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Journal articles on the topic 'Detection of arecanut diseases'

Author: Grafiati

Published: 5 June 2025

Last updated: 1 August 2025

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1

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

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

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2

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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Satheesha, K. M., K. S. Rajanna, and Prasad K. Krishna. "A Review of the Literature on Arecanut Sorting and Grading Using Computer Vision and Image Processing." International Journal of Applied Engineering and Management Letters (IJAEML) 7, no. 2 (2023): 50–67. https://doi.org/10.5281/zenodo.7878092.

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<strong>Background/Purpose: </strong><em>These days, the involvement of computer science in agriculture and food science is expanding. Classification and fault identification of diverse products employ a variety of Artificial Intelligence (AI), soft computing approaches, and methodologies, which contribute to higher-quality products for consumers. The position of Arecanuts in the international and Indian markets, as well as the application of computer vision and image processing to a system for categorizing and grading Arecanuts, are the main topics of this article.</em> <strong>Objective: </s

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Mallikarjuna, S. B., Palaiahnakote Shivakumara, Vijeta Khare, et al. "CNN BASED METHOD FOR MULTI-TYPE DISEASED ARECANUT IMAGE CLASSIFICATION." Malaysian Journal of Computer Science 34, no. 3 (2021): 255–65. http://dx.doi.org/10.22452/mjcs.vol34no3.3.

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Arecanut image classification is a challenging task to the researchers and in this paper a new combined approach of multi-gradient images and deep convolutional neural networks for multi-type arecanut image classification is presented. To enhance the fine details in arecanut images affected by different diseases, namely, rot, split and rot-split, we propose to explore multiple-Sobel masks for convolving with the input image. Although, the images suffer from distortion due to disease infection, this masking operation helps to enhance the fine details. We believe that the fine details provide vi

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Chandrashekarappa, Niranjan Murthy, Sanjay Pande Mysore Bhagwan, and Kotreshi Shivabasappa Nagur. "Efficient data sensing and monitoring model for areca nut precision farming with wireless sensor network." Indonesian Journal of Electrical Engineering and Computer Science 25, no. 3 (2022): 1549. http://dx.doi.org/10.11591/ijeecs.v25.i3.pp1549-1562.

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<span>Arecanut plays a prominent role in economic life in India; it produces ‘betel nut’ which is primarily used for the masticatory purpose. Nutrient’s cycle and environmental factors impact the forming, these impacts can be minimized through sensing technology i.e., wireless sensor network incorporated with internet of things (IoT). Designing of sensing technologies is considered as primary steps in achieving the arecanut production through precision agriculture; This research focuses on designing and developing an efficient monitoring mechanism named efficient data sensing and monitor

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14

Dinesh S. "MobileNetV3Small-CM FusionNet: A Lightweight Deep Learning Framework for Multi-Class Arecanut Disease Classification Using Feature Fusion." Journal of Information Systems Engineering and Management 10, no. 4 (2025): 923–34. https://doi.org/10.52783/jisem.v10i4.10127.

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This study presents a novel deep learning-based framework, MobileNetV3Small-CM FusionNet, for the multi-class classification of arecanut plant diseases. The proposed model combines lightweight convolutional features extracted from the MobileNetV3Small architecture with handcrafted color moment descriptors (mean, standard deviation, and skewness) to enhance classification accuracy, particularly for visually similar and minority classes. A comprehensive dataset containing images of healthy and diseased arecanut samples was used for training, validation, and testing. The model was benchmarked aga

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Nair, Smita, Ramaswamy Manimekalai, Soumya Vadakke Purayil, and Govind P. Rao. "Taqman quantitative PCR for detection of Indian arecanut yellow leaf disease phytoplasma." Phytopathogenic Mollicutes 5, no. 2 (2015): 113. http://dx.doi.org/10.5958/2249-4677.2015.00070.5.

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Nair, Smita, O. M. Roshna, V. P. Soumya, et al. "Real-time PCR technique for detection of arecanut yellow leaf disease phytoplasma." Australasian Plant Pathology 43, no. 5 (2014): 527–29. http://dx.doi.org/10.1007/s13313-014-0278-7.

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17

International, Journal of Medical Science and Advanced Clinical Research (IJMACR). "A study to assess and correlate the habit behaviours among oral submucous fibrosis, leukoplakia, oral lichen planus in Udaipur Population- A Cross-Sectional Study." International Journal of Medical Science and Advanced Clinical Research (IJMACR) 8, no. 2 (2025): 192–212. https://doi.org/10.5281/zenodo.15395323.

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<strong>Abstract</strong> <strong>Background</strong>: Oral mucosal lesions (OMLs), including oral submucous fibrosis (OSMF), leukoplakia, and oral lichen planus (OLP), are linked to tobacco and arecanut use, posing significant oral health risks.  <strong>Aim & Objectives</strong>: This study assessed habit behaviors and their correlation with OSMF, leukoplakia, and OLP severity in Udaipur’s population.  <strong>Methods</strong>: A cross-sectional study of 150 patients evaluated demographic data, habits, and clinical findings. Statistical analysis (ANOVA) determined associa

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Balanagouda, Patil, Hegde Vinayaka, H. P. Maheswarappa, and H. Narayanaswamy. "Phytophthora diseases of arecanut in India: prior findings, present status and future prospects." Indian Phytopathology 74, no. 3 (2021): 561–72. http://dx.doi.org/10.1007/s42360-021-00382-8.

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Jayarajan, Keerthana, Greena K.K., Daliyamol ., Vinayaka Hedge, and Prathibha V.H. "Scenario of fungal leaf spot and leaf blight diseases in coconut and arecanut." Journal of Mycopathological research 62, no. 1 (2024): 7–20. http://dx.doi.org/10.57023/jmycr.62.1.2024.007.

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20

Rajeev, G., V. R. Prakash, M. Mayil Vaganan, M. Sasikala, J. J. Solomon, and G. M. Nair. "Microscopic and polyclonal antibody-based detection of yellow leaf disease of arecanut (Areca catechuL.)." Archives Of Phytopathology And Plant Protection 44, no. 11 (2011): 1093–104. http://dx.doi.org/10.1080/03235408.2010.482402.

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21

Ganapathi, Harsha Keladi, and Md Moinuddin Bhuiyan. "A Comprehensive Evaluation of Machine Learning Algorithms for Harvested Arecanut Variety Classification and Detection." Procedia Computer Science 258 (2025): 4147–56. https://doi.org/10.1016/j.procs.2025.04.665.

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22

Salunke, AS, and SS Honnungar. "Development of a true density-based automated quality grading device for unboiled arecanut kernels." African Journal of Food, Agriculture, Nutrition and Development 24, no. 8 (2024): 24298–318. http://dx.doi.org/10.18697/ajfand.133.24400.

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The automated sorting of arecanut kernels is a significant challenge that has not been effectively addressed thus far. Scientific grading techniques are necessary given the paradigm change toward investigating alternative uses for arecanuts in industry and the medical field. This research work emphasizes the relatively unexplored aspect of the post-harvest process; quality grading of kernels based on physical properties. It aimed to develop a novel approach for classifying unboiled (Chali) arecanut kernels cultivated in Goa, India based on their true density, using a combination of mechanical

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Shaji, Jilssa, Greeshma Balakrishnan, Nilofer Halim, Lakshmi Jayaraj, and Rumaisha . "Potentially -malignant disorders." Journal of Otolaryngology-ENT Research 14, no. 2 (2022): 44–47. http://dx.doi.org/10.15406/joentr.2022.14.00504.

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It is estimated that more than one million new oral cancer cases are being detected annually in the Indian subcontinent, of which 90% are oral squamous cell carcinoma (OSCC). Oral potentially malignant disorder (OPMD) are associated with increased rate of occurrence of OSCC of lips or oral cavity. Transformation of oral cancer from OPMD is common, especially in South Asian countries like India, where tobacco and arecanut consumption is prevalent. Early diagnosis and timely treatment of PMD’s may help to prevent its malignant transformation into oral cancer. The aim of the article is to highlig

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Muddumadiah, Chaithra, Madhupriya, Shailendra Kumar, Ramaswamy Manimekalai, and Govind Pratap Rao. "Detection and characterization of 16SrI-B phytoplasmas associated with yellow leaf disease of arecanut palm in India." Phytopathogenic Mollicutes 4, no. 2 (2014): 77. http://dx.doi.org/10.5958/2249-4677.2014.00585.4.

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Niranjan, Murthy Chandrashekarappa1, Mysore Bhagwan2 SanjayPande, and Shivabasappa Nagur2 Kotreshi. "Efficient data sensing and monitoring model for areca nut precision farming with wireless sensor network." Indonesian Journal of Electrical Engineering and Computer Science 25, no. 3 (2022): 1549–62. https://doi.org/10.11591/ijeecs.v25.i3.pp1549-1562.

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Areca nut plays a prominent role in economic life in India; it produces ‘betel nut’ which is primarily used for the masticatory purpose. Nutrient’s cycle and environmental factors impact the forming, these impacts can be minimized through sensing technology i.e., wireless sensor network incorporated with internet of things (IoT). Designing of sensing technologies is considered as primary steps in achieving the arecanut production through precision agriculture; This research focuses on designing and developing an efficient monitoring mechanism named efficient data sensing and

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Balanagouda, Patil, Shankarappa Sridhara, Sandip Shil, et al. "Assessment of the Spatial Distribution and Risk Associated with Fruit Rot Disease in Areca catechu L." Journal of Fungi 7, no. 10 (2021): 797. http://dx.doi.org/10.3390/jof7100797.

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Phytophthora meadii (McRae) is a hemibiotrophic oomycete fungus that infects tender nuts, growing buds, and crown regions, resulting in fruit, bud, and crown rot diseases in arecanut (Areca catechu L.), respectively. Among them, fruit rot disease (FRD) causes serious economic losses that are borne by the growers, making it the greatest yield-limiting factor in arecanut crops. FRD has been known to occur in traditional growing areas since 1910, particularly in Malnad and coastal tracts of Karnataka. Systemic surveys were conducted on the disease several decades ago. The design of appropriate ma

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Karmakar, Shaswata, Dipanjan Das, Baishakhi Modak, Madhurya Kedlaya, Anneysa Basu, and Aratrika Mukherjee. "Exploring the Links Between Chronic Periodontitis and Oral Cancer: An Update." RGUHS Journal of Dental Sciences 13 (2021): 22–31. http://dx.doi.org/10.26715/rjds.13_1_5.

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Oral carcinoma is a major public health concern worldwide due to its increasing mortality. Apart from alcohol consumption, tobacco and arecanut chewing, other risk factors for oral cancer include chronic infection and inflammation, poor nutritional status, chronic trauma to the oral soft tissues etc. Studies have also found that the risk of developing oral cancer may increase with periodontal diseases like periodontitis. Chronic periodontitis is a multifactorial, inflammatory disease of the periodontium primarily caused by the pathogenic microorganisms present in dental plaque biofilm, ultimat

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Gopinath, Rajeev, Kandothum Purath Deesma, Vadakke Purayil Soumya, Chandra Mohanan, and Manimekalai Ramaswamy. "Application of light microscopic staining techniques for the detection of phytoplasmas in yellow leaf disease affected arecanut palms in India." Phytopathogenic Mollicutes 6, no. 2 (2016): 77. http://dx.doi.org/10.5958/2249-4677.2016.00013.x.

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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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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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Naik, Gangadhara Bheema, Suresh Patil, Manjanatha Krishnappa Naik, Bhavana, and Priya Nagnur. "First report of a ‘Candidatus Phytoplasma australasia’ associated with crown chocking of arecanut palm in India." Phytopathogenic Mollicutes 12, no. 2 (2022): 128–34. http://dx.doi.org/10.5958/2249-4677.2022.00053.6.

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Sai, Mandava Siva, Vighnesh Anon, MD Shakir Alam, and Vinitha S. "LEAF DISEASES DETECTION AND MEDICATION." International Journal of Electronics Engineering and Applications IX, no. I (2021): 01. http://dx.doi.org/10.30696/ijeea.ix.i.2021.01-07.

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Gittleman, Janie. "Early Detection of Occupational Diseases." Industrial and Labor Relations Review 42, no. 1 (1988): 126. http://dx.doi.org/10.2307/2523180.

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Sheinerman, Kira S., and Samuil R. Umansky. "Early detection of neurodegenerative diseases." Cell Cycle 12, no. 1 (2012): 1–2. http://dx.doi.org/10.4161/cc.23067.

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Pol, Vaishnavi, Vaishali Waghmare, Rupali Shinde, and Prof R. R. Suryavanshi. "Eye Diseases Detection using ML." International Journal for Research in Applied Science and Engineering Technology 12, no. 5 (2024): 3602–5. http://dx.doi.org/10.22214/ijraset.2024.62403.

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Abstract: This paper presents a systematic recent advancement in the application of machine learning (ML) techniques for the detection of eye diseases. With the increasing prevalence of ocular conditions worldwide, there is a growing need for automated and accurate diagnostic tools to assist clinicians in early detection and intervention. Early detection of eye diseases is critical, as individuals who have a higher risk of developing eye diseases include those with diabetes, those over 60, those with a family history of eye diseases, and those who have had eye surgery or injuries. Treatment of

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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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Aleynikov, A. F., and V. I. Toropov. "Automated diseases detection of plant diseases in space greenhouses." IOP Conference Series: Materials Science and Engineering 1155, no. 1 (2021): 012070. http://dx.doi.org/10.1088/1757-899x/1155/1/012070.

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Singh, Shivangi. "Leaf Disease Detection." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (2021): 3324–29. http://dx.doi.org/10.22214/ijraset.2021.36836.

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Agriculture is a key source of livelihood. Agriculture provides employment opportunities for village people on a large scale in developing countries like India. India's agriculture consists of the many crops and consistent with survey nearly 70% population is depends on agriculture. Most of Indian farmers are adopting manual cultivation thanks to lagging of technical knowledge. Farmers are unaware of what quite crops that grows well on their land. When plants are suffering from heterogeneous diseases through their leaves which will effect on the production of agriculture and profitable loss, a

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A, Dr Arudra. "DETECTA – MULTIPLE DISEASE DETECTION." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem31416.

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Machine Learning Techniques for Predictive Analytics in Healthcare There are multiple techniques in machine learning that can in a variety of industries, do predictive analytics on large amounts of data. Predictive analytics in healthcare is a difficult endeavor, but it can eventually assist practitioners in making timely decisions regarding patients' health and treatment based on massive data. Diseases like Breast cancer, diabetes, and heart-related diseases are causing many deaths globally but most of these deaths are due to the lack of timely check-ups of the diseases. The above problem occ

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Zhao, Langyue, Yiquan Wu, Xudong Luo, and Yubin Yuan. "Automatic Defect Detection of Pavement Diseases." Remote Sensing 14, no. 19 (2022): 4836. http://dx.doi.org/10.3390/rs14194836.

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Pavement disease detection is an important task for ensuring road safety. Manual visual detection requires a significant amount of time and effort. Therefore, an automated road disease identification technique is required to guarantee that city tasks are performed. However, due to the irregular shape and large-scale differences in road diseases, as well as the imbalance between the foreground and background, the task is challenging. Because of this, we created the deep convolution neural network—DASNet, which can be used to identify road diseases automatically. The network employs deformable c

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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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Chen, Jiayi. "Using CNN for detection of diseases." Journal of Physics: Conference Series 1936, no. 1 (2021): 012022. http://dx.doi.org/10.1088/1742-6596/1936/1/012022.

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Arinicheva, I. V., I. V. Arinichev, and Z. D. Darmilova. "Cereal fungal diseases detection using autoencoders." IOP Conference Series: Earth and Environmental Science 949, no. 1 (2022): 012048. http://dx.doi.org/10.1088/1755-1315/949/1/012048.

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Abstract According to the data of the Food and Agriculture Organization of the United Nations (FAO), diseases and pests destroy 20-40% of the world’s agricultural crops. At the same time, fungal diseases cause enormous economic damage. Farmers suffer significant financial losses every year due to fungal diseases. It is very important accurately and at an early stage to identify the symptoms of the disease in order to take the necessary measures to combat it in a timely manner. Symptoms of fungal diseases often appear in the form of spots around the infected areas, so the initial detection of t

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Santhasheela, K., and Deepan Chakravarthi AV. "Image processing in plant diseases detection." International Journal of Engineering in Computer Science 1, no. 2 (2019): 10–15. http://dx.doi.org/10.33545/26633582.2019.v1.i2a.13.

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Tasoulas, Jason, Efstratios Patsouris, Constantinos Giaginis, and Stamatios Theocharis. "Salivaomics for oral diseases biomarkers detection." Expert Review of Molecular Diagnostics 16, no. 3 (2016): 285–95. http://dx.doi.org/10.1586/14737159.2016.1133296.

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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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KOLODNY, EDWIN H. "Early Detection of Lysosomal Storage Diseases." Annals of the New York Academy of Sciences 477, no. 1 Mental Retard (1986): 312–20. http://dx.doi.org/10.1111/j.1749-6632.1986.tb40350.x.

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LIGTENBERG, A. J. M., J. J. DE SOET, E. C. I. VEERMAN, and A. V. N. AMERONGEN. "Oral Diseases: From Detection to Diagnostics." Annals of the New York Academy of Sciences 1098, no. 1 (2007): 200–203. http://dx.doi.org/10.1196/annals.1384.040.

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Adhikari, Aayush, Bisoj Bishwakrama, Charchit Regmi, Dipesh Giri, and Suramya Sharma Dahal. "Poaceae Diseases Detection using Machine Learning." KEC Journal of Science and Engineering 8, no. 1 (2024): 117–22. http://dx.doi.org/10.3126/kjse.v8i1.69278.

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Agriculture is the number one supply of livelihood for approximately more than 66% of the Nepali population and Poaceae class is one of the most important meal grains of Nepal. Poaceae plant sicknesses are the major causes to lessen the manufacturing & goodness of meals. Nutrient deficiency of Nitrogen, Potassium and Phosphorus.can also cause plants to not grow to their full strength. Identification and preventing such diseases as well as nutrient deficiency can enhance the growth of Poaceae plants.

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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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