Classification of nutrient deficiency in lettuce using Convolutional Neural Network (CNN) / Mahirah Mazlan

This project presents a study titled "Classification of Nutrient Deficiency in Lettuce using CNN." The research addresses challenges in diagnosing and categorizing nutrient deficiencies in lettuce, proposing a CNN-based solution to distinguish between nitrogen deficiency, phosphorus defici...

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Main Author:	Mazlan, Mahirah
Format:	Thesis
Language:	English
Published:	2024
Subjects:	Neural networks (Computer science)
Online Access:	https://ir.uitm.edu.my/id/eprint/95672/1/95672.pdf
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id	my-uitm-ir.95672
record_format	uketd_dc
spelling	my-uitm-ir.956722024-05-31T01:45:08Z Classification of nutrient deficiency in lettuce using Convolutional Neural Network (CNN) / Mahirah Mazlan 2024 Mazlan, Mahirah Neural networks (Computer science) This project presents a study titled "Classification of Nutrient Deficiency in Lettuce using CNN." The research addresses challenges in diagnosing and categorizing nutrient deficiencies in lettuce, proposing a CNN-based solution to distinguish between nitrogen deficiency, phosphorus deficiency, potassium deficiency, and fully nutritional. The objectives involve investigating the requirements of CNN, developing a prototype system, and evaluating its accuracy. The system achieved a 92.68% accuracy in distinguishing between nitrogen deficiency, phosphorus deficiency, potassium deficiency, and fully nutritional. Chapter Two's literature review covers plant detection techniques and the advantages of CNN. Chapter Three outlines the methodology for CNN implementation, and Chapter Four presents the system's results and findings. Limitations include the absence of real-time detection and the inability to identify unknown images. Future recommendations aim to improve real-time detection, expand the range of nutrient deficient detection, and enhance accuracy through advanced algorithms. 2024 Thesis https://ir.uitm.edu.my/id/eprint/95672/ https://ir.uitm.edu.my/id/eprint/95672/1/95672.pdf text en public degree Universiti Teknologi MARA, Terengganu College of Computing, Informatics and Media Tan, Gloria Jennis
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
advisor	Tan, Gloria Jennis
topic	Neural networks (Computer science)
spellingShingle	Neural networks (Computer science) Mazlan, Mahirah Classification of nutrient deficiency in lettuce using Convolutional Neural Network (CNN) / Mahirah Mazlan
description	This project presents a study titled "Classification of Nutrient Deficiency in Lettuce using CNN." The research addresses challenges in diagnosing and categorizing nutrient deficiencies in lettuce, proposing a CNN-based solution to distinguish between nitrogen deficiency, phosphorus deficiency, potassium deficiency, and fully nutritional. The objectives involve investigating the requirements of CNN, developing a prototype system, and evaluating its accuracy. The system achieved a 92.68% accuracy in distinguishing between nitrogen deficiency, phosphorus deficiency, potassium deficiency, and fully nutritional. Chapter Two's literature review covers plant detection techniques and the advantages of CNN. Chapter Three outlines the methodology for CNN implementation, and Chapter Four presents the system's results and findings. Limitations include the absence of real-time detection and the inability to identify unknown images. Future recommendations aim to improve real-time detection, expand the range of nutrient deficient detection, and enhance accuracy through advanced algorithms.
format	Thesis
qualification_level	Bachelor degree
author	Mazlan, Mahirah
author_facet	Mazlan, Mahirah
author_sort	Mazlan, Mahirah
title	Classification of nutrient deficiency in lettuce using Convolutional Neural Network (CNN) / Mahirah Mazlan
title_short	Classification of nutrient deficiency in lettuce using Convolutional Neural Network (CNN) / Mahirah Mazlan
title_full	Classification of nutrient deficiency in lettuce using Convolutional Neural Network (CNN) / Mahirah Mazlan
title_fullStr	Classification of nutrient deficiency in lettuce using Convolutional Neural Network (CNN) / Mahirah Mazlan
title_full_unstemmed	Classification of nutrient deficiency in lettuce using Convolutional Neural Network (CNN) / Mahirah Mazlan
title_sort	classification of nutrient deficiency in lettuce using convolutional neural network (cnn) / mahirah mazlan
granting_institution	Universiti Teknologi MARA, Terengganu
granting_department	College of Computing, Informatics and Media
publishDate	2024
url	https://ir.uitm.edu.my/id/eprint/95672/1/95672.pdf
_version_	1804889968205627392

Classification of nutrient deficiency in lettuce using Convolutional Neural Network (CNN) / Mahirah Mazlan

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