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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Autores principales:	R. Wan Nurazwin Syazwani, H. Muhammad Asraf, M.A. Megat Syahirul Amin, K.A. Nur Dalila
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2022
Materias:	Pineapple crown Crop recognition Image processing Precision agriculture Yield counting Engineering (General). Civil engineering (General) TA1-2040
Acceso en línea:	https://doaj.org/article/8b57a4891af84a47a0caa6d59617bc30
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spelling	oai:doaj.org-article:8b57a4891af84a47a0caa6d59617bc302021-11-18T04:45:29ZAutomated image identification, detection and fruit counting of top-view pineapple crown using machine learning1110-016810.1016/j.aej.2021.06.053https://doaj.org/article/8b57a4891af84a47a0caa6d59617bc302022-02-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S111001682100418Xhttps://doaj.org/toc/1110-0168Automated 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.R. Wan Nurazwin SyazwaniH. Muhammad AsrafM.A. Megat Syahirul AminK.A. Nur DalilaElsevierarticlePineapple crownCrop recognitionImage processingPrecision agricultureYield countingEngineering (General). Civil engineering (General)TA1-2040ENAlexandria Engineering Journal, Vol 61, Iss 2, Pp 1265-1276 (2022)
institution	DOAJ
collection	DOAJ
language	EN
topic	Pineapple crown Crop recognition Image processing Precision agriculture Yield counting Engineering (General). Civil engineering (General) TA1-2040
spellingShingle	Pineapple crown Crop recognition Image processing Precision agriculture Yield counting Engineering (General). Civil engineering (General) TA1-2040 R. Wan Nurazwin Syazwani H. Muhammad Asraf M.A. Megat Syahirul Amin K.A. Nur Dalila Automated image identification, detection and fruit counting of top-view pineapple crown using machine learning
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.
format	article
author	R. Wan Nurazwin Syazwani H. Muhammad Asraf M.A. Megat Syahirul Amin K.A. Nur Dalila
author_facet	R. Wan Nurazwin Syazwani H. Muhammad Asraf M.A. Megat Syahirul Amin K.A. Nur Dalila
author_sort	R. Wan Nurazwin Syazwani
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
publisher	Elsevier
publishDate	2022
url	https://doaj.org/article/8b57a4891af84a47a0caa6d59617bc30
work_keys_str_mv	AT rwannurazwinsyazwani automatedimageidentificationdetectionandfruitcountingoftopviewpineapplecrownusingmachinelearning AT hmuhammadasraf automatedimageidentificationdetectionandfruitcountingoftopviewpineapplecrownusingmachinelearning AT mamegatsyahirulamin automatedimageidentificationdetectionandfruitcountingoftopviewpineapplecrownusingmachinelearning AT kanurdalila automatedimageidentificationdetectionandfruitcountingoftopviewpineapplecrownusingmachinelearning
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Automated image identification, detection and fruit counting of top-view pineapple crown using machine learning

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