Automated image identification, detection and fruit counting of top-view pineapple crown using machine learning

Automated fruit identification or recognition using image processing is a key element in precision agriculture for performing object detection in large crop plots. Automation of fruit recognition for the captured top-view of RGB based images using an unmanned aerial vehicle (UAV) is a challenge. Ima...

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Published in:Alexandria Engineering Journal
Main Author: Wan Nurazwin Syazwani R.; Muhammad Asraf H.; Megat Syahirul Amin M.A.; Nur Dalila K.A.
Format: Article
Language:English
Published: Elsevier B.V. 2022
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85110232232&doi=10.1016%2fj.aej.2021.06.053&partnerID=40&md5=304b5a2d4b971756d318672c3aa60db8
id 2-s2.0-85110232232
spelling 2-s2.0-85110232232
Wan Nurazwin Syazwani R.; Muhammad Asraf H.; Megat Syahirul Amin M.A.; Nur Dalila K.A.
Automated image identification, detection and fruit counting of top-view pineapple crown using machine learning
2022
Alexandria Engineering Journal
61
2
10.1016/j.aej.2021.06.053
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85110232232&doi=10.1016%2fj.aej.2021.06.053&partnerID=40&md5=304b5a2d4b971756d318672c3aa60db8
Automated fruit identification or recognition using image processing is a key element in precision agriculture for performing object detection in large crop plots. Automation of fruit recognition for the captured top-view of RGB based images using an unmanned aerial vehicle (UAV) is a challenge. Image analysis demonstrated the difficulty of processing the captured image under variant illumination in natural environment and with textured objects of non-ideal geometric shapes. However, this is subjected to certain consideration settings and image-processing algorithms. The study presents an automatic method for identifying and recognising the pineapple's crown images in the designated plot using image processing and further counts the detected images using machine learning classifiers namely artificial neural network (ANN), support vector machine (SVM), random forest (RF), naive Bayes (NB), decision trees (DT) and k-nearest neighbours (KNN). The high spatial-resolution aerial images were pre-processed and segmented, and its extracted features were analysed according to shape, colour and texture for recognising the pineapple crown before classifying it as fruit or non-fruit. Feature fusion using one-way analysis of variance (ANOVA) was incorporated in this study to optimise the performance of machine learning classifier. The algorithm was quantitatively analysed and validated for performance via accuracy, specificity, sensitivity and precision. The detection for the pineapple's crown images with ANN-GDX classification has demonstrated best performance fruit counting with accuracy of 94.4% and has thus demonstrated clear potential application of an effective RGB images analysis for the pineapple industry. © 2021 THE AUTHORS
Elsevier B.V.
11100168
English
Article
All Open Access; Gold Open Access
author Wan Nurazwin Syazwani R.; Muhammad Asraf H.; Megat Syahirul Amin M.A.; Nur Dalila K.A.
spellingShingle Wan Nurazwin Syazwani R.; Muhammad Asraf H.; Megat Syahirul Amin M.A.; Nur Dalila K.A.
Automated image identification, detection and fruit counting of top-view pineapple crown using machine learning
author_facet Wan Nurazwin Syazwani R.; Muhammad Asraf H.; Megat Syahirul Amin M.A.; Nur Dalila K.A.
author_sort Wan Nurazwin Syazwani R.; Muhammad Asraf H.; Megat Syahirul Amin M.A.; Nur Dalila K.A.
title Automated image identification, detection and fruit counting of top-view pineapple crown using machine learning
title_short Automated image identification, detection and fruit counting of top-view pineapple crown using machine learning
title_full Automated image identification, detection and fruit counting of top-view pineapple crown using machine learning
title_fullStr Automated image identification, detection and fruit counting of top-view pineapple crown using machine learning
title_full_unstemmed Automated image identification, detection and fruit counting of top-view pineapple crown using machine learning
title_sort Automated image identification, detection and fruit counting of top-view pineapple crown using machine learning
publishDate 2022
container_title Alexandria Engineering Journal
container_volume 61
container_issue 2
doi_str_mv 10.1016/j.aej.2021.06.053
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85110232232&doi=10.1016%2fj.aej.2021.06.053&partnerID=40&md5=304b5a2d4b971756d318672c3aa60db8
description Automated fruit identification or recognition using image processing is a key element in precision agriculture for performing object detection in large crop plots. Automation of fruit recognition for the captured top-view of RGB based images using an unmanned aerial vehicle (UAV) is a challenge. Image analysis demonstrated the difficulty of processing the captured image under variant illumination in natural environment and with textured objects of non-ideal geometric shapes. However, this is subjected to certain consideration settings and image-processing algorithms. The study presents an automatic method for identifying and recognising the pineapple's crown images in the designated plot using image processing and further counts the detected images using machine learning classifiers namely artificial neural network (ANN), support vector machine (SVM), random forest (RF), naive Bayes (NB), decision trees (DT) and k-nearest neighbours (KNN). The high spatial-resolution aerial images were pre-processed and segmented, and its extracted features were analysed according to shape, colour and texture for recognising the pineapple crown before classifying it as fruit or non-fruit. Feature fusion using one-way analysis of variance (ANOVA) was incorporated in this study to optimise the performance of machine learning classifier. The algorithm was quantitatively analysed and validated for performance via accuracy, specificity, sensitivity and precision. The detection for the pineapple's crown images with ANN-GDX classification has demonstrated best performance fruit counting with accuracy of 94.4% and has thus demonstrated clear potential application of an effective RGB images analysis for the pineapple industry. © 2021 THE AUTHORS
publisher Elsevier B.V.
issn 11100168
language English
format Article
accesstype All Open Access; Gold Open Access
record_format scopus
collection Scopus
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