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

Author: Grafiati

Published: 5 June 2025

Last updated: 2 August 2025

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1

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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Baksh, Hari, Kavya Thottempudi, Manjit M. Khatal, et al. "Advancing Agriculture through Artificial Intelligence, Plant Disease Detection Methods, Applications, and Limitations." Journal of Advances in Biology & Biotechnology 27, no. 8 (2024): 730–39. http://dx.doi.org/10.9734/jabb/2024/v27i81191.

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In recent years, the integration of artificial intelligence (AI) into agriculture has transformed traditional farming practices. One area of significant advancement is in the detection of plant diseases, where AI-driven technologies offer innovative solutions to mitigate crop losses and enhance agricultural productivity. This paper explores the latest methodologies, applications, and challenges in utilizing AI for plant disease detection. We review various AI techniques, including machine learning, computer vision, and deep learning, that have been deployed to accurately identify and diagnose

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Tripathi, Shankar Sharan. "Intelligent Document Processing for Disease Detection." International Journal for Research in Applied Science and Engineering Technology 12, no. 5 (2024): 1874–81. http://dx.doi.org/10.22214/ijraset.2024.61951.

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Abstract: This project endeavors to develop an intelligent document processing pipeline tailored specifically for medical reports, with a primary focus on samples from Dr Lal Path lab and targeting prevalent diseases leading to kidney failures such as glomerulonephritis, chronic kidney disease, polycystic kidney disease, hypertensive nephropathy, and lupus nephritis. The proposed pipeline integrates cutting-edge technologies including the YOLOv8 object detection model for precise cropping of tabular data, Paddle OCR for accurate extraction of information from tabular images, and Fuzzy Wuzzy NL

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Poblete-Echeverría, Carlos, Inés Hernández, Salvador Gutiérrez, Rubén Iñiguez, Ignacio Barrio, and Javier Tardaguila. "Using artificial intelligence (AI) for grapevine disease detection based on images." BIO Web of Conferences 68 (2023): 01021. http://dx.doi.org/10.1051/bioconf/20236801021.

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Nowadays, diseases are one of the major threats to sustainable viticulture. Manual detection through visual surveys, usually done by agronomists, relies on symptom identification and requires an enormous amount of time. Detection in field conditions remains difficult due to the lack of infrastructure to perform detailed and rapid field scouting covering the whole vineyard. In general, symptoms of grapevine diseases can be seen as spots and patterns on leaves. In this sense, computer vision technologies and artificial intelligence (AI) provide an excellent alternative to improve the current dis

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Yauri, Ricardo, Antero Castro, and Rafael Espino. "Automatic Leaf Health Monitoring with an IoT Camera System based on Computer Vision and Segmentation for Disease Detection." WSEAS TRANSACTIONS ON ELECTRONICS 15 (December 16, 2024): 148–56. https://doi.org/10.37394/232017.2024.15.17.

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Manual identification of diseases in crops is costly and subjective, driving the need for automated systems for accurate detection in the field. This requires the use of technologies based on the integration of IoT and deep learning models to improve the assessment capacity of crop health and leaf disease, with continuous monitoring. The literature review highlights technological solutions that include weed and disease detection using artificial intelligence and autonomous systems, as well as semantic segmentation algorithms to locate diseases in field images whose processes can be improved wi

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Samuel Dave R. Signo, Chloe Lei G. Tuquero, and Edwin R. Arboleda. "Coffee disease detection and classification using image processing: A Literature review." International Journal of Science and Research Archive 11, no. 1 (2024): 1614–21. http://dx.doi.org/10.30574/ijsra.2024.11.1.0212.

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Coffee, as one of the world's most consumed beverages, sustains livelihoods for millions across more than 50 nations. The vulnerability of coffee plants to diseases, particularly Coffee Leaf Rust and Coffee Berry Disease, poses a significant threat to global production and quality. Leveraging advancements in image processing and computer vision, researchers have explored diverse classification algorithms, ranging from traditional Support Vector Machines to state-of-the-art Deep Convolutional Neural Networks (DCNNs). This review literature addresses the challenges of coffee disease detection, e

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Asif, Muhammad, Aleena Rayamajhi, and Md Sultan Mahmud. "Technological Progress Toward Peanut Disease Management: A Review." Sensors 25, no. 4 (2025): 1255. https://doi.org/10.3390/s25041255.

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Peanut (Arachis hypogea L.) crops in the southeastern U.S. suffer significant yield losses from diseases like leaf spot, southern blight, and stem rot. Traditionally, growers use conventional boom sprayers, which often leads to overuse and wastage of agrochemicals. However, advances in computer technologies have enabled the development of precision or variable-rate sprayers, both ground-based and drone-based, that apply agrochemicals more accurately. Historically, crop disease scouting has been labor-intensive and costly. Recent innovations in computer vision, artificial intelligence (AI), and

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C Mohammed Gulzar, Et al. "Survey on Therapy Prediction using Deep Learning for Pores and Skin Diseases." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 10 (2023): 1429–34. http://dx.doi.org/10.17762/ijritcc.v11i10.8687.

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Introduction: Prediction and detection of skin ailments have generally been a hard and important task for health care specialists. In the cutting-edge situation majority of the pores and skin care practitioners are the uses of traditional techniques to diagnose the ailment which may also take a large amount of time. Skin Diseases are excessive troubles in recent times as it is a consider form of environmental factors, socioeconomic elements, loss of entire weight loss program, and so on. Identifying the particular skin disease by computer vision is introduced as a novel task. Based on skin or

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Reddy K, Vigneswara, A. Suhasini, and V. V. S. S. S. Balaram. "Comparative Analysis of Fruit Disease Identification Methods: A Comprehensive Study." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 7 (2023): 315–25. http://dx.doi.org/10.17762/ijritcc.v11i7.7941.

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The need for accurate and efficient technologies for recognising and controlling fruit diseases has increased due to the rising global demand for high-quality agricultural products. This study focuses on the advantages, disadvantages, and potential practical applications of a range of methods for identifying fecundities. Thanks to developments like improved imaging, machine learning, and data analysis tools, old methods of disease diagnosis have altered as technology has developed. The study compares older methods like visual observation, manual symptom correlation, spectroscopy, and chemical

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Lazcano-García, Carolina, Karen Guadalupe García-Resendiz, Jimena Carrillo-Tripp, et al. "Deep Learning-Based System for Early Symptoms Recognition of Grapevine Red Blotch and Leafroll Diseases and Its Implementation on Edge Computing Devices." AgriEngineering 7, no. 3 (2025): 63. https://doi.org/10.3390/agriengineering7030063.

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In recent years, the agriculture sector has undergone a significant digital transformation, integrating artificial intelligence (AI) technologies to harness and analyze the growing volume of data from diverse sources. Machine learning (ML), a powerful branch of AI, has emerged as an essential tool for developing knowledge-based agricultural systems. Grapevine red blotch disease (GRBD) and grapevine leafroll disease (GLD) are viral infections that severely impact grapevine productivity and longevity, leading to considerable economic losses worldwide. Conventional diagnostic methods for these di

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Nayim, A. Dalawai, B. Prathamraj P, S. Dayananda B, P. Joythi A, and Suresh R. "Real-Time Skin Disease Detection and Classification Using YOLOv8 Object Detection for Healthcare Diagnosis." INTERNATIONAL JOURNAL OF MULTIDISCIPLINARY RESEARCH AND ANALYSIS 07, no. 12 (2024): 5429–544. https://doi.org/10.5281/zenodo.14287188.

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Skin diseases are one of the most extensive and difficult to manage topics in healthcare, affecting millions of people worldwide. Current manual diagnosis by healthcare professionals is time-consuming and dependent on an individual’s experience, authority or a medical professional’s subjective opinion, so it is paramount to create new methods to evaluate the severity of skin damage. Deep learning and computer vision technologies focused on automating the process of diagnosing and classifying skin diseases are among the most promising areas. This paper investigates the usage of webc

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Alok Singh Jadaun. "Hybrid Learning Approach for Identifying Plant Characteristics and Diseases Through Image Analysis." Journal of Information Systems Engineering and Management 10, no. 1s (2024): 390–99. https://doi.org/10.52783/jisem.v10i1s.223.

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Advancements in deep learning and computer vision have enabled significant progress in the automated analysis of plant images, aiding in the detection of diseases and characterization of plant traits. Combining these technologies into hybrid models offers the potential for enhanced accuracy and efficiency in addressing the complexities of plant disease identification in agricultural practices. Proposed research presents the development of a system based on deep hybrid learning for analyzing plant images, identifying various characteristics, and detecting diseases using computer vision techniqu

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Stefan, Williams, Fang Hui, Qahwaji Rami, et al. "WED 109 Computer vision: a smartphone camera can ‘see’ bradykinesia." Journal of Neurology, Neurosurgery & Psychiatry 89, no. 10 (2018): A11.4—A12. http://dx.doi.org/10.1136/jnnp-2018-abn.42.

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The core clinical feature of Parkinson’s disease is bradykinesia. However, the most common method of clinical assessment, finger tapping, has poor inter-rater reliability, even among movement disorder specialists. Many technologies have been devised to objectively measure finger tapping, but virtually all involve specialised equipment, which may explain why none are in widespread use. One method involves patients tapping a smartphone screen, but this cannot detect tapping amplitude or decrement (key features of bradykinesia assessment).Computer vision takes static or moving images from a camer

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C. Nandini, Dr. "Botanicare – Agricultural Portfolio for Medicinal Plants." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–9. https://doi.org/10.55041/ijsrem49721.

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Abstract The agriculture sector is undergoing a transformative evolution driven by the integration of cutting-edge technologies such as Artificial Intelligence (AI), Machine Learning (ML), and Computer Vision (CV). These advancements are not only addressing long-standing challenges but also unlocking new possibilities for enhancing productivity, sustainability, and decision-making in agricultural practices. In this context, BotaniCare emerges as a comprehensive AI-based intelligent agricultural assistant, designed to support both farmers and agricultural researchers through a suite of innovati

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C. Nandini, Dr. "Botanicare – Agricultural Portfolio for Medicinal Plants." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–9. https://doi.org/10.55041/ijsrem49568.

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Abstract The agriculture sector is undergoing a transformative evolution driven by the integration of cutting-edge technologies such as Artificial Intelligence (AI), Machine Learning (ML), and Computer Vision (CV). These advancements are not only addressing long-standing challenges but also unlocking new possibilities for enhancing productivity, sustainability, and decision-making in agricultural practices. In this context, BotaniCare emerges as a comprehensive AI-based intelligent agricultural assistant, designed to support both farmers and agricultural researchers through a suite of innovati

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Mrittika Mahbub, Md. Habib Ehsanul Hoque, and Mst. Rehena Khatun. "Smart Farming in Bangladesh: Mobile Application for Tomato Leaf Disease Detection Using a Hybrid VGG16-CNN Model." International Journal of Latest Technology in Engineering Management & Applied Science 13, no. 12 (2025): 228–38. https://doi.org/10.51583/ijltemas.2024.131220.

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Abstract: Tomato cultivation (Solanum lycopersicum L.) is highly significant due to its considerable economic value, high consumer demand, and critical role in supporting the livelihoods of farmers in Bangladesh. However, the majority of Bangladeshi farmers rely on traditional, manual methods for detecting tomato leaf diseases, relying on visual inspection and personal experience. Limited resources and a lack of awareness about advanced technologies further hinder the adoption of efficient disease detection methods. Computer vision, a cutting-edge technology, enables the automated identificati

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Yuan, Chang, Shicheng Li, Ke Wang, et al. "Mamba-YOLO-ML: A State-Space Model-Based Approach for Mulberry Leaf Disease Detection." Plants 14, no. 13 (2025): 2084. https://doi.org/10.3390/plants14132084.

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Mulberry (Morus spp.), as an economically significant crop in sericulture and medicinal applications, faces severe threats to leaf yield and quality from pest and disease infestations. Traditional detection methods relying on chemical pesticides and manual observation prove inefficient and unsustainable. Although computer vision and deep learning technologies offer new solutions, existing models exhibit limitations in natural environments, including low recognition rates for small targets, insufficient computational efficiency, poor adaptability to occlusions, and inability to accurately ident

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Othman, Nashwan Adnan, and Ilhan Aydin. "A New UAV-Based Social Distance Detector for COVID-19 Outbreaks Reduction, Using IoT, Computer Vision and Deep Learning Technologies." Traitement du Signal 39, no. 6 (2022): 1951–59. http://dx.doi.org/10.18280/ts.390607.

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Nowadays, we are living in a dangerous environment and our health system is under the threatened causes of Covid19 and other diseases. The people who are close together are more threatened by different viruses, especially Covid19. In addition, limiting the physical distance between people helps minimize the risk of the virus spreading. For this reason, we created a smart system to detect violated social distance in public areas as markets and streets. In the proposed system, the algorithm for people detection uses a pre-existing deep learning model and computer vision techniques to determine t

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Poudanien, Yu Eu, and A. V. Kozhemiako. "Image classification using optical-digital image enhancement methods and deep learning in endoscopic examinations." Optoelectronic Information-Power Technologies 49, no. 1 (2025): 135–46. https://doi.org/10.31649/1681-7893-2025-49-1-135-146.

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Gastrointestinal tract (GIT) diseases remain among the most pressing challenges in modern medicine, with external environmental factors affecting human health negatively. The rapid development of artificial intelligence and computer vision is aimed at improving existing methods for disease detection through the analysis of biomedical images. This study summarizes recent scientific advances in endoscopy that integrate machine learning with both digital and opto-digital image enhancement technologies. The paper reviews sources evaluating the use of white light imaging (WLI) and various enhanceme

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Gupta,, Sanskriti. "HEALTHCURE-Disease Diagnosis using NLP." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 06 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem35455.

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HealthCure is an innovative medical project designed to provide an all-in-one solution for the detection of seven critical diseases using advanced machine learning, computer vision, and deep learning technologies. The project aims to revolutionize healthcare by enabling users to obtain immediate diagnostic results from the comfort of their homes, making medical testing more accessible and efficient. The backend of HealthCure is powered by Flask, a lightweight and flexible web framework that facilitates rapid development and seamless integration of various machine learning models. The core of t

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Feng, Guoqing, Ying Gu, Cheng Wang, Yanan Zhou, Shuo Huang, and Bin Luo. "Wheat Fusarium Head Blight Automatic Non-Destructive Detection Based on Multi-Scale Imaging: A Technical Perspective." Plants 13, no. 13 (2024): 1722. http://dx.doi.org/10.3390/plants13131722.

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Fusarium head blight (FHB) is a major threat to global wheat production. Recent reviews of wheat FHB focused on pathology or comprehensive prevention and lacked a summary of advanced detection techniques. Unlike traditional detection and management methods, wheat FHB detection based on various imaging technologies has the obvious advantages of a high degree of automation and efficiency. With the rapid development of computer vision and deep learning technology, the number of related research has grown explosively in recent years. This review begins with an overview of wheat FHB epidemic mechan

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Latif, Jahanzaib, Shanshan Tu, Chuangbai Xiao, Sadaqat Ur Rehman, Mazhar Sadiq, and Muhammad Farhan. "Digital Forensics Use Case for Glaucoma Detection Using Transfer Learning Based on Deep Convolutional Neural Networks." Security and Communication Networks 2021 (November 29, 2021): 1–13. http://dx.doi.org/10.1155/2021/4494447.

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In parallel with the development of various emerging fields such as computer vision and related technologies, e.g., iris identification and glaucoma detection, criminals are developing their methods. It is the foremost reason for the blindness of human beings that affects the eye’s optic nerve. Fundus photography is carried out to examine this eye disease. Medical experts evaluate fundus photographs, which is a time-consuming visual inspection. Most current systems for automated glaucoma detection in fundus images rely on segmentation-based features nuanced by the underlying segmentation metho

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Shadesh, Naimul Hasan, Arifur Rahaman, Sadia Tasnim Barsha, Salma Tabashum, and Sabrina Tasnim. "ADVANCED FACE MASK DETECTION USING TRANSFER LEARNING AND CUSTOM CLASSIFIERS: ENHANCING PUBLIC SAFETY THROUGH COMPUTER VISION AND DEEP LEARNING." International Journal of Engineering Applied Sciences and Technology 09, no. 11 (2025): 01–14. https://doi.org/10.33564/ijeast.2025.v09i11.001.

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Face masks offer protection against air pollution and spread of the disease and should be worn for effortful distancing. Video cameras have proven useful upholding uniformity of mask-wearing using computer vision. Earlier mentioned methods based on convolutional neural network (CNN), YOLO (you only look once) and faster R-CNN support vector machines (SVM), and haar cascade techniques have had difficulties mainly for frontal view faces. This study provides nascent developments that will bring about remarkable changes in the field of public health as well as technologies with a unique mask detec

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Vo, Thi Thu Em, Hyeyoung Ko, Jun-Ho Huh, and Yonghoon Kim. "Overview of Smart Aquaculture System: Focusing on Applications of Machine Learning and Computer Vision." Electronics 10, no. 22 (2021): 2882. http://dx.doi.org/10.3390/electronics10222882.

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Smart aquaculture is nowadays one of the sustainable development trends for the aquaculture industry in intelligence and automation. Modern intelligent technologies have brought huge benefits to many fields including aquaculture to reduce labor, enhance aquaculture production, and be friendly to the environment. Machine learning is a subdivision of artificial intelligence (AI) by using trained algorithm models to recognize and learn traits from the data it watches. To date, there are several studies about applications of machine learning for smart aquaculture including measuring size, weight,

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Rudenko, Marina, Anatoliy Kazak, Nikolay Oleinikov, et al. "Intelligent Monitoring System to Assess Plant Development State Based on Computer Vision in Viticulture." Computation 11, no. 9 (2023): 171. http://dx.doi.org/10.3390/computation11090171.

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Plant health plays an important role in influencing agricultural yields and poor plant health can lead to significant economic losses. Grapes are an important and widely cultivated plant, especially in the southern regions of Russia. Grapes are subject to a number of diseases that require timely diagnosis and treatment. Incorrect identification of diseases can lead to large crop losses. A neural network deep learning dataset of 4845 grape disease images was created. Eight categories of common grape diseases typical of the Black Sea region were studied: Mildew, Oidium, Anthracnose, Esca, Gray r

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Praveen Payili. "AI in agriculture: Smart greenhouses and indoor farming systems." International Journal of Science and Research Archive 14, no. 1 (2025): 315–22. https://doi.org/10.30574/ijsra.2025.14.1.0054.

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This comprehensive article examines the transformative impact of artificial intelligence on modern indoor agriculture, focusing on key technological advancements in smart greenhouse management and controlled environment agriculture. The article explores critical areas, including precision environmental control, nutrient management in hydroponic systems, plant health monitoring and disease management, harvest optimization, quality control, and energy management with sustainability practices. AI-driven solutions have revolutionized traditional farming approaches by integrating deep learning algo

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Anand, Aditya. "Noval Approach for Retinal Disease Detection System." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem47573.

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ABSTRACT This research paper explores the complex terrain of developing an automatic retinal disease detection system in an attempt to break down the core characteristics, technological paradigms, and challenges involved in emulating a powerful AI-driven diagnostic tool. The system detects diabetic retinopathy and glaucoma, two major causes of blindness, through the utilization of deep learning and computer vision methodologies. The main goals of this research include an in-depth examination of the system's major features, investigation of appropriate technologies for medical image classificat

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Sriram Sitaraman. "AI-Driven Diagnostics and Imaging: Transforming Early Detection and Precision in Healthcare." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 10, no. 6 (2024): 1258–67. https://doi.org/10.32628/cseit241061167.

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Artificial intelligence is revolutionizing medical imaging and diagnostics, marking a transformative era in healthcare delivery. This comprehensive article explores the evolution from early computer-aided diagnosis systems to sophisticated deep-learning architectures, examining their impact across radiology, pathology, and clinical workflows. The article covers breakthrough technologies, including vision transformers, multi-modal integration, and explainable AI frameworks, highlighting their contributions to improved diagnostic accuracy and efficiency. The article encompasses the clinical bene

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S, Jeevanraja, Vishnu Vardhan Reddy A, Ashok Reddy S, and Hari Krishna Reddy V. "Deep Learning for Crop Yield Forcasting in Agriculture Using Multilayer Perceptron and Convolutional Neural Networks." Journal of Soft Computing Paradigm 6, no. 4 (2025): 401–11. https://doi.org/10.36548/jscp.2024.4.006.

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The primary challenge facing the agricultural sector, which is essential for ensuring global food security, is enhancing crop productivity while effectively addressing the challenges posed by plant diseases. Advanced technologies have the potential to completely transform agricultural methods, especially in the areas of computer vision and machine learning. This study uses meteorological as well as fruit and vegetables image datasets to create an integrated agricultural decision support system for crop yield estimation and disease prediction. By enabling early plant disease detection and preci

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Avijit Chakraborty, Apurba Basu, Sujit Raha, and Tanmoy Chakraborty. "A Scientific Approach to Building an Image Classification model of brain MRI images for Brain Tumor detection." international journal of engineering technology and management sciences 7, no. 2 (2023): 164–71. http://dx.doi.org/10.46647/ijetms.2023.v07i02.021.

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The Computer associated-learning is one of the most significant achievements in the field of medical imaging. Generally, we use different technologies like computer tomography (CT scan) to diagnose disease or injury; in the lungs, liver, brain, etc. For this research, we have used MRI image datasets for the identification of Brain tumor classification and non-tumor classification. Tumors are generally abnormal growth; if this type of growth occurs in the brain is called a brain tumor. Early detection and proper treatment may reduce the chances of cancer. Computer vision is the domain where ima

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Sun, Hao, Isack Thomas Nicholaus, Rui Fu, and Dae-Ki Kang. "YOLO-FMDI: A Lightweight YOLOv8 Focusing on a Multi-Scale Feature Diffusion Interaction Neck for Tomato Pest and Disease Detection." Electronics 13, no. 15 (2024): 2974. http://dx.doi.org/10.3390/electronics13152974.

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At the present stage, the field of detecting vegetable pests and diseases is in dire need of the integration of computer vision technologies. However, the deployment of efficient and lightweight object-detection models on edge devices in vegetable cultivation environments is a key issue. To address the limitations of current target-detection models, we propose a novel lightweight object-detection model based on YOLOv8n while maintaining high accuracy. In this paper, (1) we propose a new neck structure, Focus Multi-scale Feature Diffusion Interaction (FMDI), and inject it into the YOLOv8n archi

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Bilal, Anas, Xiaowen Liu, Talha Imtiaz Baig, Haixia Long, and Muhammad Shafiq. "EdgeSVDNet: 5G-Enabled Detection and Classification of Vision-Threatening Diabetic Retinopathy in Retinal Fundus Images." Electronics 12, no. 19 (2023): 4094. http://dx.doi.org/10.3390/electronics12194094.

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The rise of vision-threatening diabetic retinopathy (VTDR) underscores the imperative for advanced and efficient early detection mechanisms. With the integration of the Internet of Things (IoT) and 5G technologies, there is transformative potential for VTDR diagnosis, facilitating real-time processing of the burgeoning volume of fundus images (FIs). Combined with artificial intelligence (AI), this offers a robust platform for managing vast healthcare datasets and achieving unparalleled disease detection precision. Our study introduces a novel AI-driven VTDR detection framework that integrates

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"AI-Powered Hive Monitoring System For Varroa Mite Detection And Bee Health Management." IOSR Journal of Environmental Science Toxicology and Food Technology 18, no. 11 (2024): 23–26. http://dx.doi.org/10.9790/2402-1811022326.

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This paper proposes an innovative AI-powered hive monitoring system designed to address the critical issue of Varroa destructor mite infestations in honey bee colonies. The Varroa mite poses a significant threat to global bee populations, weakening bees, transmitting diseases, and potentially leading to colony collapse. Traditional control methods, such as chemical treatments, often have adverse effects on bee health and the environment. Our proposed system leverages advanced technologies, including machine learning, computer vision, and sensor networks, to provide a more sustainable and effec

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Hossain, Sazzad, Touhidul Seyam, Avijit Chowdhury, et al. "Enhancing Agricultural Diagnostics: Advanced Training of Pre-Trained CNN Models for Paddy Leaf Disease Detection." Machine Learning Research 10, no. 1 (2025): 1–13. https://doi.org/10.11648/j.mlr.20251001.11.

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Timely and precise identification of foliar diseases is essential in contemporary agriculture to avert crop loss, enhance productivity, and guarantee food security. Paddy, being one of the most extensively farmed and consumed staple crops globally, is especially vulnerable to several leaf diseases that can markedly diminish yield. Conventional illness detection techniques, which depend significantly on manual observation and expert evaluation, are frequently time-consuming, labor-intensive, and susceptible to discrepancies. These constraints need the implementation of automated and efficient d

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Ozella, Laura, Alessandro Magliola, Simone Vernengo, et al. "A computer vision approach for the automatic detection of social interactions of dairy cows in automatic milking systems." Acta IMEKO 13, no. 3 (2024): 1–6. http://dx.doi.org/10.21014/actaimeko.v13i3.1628.

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The integration of digital technologies and Artificial Intelligence (DT&AI) in veterinary practice is one of the key topics to improve Herd Health Management (HHM). The HHM includes the prevention of diseases, the assessment of the welfare, and the sustainability production of farm animals. In dairy cattle farming, particular attention is paid to automatic cow detection and tracking, as such information is closely related to animal welfare and thus to possible health issues. Cows are highly social animals; therefore, a better comprehension of social context can help improve their managemen

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B.Nagaraju, R.Srija, M.Komala, and P.Dharani. "Applying Medical Technologies for Diagnoising Medical Images by Using Machine Learning." International Journal of Innovative Science and Research Technology 8, no. 5 (2023): 213–18. https://doi.org/10.5281/zenodo.7935352.

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Medical imaging is important in a variety of clinical activities, including early detection, monitoring, an opinion, and therapy evaluation of many medical diseases. grasp medical image analysis in a computer vision requires a solid grasp of the principles and operations of artificial neural networks, as well as deep literacy. Deep Learning Approach (DLA) in medical image processing is emerging as a rapidly increasing research subject. DLA has been widely utilised in medical imaging to characterise the presence or absence of a complaint. The vast majority of DLA executions focus on X-ray pictu

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Aljbori, Mohanned A., Amel Meddeb-Makhlouf, and Ahmed Fakhfakh. "A Review and Comparative Study of Works that Care is Monitoring Detection and Therapy of Children with Autism Spectrum Disorder." WSEAS TRANSACTIONS ON COMPUTER RESEARCH 12 (March 7, 2024): 244–63. http://dx.doi.org/10.37394/232018.2024.12.24.

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Recognizing human activity from video sequences and sensor data is one of the major challenges in human-computer interaction and computer vision. Health care is a rapidly developing field of technology and services. The latest development in this field is remote patient monitoring, which has many advantages in a rapidly evolving world. With relatively simple applications for monitoring patients within hospital rooms, technology has advanced to the point where a patient can be allowed to carry out normal daily activities at home while still being monitored using modern communication technologie

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Hashmani, Manzoor Ahmed, Syed Muslim Jameel, Syed Sajjad Hussain Rizvi, and Saurabh Shukla. "An Adaptive Federated Machine Learning-Based Intelligent System for Skin Disease Detection: A Step toward an Intelligent Dermoscopy Device." Applied Sciences 11, no. 5 (2021): 2145. http://dx.doi.org/10.3390/app11052145.

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The prevalence of skin diseases has increased dramatically in recent decades, and they are now considered major chronic diseases globally. People suffer from a broad spectrum of skin diseases, whereas skin tumors are potentially aggressive and life-threatening. However, the severity of skin tumors can be managed (by treatment) if diagnosed early. Health practitioners usually apply manual or computer vision-based tools for skin tumor diagnosis, which may cause misinterpretation of the disease and lead to a longer analysis time. However, cutting-edge technologies such as deep learning using the

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Cruz, Mateus, Samuel Mafra, Eduardo Teixeira, and Felipe Figueiredo. "Smart Strawberry Farming Using Edge Computing and IoT." Sensors 22, no. 15 (2022): 5866. http://dx.doi.org/10.3390/s22155866.

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Strawberries are sensitive fruits that are afflicted by various pests and diseases. Therefore, there is an intense use of agrochemicals and pesticides during production. Due to their sensitivity, temperatures or humidity at extreme levels can cause various damages to the plantation and to the quality of the fruit. To mitigate the problem, this study developed an edge technology capable of handling the collection, analysis, prediction, and detection of heterogeneous data in strawberry farming. The proposed IoT platform integrates various monitoring services into one common platform for digital

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Bharti B. Galat, Vivek B. Kute, and Nitin R. Chopde. "A survey on the cultivation of citrus fruits, particularly oranges, holds significant economic and nutritional value worldwide." International Journal of Science and Research Archive 15, no. 1 (2025): 850–55. https://doi.org/10.30574/ijsra.2025.15.1.1051.

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However, orange orchards are frequently threatened by a plethora of diseases that can drastically reduce yield and quality. Early and accurate disease detection is paramount for effective management and mitigation, preventing widespread crop loss and ensuring sustainable agricultural practices. Traditional disease identification methods often rely on visual inspection by experts, which can be time-consuming, subjective, and prone to human error. Moreover, the rapid spread of certain diseases necessitates swift and reliable diagnostic tools. In this context, the application of advanced technolo

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Jain, Muskan Naresh, Salah Mohammed Awad Al-Heejawi, Jamil R. Azzi, and Saeed Amal. "Digital Pathology and Ensemble Deep Learning for Kidney Cancer Diagnosis: Dartmouth Kidney Cancer Histology Dataset." Applied Biosciences 4, no. 1 (2025): 8. https://doi.org/10.3390/applbiosci4010008.

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Kidney cancer has become a major global health issue over time, showing how early detection can play a very important role in mediating the disease. Traditional histological image analysis is recognized as the clinical gold standard for diagnosis, although it is highly manual and labor-intensive. Due to this issue, many are interested in computer-aided diagnostic technologies to assist pathologists in their diagnostics. Specifically, deep learning (DL) has become a viable remedy in this field. Nonetheless, the capacity of existing DL models to extract comprehensive visual features for accurate

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