Temat: disease detection - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: Development of the hardware and software complex for fertilizer application on agricultural fields
Rozwój sprzętu komputerowego i kompleksowego oprogramowania dla aplikacjinawozu na polach rolniczych
Autorzy:: Ganchenko, V.
Doudkin, A.
Petrovsky, A.
Pawłowski, T.
Powiązania:: https://bibliotekanauki.pl/articles/336244.pdf
Data publikacji:: 2014
Wydawca:: Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Maszyn Rolniczych
Tematy:: precision agriculture
disease detection
object classification
Opis:: In the article developing of hardware and software complex for fertilizer application on agricultural fields is described. The complex is intended for environmental pressures reduction in case of treatment and prevention of agricultural vegetation diseases. The developed technique of data obtaining by UAV, processing of remote sensing data and preparing of control data for system of fertilizer application is considered.
Źródło:: Journal of Research and Applications in Agricultural Engineering; 2014, 59, 1; 34-39
1642-686X
2719-423X
Pojawia się w:: Journal of Research and Applications in Agricultural Engineering
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: Artificial intelligence technologies in precision agriculture
Autorzy:: Pawłowski, T.
Ganchenko, V.
Doudkin, A.
Petrovsky, A.
Powiązania:: https://bibliotekanauki.pl/articles/337083.pdf
Data publikacji:: 2013
Wydawca:: Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Maszyn Rolniczych
Tematy:: precision agriculture
perceptron
disease detection
object classification
training sample
Opis:: Image processing, object classification and artificial neural network algorithms are considered in the paper applying to disease area recognition of agricultural field images. The images are presented as reduced normalized histograms. The classification is carried out for RGB-and HSV-space by using a multilayer perceptron.
Źródło:: Journal of Research and Applications in Agricultural Engineering; 2013, 58, 2; 119-126
1642-686X
2719-423X
Pojawia się w:: Journal of Research and Applications in Agricultural Engineering
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 3.

Tytuł:: Tomato disease detection model based on densenet and transfer learning
Autorzy:: Bakr, Mahmoud
Abdel-Gaber, Sayed
Nasr, Mona
Hazman, Maryam
Powiązania:: https://bibliotekanauki.pl/articles/2097440.pdf
Data publikacji:: 2022
Wydawca:: Polskie Towarzystwo Promocji Wiedzy
Tematy:: leaf disease detection
convolutional neural network
deep learning
transfer learning
Opis:: Plant diseases are a foremost risk to the safety of food. They have the potential to significantly reduce agricultural products quality and quantity. In agriculture sectors, it is the most prominent challenge to recognize plant diseases. In computer vision, the Convolutional Neural Network (CNN) produces good results when solving image classification tasks. For plant disease diagnosis, many deep learning architectures have been applied. This paper introduces a transfer learning based model for detecting tomato leaf diseases. This study proposes a model of DenseNet201 as a transfer learning-based model and CNN classifier. A comparison study between four deep learning models (VGG16, Inception V3, ResNet152V2 and DenseNet201) done in order to determine the best accuracy in using transfer learning in plant disease detection. The used images dataset contains 22930 photos of tomato leaves in 10 different classes, 9 disorders and one healthy class. In our experimental, the results shows that the proposed model achieves the highest training accuracy of 99.84% and validation accuracy of 99.30%.
Źródło:: Applied Computer Science; 2022, 18, 2; 56--70
1895-3735
Pojawia się w:: Applied Computer Science
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 4.

Tytuł:: Identifying selected diseases of leaves using deep learning and transfer learning models
Autorzy:: Mimi, Afsana
Zohura, Sayeda Fatema Tuj
Ibrahim, Muhammad
Haque, Riddho Ridwanul
Farrok, Omar
Jabid, Taskeed
Ali, Md Sawkat
Powiązania:: https://bibliotekanauki.pl/articles/2204260.pdf
Data publikacji:: 2023
Wydawca:: Szkoła Główna Gospodarstwa Wiejskiego w Warszawie. Instytut Informatyki Technicznej
Tematy:: convolutional neural network
transfer learning
leaf disease detection
image classification
Opis:: Leaf diseases may harm plants in different ways, often causing reduced productivity and, at times, lethal consequences. Detecting such diseases in a timely manner can help plant owners take effective remedial measures. Deficiencies of vital elements such as nitrogen, microbial infections and other similar disorders can often have visible effects, such as the yellowing of leaves in Catharanthus roseus (bright eyes) and scorched leaves in Fragaria ×ananassa (strawberry) plants. In this work, we explore approaches to use computer vision techniques to help plant owners identify such leaf disorders in their plants automatically and conveniently. This research designs three machine learning systems, namely a vanilla CNN model, a CNN-SVM hybrid model, and a MobileNetV2-based transfer learning model that detect yellowed and scorched leaves in Catharanthus roseus and strawberry plants, respectively, using images captured by mobile phones. In our experiments, the models yield a very promising accuracy on a dataset having around 4000 images. Of the three models, the transfer learning-based one demonstrates the highest accuracy (97.35% on test set) in our experiments. Furthermore, an Android application is developed that uses this model to allow end-users to conveniently monitor the condition of their plants in real time.
Źródło:: Machine Graphics & Vision; 2023, 32, 1; 55--71
1230-0535
2720-250X
Pojawia się w:: Machine Graphics & Vision
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 5.

Tytuł:: Plant disease detection using ensembled CNN framework
Autorzy:: Mondal, Subhash
Banerjee, Suharta
Mukherjee, Subinoy
Sengupta, Diganta
Powiązania:: https://bibliotekanauki.pl/articles/27312905.pdf
Data publikacji:: 2022
Wydawca:: Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Tematy:: convolutional neural network
disease detection
ResNet-50
VGG-19
InceptionV3
Opis:: Agriculture exhibits the prime driving force for the growth of agro-based economies globally. In agriculture, detecting and preventing crops from the attacks of pests is a primary concern in today’s world. The early detection of plant disease becomes necessary in order to avoid the degradation of the yield of crop production. In this paper, we propose an ensemble-based convolutional neural network (CNN) architecture that detects plant disease from the images of a plant’s leaves. The proposed architecture considers CNN architectures like VGG-19, ResNet-50, and InceptionV3 as its base models, and the prediction from these models is used as an input for our meta-model (Inception-ResNetV2). This approach helped us build a generalized model for disease detection with an accuracy of 97.9% under test conditions.
Źródło:: Computer Science; 2022, 23 (3); 321--333
1508-2806
2300-7036
Pojawia się w:: Computer Science
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 6.

Tytuł:: Classification and detection of skin disease based on machine learning and image processing evolutionary models
Autorzy:: Bordoloi, Dibyahash
Singh, Vijay
Kaliyaperumal, Karthikeyan
Ritonga, Mahyudin
Jawarneh, Malik
Kassanuk, Thanwamas
Quiñonez-Choquecota, Jose
Powiązania:: https://bibliotekanauki.pl/articles/38700501.pdf
Data publikacji:: 2023
Wydawca:: Instytut Podstawowych Problemów Techniki PAN
Tematy:: skin disorder
machine learning
classification
image enhancement
image segmentation
disease detection
schorzenie skóry
nauczanie maszynowe
klasyfikacja
ulepszenie obrazu
segmentacja obrazów
wykrywanie choroby
Opis:: Skin disorders, a prevalent cause of illnesses, may be identified by studying their physical structure and history of the condition. Currently, skin diseases are diagnosed using invasive procedures such as clinical examination and histology. The examinations are quite effective and beneficial. This paper describes an evolutionary model for skin disease classification and detection based on machine learning and image processing. This model integrates image preprocessing, image augmentation, segmentation, and machine learning algorithms. The experimental investigation makes use of a dermatology data set. The model employs the machine learning methods: the support vector machine (SVM), the k-nearest neighbors (KNN), and random forest algorithms for image categorization and detection. This suggested methodology is beneficial for the accurate identification of skin disease using image analysis. The SVM algorithm achieved an accuracy of 98.8%. The KNN algorithm achieved a sensitivity of 91%. The specificity of KNN was 99%.
Źródło:: Computer Assisted Methods in Engineering and Science; 2023, 30, 2; 247-256
2299-3649
Pojawia się w:: Computer Assisted Methods in Engineering and Science
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 7.

Tytuł:: Year-round blueberry scorch virus detection in highbush blueberry
Wykrywanie wirusa oparzeliny borówki wysokiej w różnych okresach roku
Autorzy:: Paduch-Cichal, E.
Chodorska, M.
Kalinowska, E.
Komorowska, B.
Powiązania:: https://bibliotekanauki.pl/articles/11542904.pdf
Data publikacji:: 2014
Wydawca:: Uniwersytet Przyrodniczy w Lublinie. Wydawnictwo Uniwersytetu Przyrodniczego w Lublinie
Tematy:: plant cultivar
blueberry
blueberry scorch virus
scorch disease
plant disease
detection
Northern highbush blueberry
viral disease
Opis:: Viral diseases are a worldwide problem of blueberry which a major limiting factor for production. A survey for Blueberry scorch virus (BlScV) by DAS-ELISA in various organs of highbush blueberry conducted from May 2010 to April 2011, showed the occurrence of these virus in cvs Bluecrop and Herbert, which showing virus-like symptoms. Samples of plant materials (bud flower, flower, leaf, bark) were collected individually from each highbush blueberry plant of every cultivar. It was established that the detection of virus of each the investigated bushes cvs Bluecrop and Herbert depended on the tested plant materials as well as the period in which the tests were performed. The effectiveness of the virus detection varied for the investigated cultivars. The presence of the BlScV was confirmed in leaves samples with specific primer pair which amplifies a 430 bp fragment of the 5’-proximal ORF I [RNA-dependent RNA polymerase (RdRp)].
Celem przeprowadzonych badań było wykrywanie i identyfikacja wirusa oparzeliny borówki wysokiej (Blueberry scorch virus, BlScV) w różnych organach pobieranych z krzewów borówki wysokiej odmian Bluecrop i Herbert rosnących na plantacji produkcyjnej zlokalizowanej w centralnej Polsce. Badania byáy prowadzone w okresie od maja 2010 do kwietnia 2011 r. przy użyciu testu serologicznego DAS-ELISA. Próby materiału roślinnego (pąki kwiatowe, kwiaty, liście, kora) pobierano indywidualnie z krzewów kaĪdej z badanych odmian. Ustalono, że wykrywanie wirusów w krzewach odmian Bluecrop i Herbert zależało od testowanego organu oraz terminu, w którym przeprowadzono test. Obecność BlScV w krzewach badanych odmian potwierdzono przy pomocy techniki RT-PCR z wykorzystaniem starterów amplifikujących fragment 5’ genu kodującego polimerazą RNA zależną od RNA.
Źródło:: Acta Scientiarum Polonorum. Hortorum Cultus; 2014, 13, 3; 3-11
1644-0692
Pojawia się w:: Acta Scientiarum Polonorum. Hortorum Cultus
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 8.

Tytuł:: Update on the study of Alzheimer’s disease through artificial intelligence techniques
Autorzy:: Garea-Llano, Eduardo
Powiązania:: https://bibliotekanauki.pl/articles/27314235.pdf
Data publikacji:: 2023
Wydawca:: Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Tematy:: Alzheimer's disease
detection
progression
artificial intelligence
deep learning
Opis:: Alzheimer’s disease is the most common form of dementia that can cause a brain neurological disorder with progressive memory loss as a result of brain cell damage. Prevention and treatment of disease is a key challenge in today’s aging society. Accurate diagnosis of Alzheimer’s disease plays an important role in patient management, especially in the early stages of the disease, because awareness of risk allows patients to undergo preventive measures even before irreversible brain damage occurs. Over the years, techniques such as statistical modeling or machine learning algorithms have been used to improve understanding of this condition. The objective of the work is the study of the methods of detection and progression of Alzheimer’s disease through artificial intelligence techniques that have been proposed in the last three years. The methodology used was based on the search, selection, review, and analysis of the state of the art and the most current articles published on the subject. The most representative works were analyzed, which allowed proposing a taxonomic classification of the studied methods and on this basis a possible solution strategy was proposed within the framework of the project developed by the Cuban Center for Neurosciences based on the conditions more convenient in terms of cost and effectiveness and the most current trends based on the use of artificial intelligence techniques.
Źródło:: Journal of Automation Mobile Robotics and Intelligent Systems; 2023, 17, 2; 51--60
1897-8649
2080-2145
Pojawia się w:: Journal of Automation Mobile Robotics and Intelligent Systems
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 9.

Tytuł:: Improving coronary heart disease prediction by outlier elimination
Autorzy:: Riyaz, Lubna
Butt, Muheet Ahmed
Zaman, Majid
Powiązania:: https://bibliotekanauki.pl/articles/2097431.pdf
Data publikacji:: 2022
Wydawca:: Polskie Towarzystwo Promocji Wiedzy
Tematy:: coronary heart disease
machine learning
ensembles
outlier detection
framingham
Opis:: Nowadays, heart disease is the major cause of deaths globally. According to a survey conducted by the World Health Organization, almost 18 million people die of heart diseases (or cardiovascular diseases) every day. So, there should be a system for early detection and prevention of heart disease. Detection of heart disease mostly depends on the huge pathological and clinical data that is quite complex. So, researchers and other medical professionals are showing keen interest in accurate prediction of heart disease. Heart disease is a general term for a large number of medical conditions related to heart and one of them is the coronary heart disease (CHD). Coronary heart disease is caused by the amassing of plaque on the artery walls. In this paper, various machine learning base and ensemble classifiers have been applied on heart disease dataset for efficient prediction of coronary heart disease. Various machine learning classifiers that have been employed include k-nearest neighbor, multilayer percep-tron, multinomial naïve bayes, logistic regression, decision tree, random forest and support vector machine classifiers. Ensemble classifiers that have been used include majority voting, weighted average, bagging and boosting classifiers. The dataset used in this study is obtained from the Framingham Heart Study which is a long-term, ongoing cardiovascular study of people from the Framingham city in Massachusetts, USA. To evaluate the performance of the classifiers, various evaluation metrics including accuracy, precision, recall and f1 score have been used. According to our results, the best accuracy was achieved by logistic regression, random forest, majority voting, weighted average and bagging classifiers but the highest accuracy among these was achieved using weighted average ensemble classifier.
Źródło:: Applied Computer Science; 2022, 18, 1; 70--88
1895-3735
Pojawia się w:: Applied Computer Science
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 10.

Tytuł:: Molecular detection of Anaplasmataceae in blood samples from dogs
Autorzy:: Adamczyk, M.
Nowak, Z.
Gajda, E.
Bunkowska-Gawlik, K.
Perec-Matysiak, A.
Janaczyk, B.
Hildebrand, J.
Powiązania:: https://bibliotekanauki.pl/articles/6250.pdf
Data publikacji:: 2016
Wydawca:: Polskie Towarzystwo Parazytologiczne
Tematy:: molecular detection
Anaplasmataceae
blood sample
dog
animal disease
vector-borne disease
Anaplasma phagocytophilum
Neoehrlichia mikurensis
Źródło:: Annals of Parasitology; 2016, 62, Suppl.
0043-5163
Pojawia się w:: Annals of Parasitology
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 11.

Tytuł:: Detection and differentiation of Newcastle disease virus and influenza virus by using duplex real-time PCR
Autorzy:: Nidzworski, Dawid
Wasilewska, Edyta
Smietanka, Krzysztof
Szewczyk, Bogusław
Minta, Zenon
Powiązania:: https://bibliotekanauki.pl/articles/1039554.pdf
Data publikacji:: 2013
Wydawca:: Polskie Towarzystwo Biochemiczne
Tematy:: Newcastle disease virus
influenza virus
detection
differentiation
real-time PCR
duplex
Opis:: Newcastle disease virus (NDV), member of the Paramyxoviridae family and avian influenza virus (AIV), member of the Orthomyxoviridae family, are two main avian pathogens causing serious economic problems in poultry health. Both are enveloped, single-stranded, negative-sense RNA viruses and cause similar symptoms, ranging from sub-clinical infections to severe diseases, including decrease in egg production, acute respiratory syndrome, and high mortality. Similar symptoms hinder the differentiation of infection with the two viruses by standard veterinary procedures like clinical examination or necropsy. To overcome this problem, we have developed a new duplex real-time PCR assay for the detection and differentiation of these two viruses. Eighteen NDV strains, fourteen AIV strains, and twelve other (negative control) strains viruses were isolated from allantoic fluids of specific pathogen-free (SPF), embryonated eggs. Four-weeks-old SPF chickens were co-infected with both viruses (NDV - LaSota and AIV - H7N1). Swabs from cloaca and trachea were collected and examined. The results obtained in this study show that by using duplex real-time PCR, it was possible to detect and distinguish both viruses within less than three hours and with high sensitivity, even in case a bird was co-infected. Additionally, the results show the applicability of the real-time PCR assay in laboratory practice for the identification and differentiation of Newcastle disease and influenza A viruses in birds.
Źródło:: Acta Biochimica Polonica; 2013, 60, 3; 475-480
0001-527X
Pojawia się w:: Acta Biochimica Polonica
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 12.

Tytuł:: Prevalence of hydatidosis in pigs in the Lublin province (Poland) in the years 2005–2008
Autorzy:: Kozlowska-Loj, J.
Bartosik, K.
Loj-Maczulska, A.
Powiązania:: https://bibliotekanauki.pl/articles/2143274.pdf
Data publikacji:: 2011
Wydawca:: Polskie Towarzystwo Parazytologiczne
Tematy:: prevalence
hydatidosis
animal disease
pig
Lublin province
Polska
Echinococcus granulosus
detection
Opis:: In the years 2005–2008 hydatidosis caused by Echinococcus granulosus was detected in 163 607 (10.37%) out of 1 577 370 pigs slaughtered in the Lublin province. Similar prevalence (10.39%) was found in the years 2001–2004.
Źródło:: Wiadomości Parazytologiczne; 2011, 57, 4; 281-283
0043-5163
Pojawia się w:: Wiadomości Parazytologiczne
Dostawca treści:: Biblioteka Nauki