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Deep learning for image- based plant disease detection / Mohamad Lokman Zahari

Deep learning methods that are the Convolution Neural Network can be utilized to classify the plant disease. In addition, Sliding Windows methods also help to create dataset in ease. This research will lead as one of future references in the modern agricultural sector. Plant disease has been identif...

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Main Author: Zahari, Mohamad Lokman
Format: Student Project
Language:English
Published: 2020
Subjects:
Electronics
Detectors. Sensors. Sensor networks
Computer engineering. Computer hardware
Malaysia
Online Access:https://ir.uitm.edu.my/id/eprint/44324/1/44324.pdf
https://ir.uitm.edu.my/id/eprint/44324/
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spelling my.uitm.ir.443242024-10-25T04:50:39Z https://ir.uitm.edu.my/id/eprint/44324/ Deep learning for image- based plant disease detection / Mohamad Lokman Zahari Zahari, Mohamad Lokman Electronics Detectors. Sensors. Sensor networks Computer engineering. Computer hardware Malaysia Deep learning methods that are the Convolution Neural Network can be utilized to classify the plant disease. In addition, Sliding Windows methods also help to create dataset in ease. This research will lead as one of future references in the modern agricultural sector. Plant disease has been identified as a significant threat to food security, as it significantly decreases crop yield and compromises its consistency classification as human in the existence of plant disease is. Manual detection is limited only to small-scale agriculture. Therefore, the automatic detection of crop diseases in the agricultural sector is very important as it will enable farmers to keep track of the underlying diseases from time to time. Therefore, the purpose of this project is sliding window is used to produce a dataset. The sliding window will help the image shifter to generate faster and larger datasets. Deep convolutional neural network is implemented in order to classify diseases through the use of a dataset of images of healthy plant leaves collected under controlled conditions using Matlab platform. In this research, a number of 8554 data of each leaf set with different angles and scales are used to perform the pre-train the dataset by using Convolutional Neural Network platform. The experimental results show a good precision of 94.81 percent of testing average result, suggesting as successful classification rate. This project also can make a farmers easily to classify the disease that affected their plants. 2020-07 Student Project NonPeerReviewed text en https://ir.uitm.edu.my/id/eprint/44324/1/44324.pdf Deep learning for image- based plant disease detection / Mohamad Lokman Zahari. (2020) [Student Project] <http://terminalib.uitm.edu.my/44324.pdf> (Unpublished)
institution Universiti Teknologi Mara
building Tun Abdul Razak Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Mara
content_source UiTM Institutional Repository
url_provider http://ir.uitm.edu.my/
language English
topic Electronics
Detectors. Sensors. Sensor networks
Computer engineering. Computer hardware
Malaysia
spellingShingle Electronics
Detectors. Sensors. Sensor networks
Computer engineering. Computer hardware
Malaysia
Zahari, Mohamad Lokman
Deep learning for image- based plant disease detection / Mohamad Lokman Zahari
description Deep learning methods that are the Convolution Neural Network can be utilized to classify the plant disease. In addition, Sliding Windows methods also help to create dataset in ease. This research will lead as one of future references in the modern agricultural sector. Plant disease has been identified as a significant threat to food security, as it significantly decreases crop yield and compromises its consistency classification as human in the existence of plant disease is. Manual detection is limited only to small-scale agriculture. Therefore, the automatic detection of crop diseases in the agricultural sector is very important as it will enable farmers to keep track of the underlying diseases from time to time. Therefore, the purpose of this project is sliding window is used to produce a dataset. The sliding window will help the image shifter to generate faster and larger datasets. Deep convolutional neural network is implemented in order to classify diseases through the use of a dataset of images of healthy plant leaves collected under controlled conditions using Matlab platform. In this research, a number of 8554 data of each leaf set with different angles and scales are used to perform the pre-train the dataset by using Convolutional Neural Network platform. The experimental results show a good precision of 94.81 percent of testing average result, suggesting as successful classification rate. This project also can make a farmers easily to classify the disease that affected their plants.
format Student Project
author Zahari, Mohamad Lokman
author_facet Zahari, Mohamad Lokman
author_sort Zahari, Mohamad Lokman
title Deep learning for image- based plant disease detection / Mohamad Lokman Zahari
title_short Deep learning for image- based plant disease detection / Mohamad Lokman Zahari
title_full Deep learning for image- based plant disease detection / Mohamad Lokman Zahari
title_fullStr Deep learning for image- based plant disease detection / Mohamad Lokman Zahari
title_full_unstemmed Deep learning for image- based plant disease detection / Mohamad Lokman Zahari
title_sort deep learning for image- based plant disease detection / mohamad lokman zahari
publishDate 2020
url https://ir.uitm.edu.my/id/eprint/44324/1/44324.pdf
https://ir.uitm.edu.my/id/eprint/44324/
_version_ 1814939861455470592
score 13.211869

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