Nutrient deficiency detection in maize (Zea mays L.) leaves using image processing / Nurul Shafekah Kassim

Maize is one of the world's leading food supplies. When maize becomes more important, the crop's production must continue to reproduce. Maize is an active feeder, so as the plant grows, the soils need to be adequately supplied with nutrients. Plants must be in deep green color to indicate...

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Main Author:	Kassim, Nurul Shafekah
Format:	Thesis
Language:	English
Published:	2020
Subjects:	Data processing Computer applications S Agriculture (General)
Online Access:	https://ir.uitm.edu.my/id/eprint/31511/1/31511.pdf
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id	my-uitm-ir.31511
record_format	uketd_dc
spelling	my-uitm-ir.315112020-06-26T04:17:25Z Nutrient deficiency detection in maize (Zea mays L.) leaves using image processing / Nurul Shafekah Kassim 2020 Kassim, Nurul Shafekah Data processing. Computer applications Electronic Computers. Computer Science S Agriculture (General) Maize is one of the world's leading food supplies. When maize becomes more important, the crop's production must continue to reproduce. Maize is an active feeder, so as the plant grows, the soils need to be adequately supplied with nutrients. Plants must be in deep green color to indicate the adequate nutrient. This project is developed to solve the main problem of plant tissue laboratory testing to detect nutrient deficiencies that consume a lot of time. The purpose of this study was to help agriculturist, farmers and researchers to identify the type of maize nutrient deficiency. This Maize Leaves Nutrient Deficiency Detection uses image processing techniques to determine the type of nutrient deficiency that occurs on the plant leaf. In order to increase the accuracy model, random forest technique was used as a classifier and some combination of the texture of feature extraction. This application was checked for accuracy after analysing the percentage of the overall application. The result shows that random forest can produce accurate results with 78.35 percent of accuracy. 2020 Thesis https://ir.uitm.edu.my/id/eprint/31511/ https://ir.uitm.edu.my/id/eprint/31511/1/31511.pdf text en public degree Universiti Teknologi MARA, Cawangan Melaka Faculty of Computer and Mathematical Sciences Sabri, Nurbaity
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
advisor	Sabri, Nurbaity
topic	Data processing Computer applications Data processing Computer applications S Agriculture (General)
spellingShingle	Data processing Computer applications Data processing Computer applications S Agriculture (General) Kassim, Nurul Shafekah Nutrient deficiency detection in maize (Zea mays L.) leaves using image processing / Nurul Shafekah Kassim
description	Maize is one of the world's leading food supplies. When maize becomes more important, the crop's production must continue to reproduce. Maize is an active feeder, so as the plant grows, the soils need to be adequately supplied with nutrients. Plants must be in deep green color to indicate the adequate nutrient. This project is developed to solve the main problem of plant tissue laboratory testing to detect nutrient deficiencies that consume a lot of time. The purpose of this study was to help agriculturist, farmers and researchers to identify the type of maize nutrient deficiency. This Maize Leaves Nutrient Deficiency Detection uses image processing techniques to determine the type of nutrient deficiency that occurs on the plant leaf. In order to increase the accuracy model, random forest technique was used as a classifier and some combination of the texture of feature extraction. This application was checked for accuracy after analysing the percentage of the overall application. The result shows that random forest can produce accurate results with 78.35 percent of accuracy.
format	Thesis
qualification_level	Bachelor degree
author	Kassim, Nurul Shafekah
author_facet	Kassim, Nurul Shafekah
author_sort	Kassim, Nurul Shafekah
title	Nutrient deficiency detection in maize (Zea mays L.) leaves using image processing / Nurul Shafekah Kassim
title_short	Nutrient deficiency detection in maize (Zea mays L.) leaves using image processing / Nurul Shafekah Kassim
title_full	Nutrient deficiency detection in maize (Zea mays L.) leaves using image processing / Nurul Shafekah Kassim
title_fullStr	Nutrient deficiency detection in maize (Zea mays L.) leaves using image processing / Nurul Shafekah Kassim
title_full_unstemmed	Nutrient deficiency detection in maize (Zea mays L.) leaves using image processing / Nurul Shafekah Kassim
title_sort	nutrient deficiency detection in maize (zea mays l.) leaves using image processing / nurul shafekah kassim
granting_institution	Universiti Teknologi MARA, Cawangan Melaka
granting_department	Faculty of Computer and Mathematical Sciences
publishDate	2020
url	https://ir.uitm.edu.my/id/eprint/31511/1/31511.pdf
_version_	1783734123905417216

Nutrient deficiency detection in maize (Zea mays L.) leaves using image processing / Nurul Shafekah Kassim

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