Corn leaf disease detection using deep convolution neural network

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023.

Detalhes bibliográficos
Principais autores:	Rabbi, Rawhatur, Arefin, Mohammad Yasin, Turna, Iffat Fahmida, Zannat, Zahra
Outros Autores:	Karim, Dewan Ziaul
Formato:	Tese
Idioma:	English
Publicado em:	Brac University 2023
Assuntos:	CNN Deep learning Image processing Machine learning Proposed model Transfer learning Cognitive learning theory (Deep learning) Neural networks (Computer science)
Acesso em linha:	http://hdl.handle.net/10361/20155

id	10361-20155
record_format	dspace
spelling	10361-201552023-08-29T21:03:00Z Corn leaf disease detection using deep convolution neural network Rabbi, Rawhatur Arefin, Mohammad Yasin Turna, Iffat Fahmida Zannat, Zahra Karim, Dewan Ziaul Department of Computer Science and Engineering, Brac University CNN Deep learning Image processing Machine learning Proposed model Transfer learning Machine learning Cognitive learning theory (Deep learning) Neural networks (Computer science) This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023. Cataloged from PDF version of thesis. Includes bibliographical references (pages 42-43). Detecting corn leaf diseases helps farmers identify and treat impacted crops. Early disease identification reduces crop loss. Manual leaf diagnostic imaging takes time and is prone to mistakes. This thesis proposes a deep convolutional neural network (CNN) model for autonomous corn leaf disease identification. PlantVillage and PlantDoc were utilized. The dataset contains 4,188 photos of healthy maize leaves and three corn leaf illnesses. The photos have disease labels. We rotated, flipped, and scaled images for augmentation. After augmentation, the total number of photos in the dataset is about 12,000. We trained our CNN model using pre-trained ar chitectures like InceptionResNetV2, MobileNetV2, ResNet50, VGG19, InceptionV3, VGG16, and DenseNet201. These architectures were chosen for their image feature extraction and large dataset learning capabilities. We used transfer learning to fine tune a model using a pre-trained model. The model accurately detects corn leaf diseases in new photos. The model is computationally light, making it suited for smartphones and drones. A maize leaf disease detection mobile app was created using the proposed CNN model. The application can detect corn leaves uploaded by anyone. An API analyzes an image using our proposed model from the device’s camera or gallery when a user selects it. Rawhatur Rabbi Mohammad Yasin Arefin Iffat Fahmida Turna Zahra Zannat B. Computer Science 2023-08-29T09:15:42Z 2023-08-29T09:15:42Z 2023 2023-01 Thesis ID: 19101422 ID: 19101317 ID: 19101542 ID: 19101559 http://hdl.handle.net/10361/20155 en Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 43 pages application/pdf Brac University
institution	Brac University
collection	Institutional Repository
language	English
topic	CNN Deep learning Image processing Machine learning Proposed model Transfer learning Machine learning Cognitive learning theory (Deep learning) Neural networks (Computer science)
spellingShingle	CNN Deep learning Image processing Machine learning Proposed model Transfer learning Machine learning Cognitive learning theory (Deep learning) Neural networks (Computer science) Rabbi, Rawhatur Arefin, Mohammad Yasin Turna, Iffat Fahmida Zannat, Zahra Corn leaf disease detection using deep convolution neural network
description	This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023.
author2	Karim, Dewan Ziaul
author_facet	Karim, Dewan Ziaul Rabbi, Rawhatur Arefin, Mohammad Yasin Turna, Iffat Fahmida Zannat, Zahra
format	Thesis
author	Rabbi, Rawhatur Arefin, Mohammad Yasin Turna, Iffat Fahmida Zannat, Zahra
author_sort	Rabbi, Rawhatur
title	Corn leaf disease detection using deep convolution neural network
title_short	Corn leaf disease detection using deep convolution neural network
title_full	Corn leaf disease detection using deep convolution neural network
title_fullStr	Corn leaf disease detection using deep convolution neural network
title_full_unstemmed	Corn leaf disease detection using deep convolution neural network
title_sort	corn leaf disease detection using deep convolution neural network
publisher	Brac University
publishDate	2023
url	http://hdl.handle.net/10361/20155
work_keys_str_mv	AT rabbirawhatur cornleafdiseasedetectionusingdeepconvolutionneuralnetwork AT arefinmohammadyasin cornleafdiseasedetectionusingdeepconvolutionneuralnetwork AT turnaiffatfahmida cornleafdiseasedetectionusingdeepconvolutionneuralnetwork AT zannatzahra cornleafdiseasedetectionusingdeepconvolutionneuralnetwork
_version_	1814309223500087296

Corn leaf disease detection using deep convolution neural network

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