Weeds detection for agriculture using Convolutional Neural Network (CNN) algorithm / Khairun Nisa Mohammad Nasir

Weed detection and control are a focal point of agricultural research due to their harmful impact on crop productivity, competing for essential resources like sunlight, water, and nutrients. Modern agriculture recognizes weed detection systems as crucial tools to reduce the obstacles caused by weeds...

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Bibliographic Details
Main Author:	Mohammad Nasir, Khairun Nisa
Format:	Thesis
Language:	English
Published:	2024
Subjects:	Neural networks (Computer science)
Online Access:	https://ir.uitm.edu.my/id/eprint/95565/1/95565.pdf
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id	my-uitm-ir.95565
record_format	uketd_dc
spelling	my-uitm-ir.955652024-05-31T01:45:13Z Weeds detection for agriculture using Convolutional Neural Network (CNN) algorithm / Khairun Nisa Mohammad Nasir 2024 Mohammad Nasir, Khairun Nisa Neural networks (Computer science) Weed detection and control are a focal point of agricultural research due to their harmful impact on crop productivity, competing for essential resources like sunlight, water, and nutrients. Modern agriculture recognizes weed detection systems as crucial tools to reduce the obstacles caused by weeds, enhancing crop growth and yield. This project aims to develop a weed detection prototype specifically for agricultural settings by utilizing Convolutional Neural Networks (CNN) algorithm. The project makes a thorough analysis and optimization of CNN hyperparameters in order to improve accuracy and efficiency. As a result, this study has achieved 89.82% from the 80-20 split using CNN algorithm with an F1 score of 88.08%. The research then goes on to assess how well the CNN model generalizes to various agricultural environments that support multiple crop situations. In addition to the technological innovations in agricultural technology, this CNN-based weed detection system proves to be a reliable resource for agriculturalists. It provides accurate and timely insights, empowering efficient weed control practices and contributing to the overall enhancement of agricultural processes. 2024 Thesis https://ir.uitm.edu.my/id/eprint/95565/ https://ir.uitm.edu.my/id/eprint/95565/1/95565.pdf text en public degree Universiti Teknologi MARA, Terengganu College of Computing, Informatics and Mathematics Mohamed@Omar, Hasiah
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
advisor	Mohamed@Omar, Hasiah
topic	Neural networks (Computer science)
spellingShingle	Neural networks (Computer science) Mohammad Nasir, Khairun Nisa Weeds detection for agriculture using Convolutional Neural Network (CNN) algorithm / Khairun Nisa Mohammad Nasir
description	Weed detection and control are a focal point of agricultural research due to their harmful impact on crop productivity, competing for essential resources like sunlight, water, and nutrients. Modern agriculture recognizes weed detection systems as crucial tools to reduce the obstacles caused by weeds, enhancing crop growth and yield. This project aims to develop a weed detection prototype specifically for agricultural settings by utilizing Convolutional Neural Networks (CNN) algorithm. The project makes a thorough analysis and optimization of CNN hyperparameters in order to improve accuracy and efficiency. As a result, this study has achieved 89.82% from the 80-20 split using CNN algorithm with an F1 score of 88.08%. The research then goes on to assess how well the CNN model generalizes to various agricultural environments that support multiple crop situations. In addition to the technological innovations in agricultural technology, this CNN-based weed detection system proves to be a reliable resource for agriculturalists. It provides accurate and timely insights, empowering efficient weed control practices and contributing to the overall enhancement of agricultural processes.
format	Thesis
qualification_level	Bachelor degree
author	Mohammad Nasir, Khairun Nisa
author_facet	Mohammad Nasir, Khairun Nisa
author_sort	Mohammad Nasir, Khairun Nisa
title	Weeds detection for agriculture using Convolutional Neural Network (CNN) algorithm / Khairun Nisa Mohammad Nasir
title_short	Weeds detection for agriculture using Convolutional Neural Network (CNN) algorithm / Khairun Nisa Mohammad Nasir
title_full	Weeds detection for agriculture using Convolutional Neural Network (CNN) algorithm / Khairun Nisa Mohammad Nasir
title_fullStr	Weeds detection for agriculture using Convolutional Neural Network (CNN) algorithm / Khairun Nisa Mohammad Nasir
title_full_unstemmed	Weeds detection for agriculture using Convolutional Neural Network (CNN) algorithm / Khairun Nisa Mohammad Nasir
title_sort	weeds detection for agriculture using convolutional neural network (cnn) algorithm / khairun nisa mohammad nasir
granting_institution	Universiti Teknologi MARA, Terengganu
granting_department	College of Computing, Informatics and Mathematics
publishDate	2024
url	https://ir.uitm.edu.my/id/eprint/95565/1/95565.pdf
_version_	1804889964926730240

Weeds detection for agriculture using Convolutional Neural Network (CNN) algorithm / Khairun Nisa Mohammad Nasir

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