Image-based Ananas Comosus detection and counting using artificial neural network / Wan Nurazwin Syazwani Rahimi

Ananas Comosus or pineapple is currently one of the most popular fruits with high demand from the global market. This tropical fruit is widely planted, especially in Johor, Malaysia and is increasingly growing in other countries. However, the method of fruit counting and yield estimate continues to...

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Main Author:	Rahimi, Wan Nurazwin Syazwani
Format:	Thesis
Language:	English
Published:	2021
Subjects:	Neural networks (Computer science)
Online Access:	https://ir.uitm.edu.my/id/eprint/60177/1/60177.pdf
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id	my-uitm-ir.60177
record_format	uketd_dc
spelling	my-uitm-ir.601772022-05-24T01:45:15Z Image-based Ananas Comosus detection and counting using artificial neural network / Wan Nurazwin Syazwani Rahimi 2021-08 Rahimi, Wan Nurazwin Syazwani Neural networks (Computer science) Ananas Comosus or pineapple is currently one of the most popular fruits with high demand from the global market. This tropical fruit is widely planted, especially in Johor, Malaysia and is increasingly growing in other countries. However, the method of fruit counting and yield estimate continues to follow the conventional approach by manually counting pineapple fruit, resulting in inaccurate yield after harvesting. Therefore, to automate the process of fruit detection, counting, and yield estimation, a two-step process, namely image processing and artificial neural network (ANN), was proposed. First, the 360 images were extracted from recorded video using an unmanned aerial vehicle (UAV) DJI Phantom 3 Advanced collected at a pineapple plantation in Simpang Renggam, Johor. Then, the top view of the pineapple's crown images was pre-processed, segmented and performed via feature extraction process by shape, colour and texture features before classifying it as fruit or non-fruit using the ANN counting algorithm. The proposed fruit counting algorithm was quantitatively analysed and validated using performance metrics of accuracy, precision, specificity and sensitivity. Results demonstrated that the pineapple's crown images with the best lowest MAPE are 8%, which is at the 3-meter height from the ground to the UAV. Colour thresholding of HSV colour space resulted in the lowest average error of 12%, contrast enhancement with CLAHE technique at 0.008% of MAPE. Gradient Descent Backpropagation (GDX) with feature selection for classification and counting showed accuracy, precision, specificity and sensitivity, respectively, at 94.4%, 92.6%,96.5%, and 96.7%. This has shown that the detection of pineapple crown images is reliable for counting and finally estimating the yield of Ananas Comosus 2021-08 Thesis https://ir.uitm.edu.my/id/eprint/60177/ https://ir.uitm.edu.my/id/eprint/60177/1/60177.pdf text en public masters Universiti Teknologi MARA College of Engineering Hairuddin, Muhammad Asraf (Dr.)
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
advisor	Hairuddin, Muhammad Asraf (Dr.)
topic	Neural networks (Computer science)
spellingShingle	Neural networks (Computer science) Rahimi, Wan Nurazwin Syazwani Image-based Ananas Comosus detection and counting using artificial neural network / Wan Nurazwin Syazwani Rahimi
description	Ananas Comosus or pineapple is currently one of the most popular fruits with high demand from the global market. This tropical fruit is widely planted, especially in Johor, Malaysia and is increasingly growing in other countries. However, the method of fruit counting and yield estimate continues to follow the conventional approach by manually counting pineapple fruit, resulting in inaccurate yield after harvesting. Therefore, to automate the process of fruit detection, counting, and yield estimation, a two-step process, namely image processing and artificial neural network (ANN), was proposed. First, the 360 images were extracted from recorded video using an unmanned aerial vehicle (UAV) DJI Phantom 3 Advanced collected at a pineapple plantation in Simpang Renggam, Johor. Then, the top view of the pineapple's crown images was pre-processed, segmented and performed via feature extraction process by shape, colour and texture features before classifying it as fruit or non-fruit using the ANN counting algorithm. The proposed fruit counting algorithm was quantitatively analysed and validated using performance metrics of accuracy, precision, specificity and sensitivity. Results demonstrated that the pineapple's crown images with the best lowest MAPE are 8%, which is at the 3-meter height from the ground to the UAV. Colour thresholding of HSV colour space resulted in the lowest average error of 12%, contrast enhancement with CLAHE technique at 0.008% of MAPE. Gradient Descent Backpropagation (GDX) with feature selection for classification and counting showed accuracy, precision, specificity and sensitivity, respectively, at 94.4%, 92.6%,96.5%, and 96.7%. This has shown that the detection of pineapple crown images is reliable for counting and finally estimating the yield of Ananas Comosus
format	Thesis
qualification_level	Master's degree
author	Rahimi, Wan Nurazwin Syazwani
author_facet	Rahimi, Wan Nurazwin Syazwani
author_sort	Rahimi, Wan Nurazwin Syazwani
title	Image-based Ananas Comosus detection and counting using artificial neural network / Wan Nurazwin Syazwani Rahimi
title_short	Image-based Ananas Comosus detection and counting using artificial neural network / Wan Nurazwin Syazwani Rahimi
title_full	Image-based Ananas Comosus detection and counting using artificial neural network / Wan Nurazwin Syazwani Rahimi
title_fullStr	Image-based Ananas Comosus detection and counting using artificial neural network / Wan Nurazwin Syazwani Rahimi
title_full_unstemmed	Image-based Ananas Comosus detection and counting using artificial neural network / Wan Nurazwin Syazwani Rahimi
title_sort	image-based ananas comosus detection and counting using artificial neural network / wan nurazwin syazwani rahimi
granting_institution	Universiti Teknologi MARA
granting_department	College of Engineering
publishDate	2021
url	https://ir.uitm.edu.my/id/eprint/60177/1/60177.pdf
_version_	1783735095503355904

Image-based Ananas Comosus detection and counting using artificial neural network / Wan Nurazwin Syazwani Rahimi

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