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CROP PESTS DETECTION USING FASTER-RCNN

Agriculture is an important sector in Malaysia as it produces food for the people and increases the country's income. Pests have always been a threat to agriculture. Pests can damage crops by eating leaves, stems and roots, resulting in a decrease in the market value of the crop and thus reduci...

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Bibliographic Details
Main Author: Esther, Wong Ching Ya
Format: Final Year Project Report
Language:English
Published: Universiti Malaysia Sarawak, (UNIMAS) 2023
Subjects:
SB Plant culture
Online Access:http://ir.unimas.my/id/eprint/44196/1/Esther%20Wong%20Ching%20Ya%20ft.pdf
http://ir.unimas.my/id/eprint/44196/
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spelling my.unimas.ir.441962024-01-18T01:46:10Z http://ir.unimas.my/id/eprint/44196/ CROP PESTS DETECTION USING FASTER-RCNN Esther, Wong Ching Ya SB Plant culture Agriculture is an important sector in Malaysia as it produces food for the people and increases the country's income. Pests have always been a threat to agriculture. Pests can damage crops by eating leaves, stems and roots, resulting in a decrease in the market value of the crop and thus reducing farmers' income. In addition, pests can spread viruses that kill crops, resulting in reduced crop yields. Most crop pests are small and difficult to detect with the human eye. Therefore, this project uses a set of evidence images (such as chewed leaves) instead of pest images to train the model. Several pest detection models that have been developed by other researchers have been reviewed. This project proposes a detection model using the Faster-RCNN pre-trained model. The model is fine-tuned to the project dataset. The performance of the model was evaluated. The model can make predictions on images, although only 28.1% accurate. Universiti Malaysia Sarawak, (UNIMAS) 2023 Final Year Project Report NonPeerReviewed text en http://ir.unimas.my/id/eprint/44196/1/Esther%20Wong%20Ching%20Ya%20ft.pdf Esther, Wong Ching Ya (2023) CROP PESTS DETECTION USING FASTER-RCNN. [Final Year Project Report] (Unpublished)
institution Universiti Malaysia Sarawak
building Centre for Academic Information Services (CAIS)
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Sarawak
content_source UNIMAS Institutional Repository
url_provider http://ir.unimas.my/
language English
topic SB Plant culture
spellingShingle SB Plant culture
Esther, Wong Ching Ya
CROP PESTS DETECTION USING FASTER-RCNN
description Agriculture is an important sector in Malaysia as it produces food for the people and increases the country's income. Pests have always been a threat to agriculture. Pests can damage crops by eating leaves, stems and roots, resulting in a decrease in the market value of the crop and thus reducing farmers' income. In addition, pests can spread viruses that kill crops, resulting in reduced crop yields. Most crop pests are small and difficult to detect with the human eye. Therefore, this project uses a set of evidence images (such as chewed leaves) instead of pest images to train the model. Several pest detection models that have been developed by other researchers have been reviewed. This project proposes a detection model using the Faster-RCNN pre-trained model. The model is fine-tuned to the project dataset. The performance of the model was evaluated. The model can make predictions on images, although only 28.1% accurate.
format Final Year Project Report
author Esther, Wong Ching Ya
author_facet Esther, Wong Ching Ya
author_sort Esther, Wong Ching Ya
title CROP PESTS DETECTION USING FASTER-RCNN
title_short CROP PESTS DETECTION USING FASTER-RCNN
title_full CROP PESTS DETECTION USING FASTER-RCNN
title_fullStr CROP PESTS DETECTION USING FASTER-RCNN
title_full_unstemmed CROP PESTS DETECTION USING FASTER-RCNN
title_sort crop pests detection using faster-rcnn
publisher Universiti Malaysia Sarawak, (UNIMAS)
publishDate 2023
url http://ir.unimas.my/id/eprint/44196/1/Esther%20Wong%20Ching%20Ya%20ft.pdf
http://ir.unimas.my/id/eprint/44196/
_version_ 1789430373481971712
score 13.211869

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