Asic design for healthiness recognition of agriculture

The purpose of this study is to develop the application of healthiness recognition of agriculture plant module and then develop the module into ASIC design. Plant disease is a common issue which leads to decrease in the food production in the crop agriculture sector. The main factor of plant disease...

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Main Author:	Chin, Linn Kern
Format:	Thesis
Language:	English
Published:	2020
Subjects:	TK Electrical engineering Electronics Nuclear engineering
Online Access:	http://eprints.utm.my/id/eprint/93000/1/ChinLinnKernMSKE2020.pdf
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id	my-utm-ep.93000
record_format	uketd_dc
spelling	my-utm-ep.930002021-11-07T06:00:19Z Asic design for healthiness recognition of agriculture 2020 Chin, Linn Kern TK Electrical engineering. Electronics Nuclear engineering The purpose of this study is to develop the application of healthiness recognition of agriculture plant module and then develop the module into ASIC design. Plant disease is a common issue which leads to decrease in the food production in the crop agriculture sector. The main factor of plant disease is caused by microorganism which include bacteria, fungi and virus. Nitrogen play an important role in the yield of plant. Excess of or lacking in nitrogen may affect the deficiency in crop. There are few methods for nitrogen detection in the leaf such as colour analysis using image processing, remote sensing and neural network etc. In this project, HSV colour model is used for colour analysis through image processing that able to separate the colour portion from the intensity. Before the colour analysis, median filter is used as pre-processing step to remove unwanted salt and pepper noise in the image and preserver the edge of the leaf in the image. After pre-processing step and colour, linear SVM classification is used as classification step. Linear SVM classification, able to classify the plant into healthy and unhealthy categories by determine the concentration of nitrogen in the leaf with 95.36 % accuracy. The algorithm of recognition module is verified through Matlab software simulation. After that, HLS is used to translate the high-level language into hardware description language. The tool can handle complex image processing algorithm and floating point in the algorithm. Unrolled factor for for-loop and pipeline is combined and implemented into the design to obtain the best hardware utilization resource and timing result at netlist level. Lastly, the Verilog netlist of image recognition system is implemented into ASIC. The benefit of ASIC design, that is closer to hardware descript language, and able to minimize the latency and increase the efficiency. ASIC design has been produced successfully with 24.2 mW of power consumption, 0.039 mm2 area and able to operate at 250 MHz clock frequency. 2020 Thesis http://eprints.utm.my/id/eprint/93000/ http://eprints.utm.my/id/eprint/93000/1/ChinLinnKernMSKE2020.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:135893 masters Universiti Teknologi Malaysia, Faculty of Engineering - School of Electrical Engineering Faculty of Engineering - School of Electrical Engineering
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	TK Electrical engineering Electronics Nuclear engineering
spellingShingle	TK Electrical engineering Electronics Nuclear engineering Chin, Linn Kern Asic design for healthiness recognition of agriculture
description	The purpose of this study is to develop the application of healthiness recognition of agriculture plant module and then develop the module into ASIC design. Plant disease is a common issue which leads to decrease in the food production in the crop agriculture sector. The main factor of plant disease is caused by microorganism which include bacteria, fungi and virus. Nitrogen play an important role in the yield of plant. Excess of or lacking in nitrogen may affect the deficiency in crop. There are few methods for nitrogen detection in the leaf such as colour analysis using image processing, remote sensing and neural network etc. In this project, HSV colour model is used for colour analysis through image processing that able to separate the colour portion from the intensity. Before the colour analysis, median filter is used as pre-processing step to remove unwanted salt and pepper noise in the image and preserver the edge of the leaf in the image. After pre-processing step and colour, linear SVM classification is used as classification step. Linear SVM classification, able to classify the plant into healthy and unhealthy categories by determine the concentration of nitrogen in the leaf with 95.36 % accuracy. The algorithm of recognition module is verified through Matlab software simulation. After that, HLS is used to translate the high-level language into hardware description language. The tool can handle complex image processing algorithm and floating point in the algorithm. Unrolled factor for for-loop and pipeline is combined and implemented into the design to obtain the best hardware utilization resource and timing result at netlist level. Lastly, the Verilog netlist of image recognition system is implemented into ASIC. The benefit of ASIC design, that is closer to hardware descript language, and able to minimize the latency and increase the efficiency. ASIC design has been produced successfully with 24.2 mW of power consumption, 0.039 mm2 area and able to operate at 250 MHz clock frequency.
format	Thesis
qualification_level	Master's degree
author	Chin, Linn Kern
author_facet	Chin, Linn Kern
author_sort	Chin, Linn Kern
title	Asic design for healthiness recognition of agriculture
title_short	Asic design for healthiness recognition of agriculture
title_full	Asic design for healthiness recognition of agriculture
title_fullStr	Asic design for healthiness recognition of agriculture
title_full_unstemmed	Asic design for healthiness recognition of agriculture
title_sort	asic design for healthiness recognition of agriculture
granting_institution	Universiti Teknologi Malaysia, Faculty of Engineering - School of Electrical Engineering
granting_department	Faculty of Engineering - School of Electrical Engineering
publishDate	2020
url	http://eprints.utm.my/id/eprint/93000/1/ChinLinnKernMSKE2020.pdf
_version_	1747818623558221824

Asic design for healthiness recognition of agriculture

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