Study on leave image processing with application in herbal classification and early detection of chili plant disease
Herbs have been widely used in food preparation, medicine and cosmetic industry. Knowing which herbs to be used would be very important in these applications. The current way of identification and determination of the types of herbs however, is still being done manually and prone to human error....
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my-unimap-771792022-11-24T07:52:10Z Study on leave image processing with application in herbal classification and early detection of chili plant disease Ali Yeon, Md. Shakaff, Prof. Dr. Herbs have been widely used in food preparation, medicine and cosmetic industry. Knowing which herbs to be used would be very important in these applications. The current way of identification and determination of the types of herbs however, is still being done manually and prone to human error. Designing a convenient and automatic recognition system of herbs species is essential since this will improve herb species classification efficiency. Chili (Capsicum Annum and Capsicum Frutescen) is an important fruiting vegetable used in majority of Asian dishes. Chili cultivation has been a very difficult and meticulous task due to its vulnerability to various attacks frommicro-organisms, bacterial disease and pests which leave distinguished marks on leaves, stems or fruits. Current manual method applies pesticides and chemicals indiscriminately throughout the farm. To improve the process, development of an automated disease detection is essential. There are a few research that have been done in classification of the plant species using certain factors (leaf shape and size). The classification are accomplished through several image processing techniques. However, the literature shows that there are still a gap in classifying the herb plants species. Therefore, this research focuses on classification approach to the shape, texture features and colors of the herbs leaves. The combination of techniques used in morphology image processing i.e. SVD and skeleton would be able to classify the species of herb regardless of the shape and size. In addition, the techniques demonstrate the capability to detect early plant chili disease through leaf features inspection using HSV colour model technique. The proposed herbs species recognition system employs neural networks algorithm and image processing techniques to perform classification on twenty herbs species. One hundred samples for each species went through the system and the recognition accuracy was at 98.9%. Most importantly the system is capable of identifying the herbs leaves species even though they are dried, wet, torn or deformed. Additionally, a novel method of early automatic recognition for plant chili disease based on color and texture features using a HSV color model and BPNN technique via intelligent decision support system is presented in this research. The proposed system employs image processing technique on one thousand chili plant samples and the recognition accuracy was at 97.7%. The efficiency and effectiveness of the proposed methods in recognizing herbs plant and detecting early plant chili disease are demonstrated by the experiments. Universiti Malaysia Perlis (UniMAP) Thesis en http://dspace.unimap.edu.my:80/xmlui/handle/123456789/77179 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/77179/3/license.txt 8a4605be74aa9ea9d79846c1fba20a33 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/77179/1/Page%201-24.pdf 15e173980cc0a454d9fe214ced1c03fc http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/77179/2/Full%20text.pdf c7c408f51e14ff9b68643680fa86ba72 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/77179/4/Zulkifli%20Husin.pdf e40b0bc5bb6d66d3619a83f8e9c4ac84 Universiti Malaysia Perlis (UniMAP) Pattern recognition systems Capsicum annuum Image processing Image processing -- Digital techniques Herbs School of Computer and Communication Engineering |
| institution |
Universiti Malaysia Perlis |
| collection |
UniMAP Institutional Repository |
| language |
English |
| advisor |
Ali Yeon, Md. Shakaff, Prof. Dr. |
| topic |
Pattern recognition systems Capsicum annuum Image processing Image processing -- Digital techniques Herbs |
| spellingShingle |
Pattern recognition systems Capsicum annuum Image processing Image processing -- Digital techniques Herbs Study on leave image processing with application in herbal classification and early detection of chili plant disease |
| description |
Herbs have been widely used in food preparation, medicine and cosmetic industry. Knowing
which herbs to be used would be very important in these applications. The current way of
identification and determination of the types of herbs however, is still being done manually
and prone to human error. Designing a convenient and automatic recognition system of herbs
species is essential since this will improve herb species classification efficiency. Chili
(Capsicum Annum and Capsicum Frutescen) is an important fruiting vegetable used in
majority of Asian dishes. Chili cultivation has been a very difficult and meticulous task due
to its vulnerability to various attacks frommicro-organisms, bacterial disease and pests which
leave distinguished marks on leaves, stems or fruits. Current manual method applies
pesticides and chemicals indiscriminately throughout the farm. To improve the process,
development of an automated disease detection is essential. There are a few research that
have been done in classification of the plant species using certain factors (leaf shape and
size). The classification are accomplished through several image processing techniques.
However, the literature shows that there are still a gap in classifying the herb plants species.
Therefore, this research focuses on classification approach to the shape, texture features and
colors of the herbs leaves. The combination of techniques used in morphology image
processing i.e. SVD and skeleton would be able to classify the species of herb regardless of
the shape and size. In addition, the techniques demonstrate the capability to detect early plant
chili disease through leaf features inspection using HSV colour model technique. The
proposed herbs species recognition system employs neural networks algorithm and image
processing techniques to perform classification on twenty herbs species. One hundred
samples for each species went through the system and the recognition accuracy was at 98.9%.
Most importantly the system is capable of identifying the herbs leaves species even though
they are dried, wet, torn or deformed. Additionally, a novel method of early automatic
recognition for plant chili disease based on color and texture features using a HSV color
model and BPNN technique via intelligent decision support system is presented in this
research. The proposed system employs image processing technique on one thousand chili
plant samples and the recognition accuracy was at 97.7%. The efficiency and effectiveness
of the proposed methods in recognizing herbs plant and detecting early plant chili disease are
demonstrated by the experiments. |
| format |
Thesis |
| title |
Study on leave image processing with application in herbal classification and early detection of chili plant disease |
| title_short |
Study on leave image processing with application in herbal classification and early detection of chili plant disease |
| title_full |
Study on leave image processing with application in herbal classification and early detection of chili plant disease |
| title_fullStr |
Study on leave image processing with application in herbal classification and early detection of chili plant disease |
| title_full_unstemmed |
Study on leave image processing with application in herbal classification and early detection of chili plant disease |
| title_sort |
study on leave image processing with application in herbal classification and early detection of chili plant disease |
| granting_institution |
Universiti Malaysia Perlis (UniMAP) |
| granting_department |
School of Computer and Communication Engineering |
| url |
http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/77179/1/Page%201-24.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/77179/2/Full%20text.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/77179/4/Zulkifli%20Husin.pdf |
| _version_ |
1776104246401302528 |