Wyniki wyszukiwania - BazTech

1

Classification and detection of skin disease based on machine learning and image processing evolutionary models

Bordoloi Dibyahash, Singh Vijay, Kaliyaperumal Karthikeyan, Ritonga Mahyudin, Jawarneh Malik, Kassanuk Thanwamas, Quiñonez-Choquecota Jose

Computer Assisted Methods in Engineering and Science

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2023

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Vol. 30, no. 2

247--256

EN

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%.

2

Plant disease detection using ensembled CNN framework

Mondal Subhash, Banerjee Suharta, Mukherjee Subinoy, Sengupta Diganta

Computer Science

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2022

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T. 23 (3)

321--333

EN

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.

3

Development of the hardware and software complex for fertilizer application on agricultural fields

Ganchenko V., Doudkin A., Petrovsky A., Pawłowski T.

Journal of Research and Applications in Agricultural Engineering

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2014

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Vol. 59, nr 1

34--39

EN

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.

4

Artificial intelligence technologies in precision agriculture

Pawłowski T., Ganchenko V., Doudkin A., Petrovsky A.

Journal of Research and Applications in Agricultural Engineering

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2013

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Vol. 58, nr 2

119--126

EN

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.

5

Road Disease Real Time Detection Model on Multi-Lever Fuzzy Filter

Qin G., Li Q.

Przegląd Elektrotechniczny

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2012

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R. 88, nr 9b

104-107

EN

A Multi-lever filter fuzzy detection model on road disease is taken forward combining with advantages of noisy decreasing algorithms and characteristics of noise and disease in road image. The experimental results indicate that the proposed model is more advanced in decreasing noisy and extracting disease. The application of the road disease edge detection was realized in embedded Linux system. The evaluation report will be output in real time and sent to the central database from the embedded mobile terminal, including the corresponding position information in the cooperative GIS.

PL

Technika road desease jest stosowana w przetwarzaniu obrazu do usuwania szumu z zamazanych obrazów i odzyskiwania obrazu krawędzi drogi. W artykule przedstawiono filtr wykorzystujący logikę rozmytą.