Plant disease diagnosis using deep transfer learning architectures- VGG19, MobileNetV2 and Inception-V3

Plant disease diagnosis using deep transfer learning architectures- VGG19, MobileNetV2 and Inception-V3

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2022.

Detalhes bibliográficos
Principais autores:	Kobra, Khadija-Tul, Suham, Rahmatul Rashid, Fairooz, Maisha
Outros Autores:	Uddin, Jia
Formato:	Tese
Idioma:	English
Publicado em:	Brac University 2022
Assuntos:	Plant diseases VGG19 MobileNetV2 Inception-v3 Crop Cognitive learning theory (Deep learning) Plant diseases > Diagnosis
Acesso em linha:	http://hdl.handle.net/10361/17334

Registros relacionados

Myocardial infarction detection using ECG signal applying deep learning techniques - ConvNet, VGG16, InceptionV3 and MobileNet
por: Promita, Samanta Tabassum, et al.
Publicado em: (2022)

Comparative analysis between Inception-v3 and other learning systems using facial expressions detection
por: Nivrito, AKM, et al.
Publicado em: (2016)

Deep neural network models for COVID-19 diagnosis from CT-Scan, explainability and analysis using trained models
por: Islam, Tahsin, et al.
Publicado em: (2021)

A comparative study of lung cancer prediction using deep learning
por: Mugdho, Aka Mohammad, et al.
Publicado em: (2023)

Detection of prodromal parkinson’s disease with fMRI data and deep neural network approaches
por: Shahriar, Farhan, et al.
Publicado em: (2021)

Kidney Disease detection and classification from CT Images using Watershed Segmentation and Deep Learning.
por: Hossain, Mohammad Sakib, et al.
Publicado em: (2023)

Skin disease detection and classification using deep learning
por: Shuvon, Mehedi Hasan, et al.
Publicado em: (2022)

Analyzing students’ concentration in online courses through Webcam
por: Asif, Md., et al.
Publicado em: (2024)

An efficient deep learning approach to detect retinal disease using optical coherence tomographic images
por: Khan, Farhan Sakib, et al.
Publicado em: (2022)

Skin cancer classification for seven types of skin lesions
por: Rahman, Md. Tawsifur, et al.
Publicado em: (2024)

Multi-classification Network for Detecting Skin Diseases using Deep Learning and XAI
por: Athina, Fahima Hasan, et al.
Publicado em: (2022)

A hybrid FL-Enabled ensemble approach for lung disease diagnosis leveraging fusion of SWIN transformer and CNN
por: Chowdhury, Asif Hasan, et al.
Publicado em: (2023)

Plant leaf disease identification
por: Hasan Mahin, Mohammad Rakibul, et al.
Publicado em: (2024)

Detection of skin diseases using deep learning
por: Shodoy, Masuk Ur Rahman
Publicado em: (2024)

An efficient deep learning approach for detecting Alzheimer’s disease using brain images
por: Sani, Mehedi Hasan, et al.
Publicado em: (2022)

Automatic waste classification using deep learning and computer vision techniques
por: Akash, MD., et al.
Publicado em: (2023)

Smart detection and classification of fungal disease in rice plants using image processing techniques
por: Rashed, Akib, et al.
Publicado em: (2024)

Citrus leaf disease detection by image processing
por: Chowdhury, Mahir Faisal, et al.
Publicado em: (2024)

A literature review on the efficacy of test kits for COVID-19
por: Sajid, Saiara Sajita
Publicado em: (2022)

A hybrid FL-enabled ensemble approach for lung disease diagnosis leveraging fusion of SWIN transformer and CNN
por: Chowdhury, Asif Hasan, et al.
Publicado em: (2023)

Identifying brain abnormalities using image processing and CNN models
por: Talukder, Ismat Shehrin, et al.
Publicado em: (2021)

Smoke detection using deep Convolutional neural network
por: Niloy, Wahidul Hasan, et al.
Publicado em: (2021)

Explainable breast cancer detection from Histopathology images using transfer learning and XAI
por: Farabi, Farhan, et al.
Publicado em: (2023)

Alzheimer’s Disease detection and classification using transfer learning techniques and ensembling operations on convolutional neural networks
por: Awwal, Alvina, et al.
Publicado em: (2021)

Detection of intracranial hemorrhage on CT scan images using convolutional neural network
por: Rahman, Afridi Ibn, et al.
Publicado em: (2022)

Tomato leaf disease detection using convolutional neural network
por: Hasan, Md. Riazul, et al.
Publicado em: (2023)

An efficient deep learning approach to predict heart failure from image data using ejection fraction
por: Tanvir, Nazmul Karim, et al.
Publicado em: (2023)

A deep learning approach for pneumonia classiﬁcation from chest X-Ray images with ensemble modelling and explainable AI
por: Akhter, Nasrin
Publicado em: (2023)

Covid-19 infected lung detection using machine learning
por: Islam, Md. Muntaha, et al.
Publicado em: (2022)

Critical retinal disease detection from optical coherence tomography images by deep convolutional neural network and explainable machine learning
por: Datta, Pranab, et al.
Publicado em: (2021)

In-silico based vaccine development: targeting nucleocapsid protein of SARS-CoV-2.
por: Iva, Yesmine Akter
Publicado em: (2022)

An efficient deep learning approach to detect citrus leaves disease
por: Emon, Shaharear Hossain, et al.
Publicado em: (2024)

Real-time crime detection using convolutional LSTM and YOLOv7
por: Sakiba, Cyrus, et al.
Publicado em: (2023)

A deep learning approach to depression detection based on Convolutional Neural Networks and Transfer Learning
por: Sarmi, Kaniz Fatima, et al.
Publicado em: (2022)

Simulation based distracted driver detection system using alcohol sensor and fusion of bioelectric signals to disseminate them through V2V/V2I to alleviate risky driving
por: Sikder, Md. Sarfaraj, et al.
Publicado em: (2019)

Domestication, production and utilization of new crops /
Publicado em: (1997)

An interpretable diagnosis of retinal diseases using vision transformer and Grad-CAM
por: Bhuiyan, Mahdi Hasan, et al.
Publicado em: (2024)

A text book of plant pathology /
por: Ashrafuzzaman, Hasan
Publicado em: (1991)

V.S. Naipaul : the Indian trilogy /
por: Mahanta, Namrata Rathore
Publicado em: (2004)

An interpretable deep learning approach to detect Alzheimer using MRI images
por: Oni, Farhan Anzum, et al.
Publicado em: (2023)