Comparison between Alexnet, Googlenet and ResNet-50 for brownspot, hispa and leafblast disease classification / Siti Maisarah Zainorzuli

Rice is a staple food for Malaysian, hence ensuring its production is essential. There is a large manufacturer of rice in Malaysia every year to contain the need of millions of Malaysians but still not sufficient. There are a number of causes for the decrease in production, such as poor management o...

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Main Author:	Zainorzuli, Siti Maisarah
Format:	Thesis
Language:	English
Published:	2023
Subjects:	SB Plant culture Pests and diseases
Online Access:	https://ir.uitm.edu.my/id/eprint/91113/1/91113.pdf
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id	my-uitm-ir.91113
record_format	uketd_dc
spelling	my-uitm-ir.911132024-03-18T00:55:53Z Comparison between Alexnet, Googlenet and ResNet-50 for brownspot, hispa and leafblast disease classification / Siti Maisarah Zainorzuli 2023 Zainorzuli, Siti Maisarah SB Plant culture Pests and diseases Rice is a staple food for Malaysian, hence ensuring its production is essential. There is a large manufacturer of rice in Malaysia every year to contain the need of millions of Malaysians but still not sufficient. There are a number of causes for the decrease in production, such as poor management of pests and diseases, fertiliser, and shortage of specialists. In Malaysia, paddy fields are infected by diseases and pest known as brown planthopper (BPH) which could potentially reduce the yield by about 30%-50% and 15%. The conventional method, a paddy disease specialist is required to identify and diagnose the paddy leaf disease. The paddy disease expert will obtain several samples of paddy leaf images from the farmer. Afterwards, the required sample was sent to the biotech laboratory so that the affected leaf can be analysed. The process for this method was time-consuming, inconvenient for the farmer and it was very costly. Since the 1990s, the use of computers and information technology has enhanced the methods of agriculture and been beneficial in the agricultural industry. Thus, with the application of Deep Learning method the diseases can be detected at the early stage. Precaution measures can be taken to lessen the damage as soon as possible. The objective of this work is to classify the types of paddy disease such as brownspot, leafblast and hispa by using CNN model. Thus, this research conducts an analysis using several types of CNN to validate and compare the performance of proposed model in terms of precision, recall, F1 score, and robustness with AlexNet and GoogleNet. Hence, the result shows ResNet-50 is exceptional in all indexes except time by using the optimal configuration namely learning rate at 0.001 and number of epochs at 30. The time of ResNet-50 (1626s) is close to AlexNet but worse than GoogleNet. For each index, ResNet-50 above 90%. Meanwhile AlexNet and GoogleNet only obtained 94.84% and 95% for accuracy, while the rest only obtained around 88% to 89%. All indexes of ResNet-50 are the first ranked then followed by GoogleNet. In short, ResNet-50 is a more accurate and precise model than others. 2023 Thesis https://ir.uitm.edu.my/id/eprint/91113/ https://ir.uitm.edu.my/id/eprint/91113/1/91113.pdf text en public masters Universiti Teknologi MARA (UiTM) College of Engineering Ahmat Ruslan, Fazlina
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
advisor	Ahmat Ruslan, Fazlina
topic	SB Plant culture Pests and diseases
spellingShingle	SB Plant culture Pests and diseases Zainorzuli, Siti Maisarah Comparison between Alexnet, Googlenet and ResNet-50 for brownspot, hispa and leafblast disease classification / Siti Maisarah Zainorzuli
description	Rice is a staple food for Malaysian, hence ensuring its production is essential. There is a large manufacturer of rice in Malaysia every year to contain the need of millions of Malaysians but still not sufficient. There are a number of causes for the decrease in production, such as poor management of pests and diseases, fertiliser, and shortage of specialists. In Malaysia, paddy fields are infected by diseases and pest known as brown planthopper (BPH) which could potentially reduce the yield by about 30%-50% and 15%. The conventional method, a paddy disease specialist is required to identify and diagnose the paddy leaf disease. The paddy disease expert will obtain several samples of paddy leaf images from the farmer. Afterwards, the required sample was sent to the biotech laboratory so that the affected leaf can be analysed. The process for this method was time-consuming, inconvenient for the farmer and it was very costly. Since the 1990s, the use of computers and information technology has enhanced the methods of agriculture and been beneficial in the agricultural industry. Thus, with the application of Deep Learning method the diseases can be detected at the early stage. Precaution measures can be taken to lessen the damage as soon as possible. The objective of this work is to classify the types of paddy disease such as brownspot, leafblast and hispa by using CNN model. Thus, this research conducts an analysis using several types of CNN to validate and compare the performance of proposed model in terms of precision, recall, F1 score, and robustness with AlexNet and GoogleNet. Hence, the result shows ResNet-50 is exceptional in all indexes except time by using the optimal configuration namely learning rate at 0.001 and number of epochs at 30. The time of ResNet-50 (1626s) is close to AlexNet but worse than GoogleNet. For each index, ResNet-50 above 90%. Meanwhile AlexNet and GoogleNet only obtained 94.84% and 95% for accuracy, while the rest only obtained around 88% to 89%. All indexes of ResNet-50 are the first ranked then followed by GoogleNet. In short, ResNet-50 is a more accurate and precise model than others.
format	Thesis
qualification_level	Master's degree
author	Zainorzuli, Siti Maisarah
author_facet	Zainorzuli, Siti Maisarah
author_sort	Zainorzuli, Siti Maisarah
title	Comparison between Alexnet, Googlenet and ResNet-50 for brownspot, hispa and leafblast disease classification / Siti Maisarah Zainorzuli
title_short	Comparison between Alexnet, Googlenet and ResNet-50 for brownspot, hispa and leafblast disease classification / Siti Maisarah Zainorzuli
title_full	Comparison between Alexnet, Googlenet and ResNet-50 for brownspot, hispa and leafblast disease classification / Siti Maisarah Zainorzuli
title_fullStr	Comparison between Alexnet, Googlenet and ResNet-50 for brownspot, hispa and leafblast disease classification / Siti Maisarah Zainorzuli
title_full_unstemmed	Comparison between Alexnet, Googlenet and ResNet-50 for brownspot, hispa and leafblast disease classification / Siti Maisarah Zainorzuli
title_sort	comparison between alexnet, googlenet and resnet-50 for brownspot, hispa and leafblast disease classification / siti maisarah zainorzuli
granting_institution	Universiti Teknologi MARA (UiTM)
granting_department	College of Engineering
publishDate	2023
url	https://ir.uitm.edu.my/id/eprint/91113/1/91113.pdf
_version_	1794192258303524864

Comparison between Alexnet, Googlenet and ResNet-50 for brownspot, hispa and leafblast disease classification / Siti Maisarah Zainorzuli

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