), a tropical fruit, is susceptible to various diseases that impact its leaves. Timely disease detection can significantly reduce crop loss and improve the quality of the harvest. This paper proposes a system for detecting jackfruit leaf diseases using machine learning (ML) and deep learning (DL) techniques. A dataset of healthy and diseased jackfruit leaf images is used to train both traditional ML algorithms (Random Forest) and DL models (Convoluti

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Tiwari, Prof Swati, Pranjal Shyambabu Patle, Pranshu Shyambabu Patle, Kuldeep Moreshwar Sonkusare, and Pranali Shivshankar Mungate. "Leaf Diseases Detection System Using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 12 (2022): 2372–75. http://dx.doi.org/10.22214/ijraset.2022.48000.

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Abstract: Agriculture supports living beings and nature in many ways, such as food and habitat. All humans as well as animals depend on the agriculture sector for their basic needs of food and nutrients. Thus, the importance of agriculture and the ways it can be improved further should be taken into account. While discussing agriculture, the topic of related diseases is not far behind. Crop disease is the most common threat that falls upon agricultural harvest. To address the issue at hand, firstly, farmers must figure out the situation on time. Then only the chance of saving the crops exists.

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Baratov, Rustam, Himola Sunnatillayeva, and Almardon Mamatovich Mustafoqulov. "SMART SYSTEM FOR WHEAT DISEASES EARLY DETECTION." Chemical Technology, Control and Management 2023, no. 6 (2023): 38–43. http://dx.doi.org/10.59048/2181-1105.1509.

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Pasha.B, Asif. "Grape Leaf Disease Prediction and Management System." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem48188.

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I. ABSTRACT The grapevine industry faces numerous challenges, including the prevalence of diseases that can severely impact crop yield and quality. Timely identification of grapevine diseases is critical for effective management and prevention. Traditional methods of disease detection, relying on visual inspection by experts, are often time-consuming, inconsistent, and prone to human error. To address this challenge, we propose a state- of-the-art Grape Disease Detection System using YOLOv8 (You Only Look Once), an advanced deep learning-based object detection algorithm, for real-time identifi

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Premnath, Akshara, and Mrs V. Manoranjithem. "Plant Disease Detection and Solution System." International Journal for Research in Applied Science and Engineering Technology 11, no. 3 (2023): 2072–75. http://dx.doi.org/10.22214/ijraset.2023.49888.

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Abstract: For preventing losses in the yield and quantity of the cultural product, Classification is performed, if proper analysis is not taken in this approach or classification, then it produces serious effects on plants and due to ich respective product quality or productivity is affected. Disease classification on the plants is very critical for supportable agriculture. It is very difficult to monitor or treat plant diseases manually. It requires a huge amount of work and also needs excessive processing time, therefore image processing for the detection of plant diseases. Automatic fruit d

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Azfar, Saeed, Adnan Nadeem, Kamran Ahsan, et al. "Automated System for Detecting, Identifying, and Preventing Cotton Leaf and Boll Diseases Using Deep Learning." International Journal of Advances in Soft Computing and its Applications 17, no. 1 (2025): 64–79. https://doi.org/10.15849/ijasca.250330.05.

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The Internet of Things (IoT) technology facilitates real-time data collection through sensors, enhancing disease detection and management accuracy and crop efficiency. Pest infestations in cotton crops adversely impact its production and ultimately affect the nation's economy which depends on agriculture. Previous research has primarily focused on detecting cotton leaf diseases through imagery, while this study addresses both cotton leaf and its boll diseases. An existing dataset of cotton leaf images was customized by incorporating classes of cotton boll images, resulting in a comprehensive d

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A., Adegbola,, Ampitan, J., Akande, O., Adewuyi, O., Mgbeahuruike, E., and Adebanjo, A. "A Convolutional Neural Network Model for Crop Disease Detection System." British Journal of Computer, Networking and Information Technology 7, no. 4 (2024): 94–102. http://dx.doi.org/10.52589/bjcnit-z1blvyo8.

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Crop diseases pose a significant challenge to global food security, adversely impacting agricultural output and resulting in considerable economic repercussions. The prompt and precise identification of these diseases is essential for effective intervention and sustainable agricultural practices. This study introduces a model based on Convolutional Neural Networks (CNNs) for the automated detection of crop diseases. The model employs advanced deep learning methodologies to recognize and categorize plant diseases through the analysis of leaf images. Our CNN framework is trained on an extensive

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G., Sekar, and Benson Mansingh P.M. "Smart Leaf Recognition System." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 5 (2020): 289–91. https://doi.org/10.35940/ijeat.D7577.069520.

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One of major issue nowadays is the agricultural productivity which is something our Nation’s economy highly depends. Technology based advancements may lead to detection of diseases in plants which are quite natural. Care should be taken in this area before it causes serious effects on plants which mainly affect the product quality, quantity or productivity. Early stage detection of diseases in plants through some automatic technique is beneficial as it reduces a huge work of monitoring in large acres of crops. When they appear on plant leaves, earlier detection helps us to increase the y

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SAHANA R S, Mrs. "Plant Parenting and Diseases Detection." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem45994.

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ABSTRACT - Plant diseases pose a critical challenge to agricultural productivity worldwide. Early and accurate diagnosis of plant diseases is essential for ensuring food security and minimizing economic losses. This project presents a deep learning-based solution to detect plant diseases using Convolutional Neural Networks (CNNs). The model is trained on a dataset of plant leaf images, including various healthy and tdiseased conditions. The proposed system preprocesses input images, extracts features using CNN layers, and classifies them into predefined disease categories. Experimental results

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Elmie Dansy, Sholestica, Achmad Yani, Abdi Manaf, Azmi Shawkat Abdulbaqi, and Nur Iksan. "Expert System for Diagnosis Coronavirus Disease." International Journal of Artificial Intelligence 10, no. 1 (2023): 39–44. http://dx.doi.org/10.36079/lamintang.ijai-01001.537.

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The latest issue of the disease called COVID-19 has become famous all over the world. Hence, through this problem, it found that this disease have the same symptom with other diseases such as Influenza and also normal flu. Detecting diseases at early stage can enable to overcome and treat them appropriately. This is because many of peoples does not know and does not aware of the symptom of this various diseases. In an effort to address those problems, an Expert System for Corona Earlier Detection has been proposed to help the doctors to detect those various diseases in human body. Through this

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Alam, Arman. "Plant Disease Detection & Classification System." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem47305.

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Abstract -- Agriculture plays a pivotal role in global food security, and the early detection of crop diseases is essential for maintaining optimal crop yield and quality. In this research, we explore the application of machine learning (ML) techniques for crop disease detection, aiming to provide a reliable and efficient solution to mitigate the impact of diseases on agricultural productivity. Leveraging a comprehensive dataset comprising diverse crops and disease instances, we employ state-of-the- art ML algorithms, including convolutional neural networks (CNNs) and support vector machines (

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Almayyan, Waheeda I., and Bareeq A. AlGhannam. "Detection of Kidney Diseases." International Journal of E-Health and Medical Communications 15, no. 1 (2024): 1–21. http://dx.doi.org/10.4018/ijehmc.354587.

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Chronic kidney disease (CKD) is a medical condition characterized by impaired kidney function, which leads to inadequate blood filtration. To reduce mortality rates, recent advancements in early diagnosis and treatment have been made. However, as diagnosis is time-consuming, an automated system is necessary. Researchers have been employing various machine learning approaches to analyze extensive and complex medical data, aiding clinicians in predicting CKD and enabling early intervention. Identifying the most crucial attributes for CKD diagnosis is this paper's primary objective. To address th

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Sarangi, Piyush Kumar, and Er Jagannath Ray. "Plant Leaf Disease Detection Using Machine Learning Algorithm." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 07 (2025): 1–9. https://doi.org/10.55041/ijsrem51361.

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This project focuses on detecting plant leaf diseases using machine learning. Farmers often face crop loss due to diseases, and early detection can help prevent this. The system takes images of plant leaves, processes them to extract important features (like color and shape), and then uses a machine learning model to identify if the leaf is healthy or has a disease. We use algorithms like Support Vector Machine (SVM) or Convolutional Neural Network (CNN) to train the model with examples of diseased and healthy leaves. Once trained, the system can accurately predict the disease from a new leaf

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Giri, Deepa Kumari. "Plant Disease Detection System." International Journal for Research in Applied Science and Engineering Technology 13, no. 1 (2025): 1113–17. https://doi.org/10.22214/ijraset.2025.66511.

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This project develops a Plant Leaf Disease Detection System that uses machine learning to identify plant diseases from uploaded leaf images. By applying a pre-trained deep learning model, the system provides real-time diagnosis and suggests remedies stored in a database. It also integrates the Google Translate API for multilingual support, making the system accessible to a global audience. The aim is to assist farmers in early disease detection, improve crop management, and promote sustainable agricultural practices.

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Barthare, Nanda. "Different Plant Disease Detection and Pest Detection Techniques Using Image Processing." International Journal for Research in Applied Science and Engineering Technology 10, no. 1 (2022): 1486–92. http://dx.doi.org/10.22214/ijraset.2022.40003.

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Abstract: In India, agriculture is a significant industry. Our Indian economy is also heavily reliant on agriculture; given that agriculture employs near about 70% of the population, it is critical to boost crop/plant productivity. Farmers have struggled to achieve higher productivity and better market prices due to many sorts of crop diseases. As a result, early detection of plant diseases becomes an essential strategy for avoiding losses in an agricultural production system. The disease's symptoms are mostly noticeable on leaves. It's difficult to keep track of each plant manually across a l

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Shitole, Shubham. "Respiratory Diseases Detection using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (2021): 2698–701. http://dx.doi.org/10.22214/ijraset.2021.35507.

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Prediction of the Respiratory diseases in the earlier stage can be very useful specially to improve the survival rate of that patient. CT scan images are used to detect various lung diseases .These CT scan reports are sent to pathologists for further process. Pathologists analyze CT scan report and predict the infected tissues which are the main cause of the particular disease. This is lengthy process and to avoid this steps and increase the accuracy of the prediction Machine learning plays an important role . The system proposes to build "Predictive Diagnostic System" of infectious lung by us

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Li, Dengshan, Rujing Wang, Chengjun Xie, et al. "A Recognition Method for Rice Plant Diseases and Pests Video Detection Based on Deep Convolutional Neural Network." Sensors 20, no. 3 (2020): 578. http://dx.doi.org/10.3390/s20030578.

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Increasing grain production is essential to those areas where food is scarce. Increasing grain production by controlling crop diseases and pests in time should be effective. To construct video detection system for plant diseases and pests, and to build a real-time crop diseases and pests video detection system in the future, a deep learning-based video detection architecture with a custom backbone was proposed for detecting plant diseases and pests in videos. We first transformed the video into still frame, then sent the frame to the still-image detector for detection, and finally synthesized

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Raja, Ronish. "Plant Disease Detection System Using Convolutional Neural Networks and TensorFlow Lite." International Journal for Research in Applied Science and Engineering Technology 13, no. 5 (2025): 725–33. https://doi.org/10.22214/ijraset.2025.69515.

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This research presents an intelligent system for the classification of plant diseases using a convolutional neural network (CNN) trained on a large dataset of diseased and healthy plant leaves. The model was developed using Python and deep learning libraries such as TensorFlow and Keras, achieving high accuracy in classifying various plant diseases. The trained model is integrated into a user-friendly web application using Streamlit, enabling real-time predictions from uploaded images. The system provides an accessible interface for farmers, researchers, and agricultural workers to detect plan

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Kang, Sungmin, Kuntaek Lim, Byungsub Lee, et al. "P4-072: Blood Detection of Alzheimer'S Diseases With Multimer Detection System-ad." Alzheimer's & Dementia 6 (July 2010): e43-e43. http://dx.doi.org/10.1016/j.jalz.2010.08.132.

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M. Dhana Lakshmi. "Advancements in Early Detection of Cotton Leaf Diseases in Plants Using Multimodal Deep Learning Techniques." Journal of Information Systems Engineering and Management 10, no. 2s (2025): 167–75. https://doi.org/10.52783/jisem.v10i2s.211.

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Due to the vital importance of promptly controlling plant diseases and the challenges facing today's agricultural disease management techniques, a novel framework has been introduced for detecting crop disease. This paper proposes a new methodology for detecting cotton leaf disease using multi-modal deep learning approaches. Since the early control of plant diseases is paramount and commonly utilized methods for managing agricultural diseases suffer in practice, a new framework for disease detection has been proposed. Inspecting modern deep learning algorithms concerning the analysis of images

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Dai, Dikang, Peiwen Xia, Zeyang Zhu, and Huilian Che. "MTDL-EPDCLD: A Multi-Task Deep-Learning-Based System for Enhanced Precision Detection and Diagnosis of Corn Leaf Diseases." Plants 12, no. 13 (2023): 2433. http://dx.doi.org/10.3390/plants12132433.

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Corn leaf diseases lead to significant losses in agricultural production, posing challenges to global food security. Accurate and timely detection and diagnosis are crucial for implementing effective control measures. In this research, a multi-task deep learning-based system for enhanced precision detection and diagnosis of corn leaf diseases (MTDL-EPDCLD) is proposed to enhance the detection and diagnosis of corn leaf diseases, along with the development of a mobile application utilizing the Qt framework, which is a cross-platform software development framework. The system comprises Task 1 fo

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Vaidya, Ayush. "Predictive Modeling for Heart Diseases Detection." International Journal for Research in Applied Science and Engineering Technology 13, no. 5 (2025): 560–63. https://doi.org/10.22214/ijraset.2025.70225.

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In this Research paper focuses on the development and application of predictive modeling techniques for the early detection of heart disease. Heart disease remains a leading cause of death globally, making early diagnosis and prevention essential. This project seeks to develop a reliable system for predicting the risk of heart disease by utilizing modern machine learning and data analysis techniques, drawing on patient data such as demographics, lifestyle habits, medical background, and clinical test results. By applying various predictive algorithms, such as decision trees, support vector mac

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CHLUDZIŃSKI, Tomasz. "System of breath collection and analysis for diseases detection." PRZEGLĄD ELEKTROTECHNICZNY 1, no. 11 (2016): 49–52. http://dx.doi.org/10.15199/48.2016.11.12.

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Huang, Qin, Shanqiao Han, Yan Zhang, Yue Kou, Xiaohang Zhao, and Guoliang Huang. "Fast infectious diseases diagnostics based on microfluidic biochip system." Journal of Innovative Optical Health Sciences 10, no. 02 (2017): 1650044. http://dx.doi.org/10.1142/s1793545816500449.

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Molecular diagnostics is one of the most important tools currently in use for clinical pathogen detection due to its high sensitivity, specificity, and low consume of sample and reagent is keyword to low cost molecular diagnostics. In this paper, a sensitive DNA isothermal amplification method for fast clinical infectious diseases diagnostics at aM concentrations of DNA was developed using a polycarbonate (PC) microfluidic chip. A portable confocal optical fluorescence detector was specifically developed for the microfluidic chip that was capable of highly sensitive real-time detection of ampl

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Gulzar, Shahid. "Multiple Disease Detection System Using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 12, no. 5 (2024): 1798–804. http://dx.doi.org/10.22214/ijraset.2024.61575.

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Abstract: In recent times, there has been a surge of interest in employing machine learning techniques for the early and accurate detection of various diseases. This research introduces a holistic approach to constructing a robust multi-disease detection system that utilizes advanced machine learning algorithms. Our proposed system integrates diverse datasets encompassing a range of medical conditions, enabling the simultaneous detection of multiple diseases within a unified framework. We leverage cutting-edge machine learning models, including, but not limited to, [specify the models used], t

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Jamerlan, Angelo Moscoso, Kyu Hwan Shim, Niti Sharma, and Seong Soo A. An. "Multimer Detection System: A Universal Assay System for Differentiating Protein Oligomers from Monomers." International Journal of Molecular Sciences 26, no. 3 (2025): 1199. https://doi.org/10.3390/ijms26031199.

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Depositions of protein aggregates are typical pathological hallmarks of various neurodegenerative diseases (NDs). For example, amyloid-beta (Aβ) and tau aggregates are present in the brain and plasma of patients with Alzheimer’s disease (AD); α-synuclein in Parkinson’s disease (PD), dementia with Lewy bodies (DLB), and multiple system atrophy (MSA); mutant huntingtin protein (Htt) in Huntington’s disease (HD); and DNA-binding protein 43 kD (TDP-43) in amyotrophic lateral sclerosis (ALS), frontotemporal dementia (FTD), and limbic-predominant age-related TDP-43 encephalopathy (LATE). The same mi

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Al Mamun, Md, and Mohammad Shorif Uddin. "A Survey on a Skin Disease Detection System." International Journal of Healthcare Information Systems and Informatics 16, no. 4 (2021): 1–17. http://dx.doi.org/10.4018/ijhisi.20211001.oa35.

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Skin diseases are frequent and quite perennial in the world, and in some cases, these lead to cancer. These are curable if detected earlier and treated appropriately. An automated image-based detection system consisting of four main modules: image enhancement, region of interest segmentation, feature extraction, and detection can facilitate early identification of these diseases. Diverse image-based methods incorporating machine learning techniques are developed to diagnose different types of skin diseases. This article focuses on the review of the tools and techniques used in the diagnosis of

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Veena.K.N and Shobha.S. "An Electrocardiograph based Arrythmia Detection System." International Journal of Engineering and Management Research 8, no. 3 (2018): 131–36. https://doi.org/10.31033/ijemr.8.3.16.

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Cardiac disorders turn out to be a serious disease if not diagnosed and treated at the earliest. Arrhythmia is a cardiac disorder that exists as a result of irregular heart beat conditions. There are several variants in this type of disorder which can be only diagnosed only when patient is under an intensive care conditions and also the patient with such disorder do not experience and physical symptoms. Such diseases turn out to be deadly if not treated early. A detection system is thus required which is capable of detecting these arrhythmias in real time and aid in the diagnosis. An FPGA base

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K Bhavadharni and Dr. K Banuroopa. "Pest Detection on Plants Using Image Processing." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 2 (2025): 1782–86. https://doi.org/10.32628/cseit25112516.

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Plant pest and disease detection is crucial for ensuring agricultural productivity and food security. This paper presents a machine learning-based approach utilizing image processing techniques to identify pests and diseases in plants. The system employs Histogram of Oriented Gradients (HOG) for feature extraction and a Support Vector Machine (SVM) classifier for classification. The dataset is built from labelled images of pests and diseases in plants, and the trained model is used to predict new instances. Additionally, color-based segmentation in the HSV color space enhances detection by iso

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Basha, M. Suleman, K. Rajendra Prasad, S. K. Mouleeswaran, Ramesh Chandra Poonia, and Shiju Sebastian. "Multi-disease detection system with X-ray images using deep learning techniques." Journal of Information and Optimization Sciences 45, no. 5 (2024): 1379–88. http://dx.doi.org/10.47974/jios-1710.

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In the realm of medical diagnostics, advanced deep learning algorithms have become powerful tools for detecting and diagnosing diseases. This study introduces a groundbreaking Multi-Disease Detection System designed specifically for analyzing X-ray images. It focuses on detecting Alzheimer’s disease, brain tumors, COVID-19 infection, and pneumonia, marking a significant advancement in medical imaging analysis and clinical decision-making. The system utilizes CNN and RNN to attain unparalleled accuracy and reliability in disease detection. By processing MRI and CT scans for Alzheimer’s and brai

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Carrillo-de-Gea, Juan Manuel, Ginés García-Mateos, José Luis Fernández-Alemán, and José Luis Hernández-Hernández. "A Computer-Aided Detection System for Digital Chest Radiographs." Journal of Healthcare Engineering 2016 (2016): 1–9. http://dx.doi.org/10.1155/2016/8208923.

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Computer-aided detection systems aim at the automatic detection of diseases using different medical imaging modalities. In this paper, a novel approach to detecting normality/pathology in digital chest radiographs is proposed. The problem tackled is complicated since it is not focused on particular diseases but anything that differs from what is considered as normality. First, the areas of interest of the chest are found using template matching on the images. Then, a texture descriptor called local binary patterns (LBP) is computed for those areas. After that, LBP histograms are applied in a c

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Mohammed, Kamel K., Heba M. Afify, and Aboul Ella Hassanien. "ARTIFICIAL INTELLIGENT SYSTEM FOR SKIN DISEASES CLASSIFICATION." Biomedical Engineering: Applications, Basis and Communications 32, no. 05 (2020): 2050036. http://dx.doi.org/10.4015/s1016237220500362.

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In this paper, an artificial intelligent technique is proposed for skin disease detection and classification. The suggested method comprises four stages, including segmentation, extraction of textural features, and classification. The stretch-based enhanced algorithm has been adapted for image enhancement. Then the method of an active contour is used for segmentation to determine the skin lesion in tissue. Textural features are obtained from the segmented skin lesion. As several numbers of the features can affect the classification precision, ideal feature selection is made to exclude features

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Lincy, Vincent Rakesh V. S. "DETECTION AND ANALYSIS OF ANTHRACNOSE DISEASE USING C++ PROGRAMMING WITH OPENCV LIBRARIES AND MATLAB." GLOBAL JOURNAL OF ENGINEERING SCIENCE AND RESEARCHES [AIVESC-18] (April 26, 2018): 12–16. https://doi.org/10.5281/zenodo.1230356.

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— Physical recognition of defected fruits is very difficult. These days, the existing system has the drawback of low speed, low efficiency, high cost and complexity. The identification of diseases on fruits is the major factor for reduces the diseases on fruits and thereby increasing the productivity. The symptoms can be observed as spots or lesions on fruits and Its effect will diminish the quantity and quality of fruit, as it reduces the photosynthesis process. The system uses openCV to monitor the diseases on fruits and the steps for the resulting system are image acquisition, Image p

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Rao, K. Deepa. "Paddy Crop Disease Detection Using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 12, no. 4 (2024): 686–93. http://dx.doi.org/10.22214/ijraset.2024.59861.

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Abstract: India has risen as a significant player in earlier years, the world’s second-largest producer of paddy, yielding approximately 497.7 million metric tons annualy. Sustaining vast paddy fields demand ongoing attention and upkeep. It is critical to recognize the symptoms and understand how this can effectively control the disease. Therefore, inspired by this research paper, a solution is suggested to train machine learning for identify diseases in paddy plants. The system utilizes rea;- time datasets sourced from the Agriculture Research Institute of Lonavala, which are freely accessibl

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PhD, Emtithal Ahmed, Almustafa Mohamed, and Ammar Khairi. "CAD System Based on Face Mask Recognition for Respiratory Infections Diseases Hospital." Journal of Image Processing and Intelligent Remote Sensing, no. 31 (January 28, 2023): 40–48. http://dx.doi.org/10.55529/jipirs.31.40.48.

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The infection of respiratory diseases can be eliminating and controlling by wearing face mask in contaminated areas. However, to control people about wearing face mask it has been challenging unless the automatic recognitions are applied. Therefore, in this paper, a Face Mask Recognition System by Computer Aided Design (CAD) is introduced. The proposed design system is based on face, mouth and nose detections in captured image. The CAD system considers to be implemented for specialized respiratory diseases hospital with different departments, each department controlled by separated door. The m

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Baratov, Rustam, and Himola Valixanova. "Smart system for early detection of agricultural plant diseases in the vegetation period." E3S Web of Conferences 386 (2023): 01007. http://dx.doi.org/10.1051/e3sconf/202338601007.

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This paper presents a smart system for the early detection of agricultural plant diseases in the vegetation period. The proposed smart system allows the detection of three types of wheat diseases, particularly yellow rust, powdery mildew, and Septoria at an early stage, and significantly improves the soil and ecology by locally spraying harmful chemicals just on sick plants. The proposed disease-detecting method is based on the structure of a convolutional neural network (CNN) using the Pycharm program based on the C ++ programming language. The basic structure of the smart system consists of

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Waidyanatha, Nuwan, Artur Dubrawski, Ganesan M., and Gordon Gow. "Affordable System for Rapid Detection and Mitigation of Emerging Diseases." International Journal of E-Health and Medical Communications 2, no. 1 (2011): 73–90. http://dx.doi.org/10.4018/jehmc.2011010105.

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South and South-East Asian countries are currently in the midst of a new epidemic of Dengue Fever. This paper presents disease surveillance systems in Sri Lanka and India, monitoring a handful of communicable diseases termed as notifiable. These systems typically require 15-30 days to communicate field data to the central Epidemiology Units, to be then manually processed (Prashant & Waidyanatha, 2010). Currently used analyses rely on aggregating counts of notifiable disease cases by district, disease, and week. The Real-Time Biosurveillance Program (RTBP), a multi-partner initiative, aims

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Righi, Thabet, Mohammed Charaf Eddine Meftah, Abdelkader Laouid, Mohammed Al-Khalidi, and Mostefa Kara. "An intelligent agriculture monitoring framework for leaf disease detection using YOLOv7." STUDIES IN ENGINEERING AND EXACT SCIENCES 5, no. 2 (2024): e10498. http://dx.doi.org/10.54021/seesv5n2-516.

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Agriculture is one of the most important economic sectors on which societies have relied since ancient times. With the recent development of technology, agriculture has also been incorporating modern techniques such as the Internet of Things and Artificial Intelligence to improve productivity and monitor the farming process. One of agriculture’s most prominent issues is the spread of plant diseases and the lack of real-time monitoring. Various systems and operations have recently been developed to predict and diagnose plant diseases. However, current operations have been selective, focusing on

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Ouyang, Chen, Emiko Hatsugai, and Ikuko Shimizu. "Tomato Disease Monitoring System Using Modular Extendable Mobile Robot for Greenhouses: Automatically Reporting Locations of Diseased Tomatoes." Agronomy 12, no. 12 (2022): 3160. http://dx.doi.org/10.3390/agronomy12123160.

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Based on the appearance of tomatoes, it is possible to determine whether they are diseased. Detecting diseases early can help the yield losses of tomatoes through timely treatment. However, human visual inspection is expensive in terms of the time and labor required. This paper presents an automatic tomato disease monitoring system using modular and extendable mobile robot we developed in a greenhouse. Our system automatically monitors whether tomatoes are diseased and conveys the specific locations of diseased tomatoes to users based on the location information of the image data collected by

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Singh, Harshvardhan. "Plant Disease Detection System Using Machine Learning Algorithm." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem48921.

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Abstract Plant diseases pose a significant threat to global food security, causing substantial agricultural losses. Traditional disease detection methods are labor-intensive and often unreliable. This paper presents an automated Machine Learning (ML)-based Plant Disease Detection System that leverages Convolutional Neural Networks (CNNs), Support Vector Machines (SVMs), and Random Forest (RF) for accurate disease classification using leaf images. We utilize a publicly available dataset (Plant Village) containing thousands of labeled images of healthy and diseased leaves. Our experiments demons

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Parmin, Nor Azizah, Uda Hashim, Subash C. B. Gopinath, et al. "A mini review of electrochemical genosensor based biosensor diagnostic system for infectious diseases." Environmental and Toxicology Management 1, no. 1 (2021): 14–17. http://dx.doi.org/10.33086/etm.v1i1.2038.

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The quest for alternative methods is driven by the need to provide expertise in real time in biological fields such as medicine, pathogenic bacteria and viruses identification, food protection, and quality control. Polymerase Chain Reaction (PCR) and Enzyme Linked Immunosorbent Assay (ELISA) are examples of traditional methods that have some limitations and lengthy procedures. Biosensors are the most appealing option because they provide easy, dependable, fast, and selective detection systems compared to conventional methods. This review provides an overview of electrochemical genosensor based

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