Complex relative permittivity analysis for monitoring watermelon

Yoiyod, P.

doi:10.1109/isanp.2017.8228962

Cited by 3 publications

(2 citation statements)

References 3 publications

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“…Both contact and non-contact acoustic sensor are highly sensitive to ambient environmental conditions, which make them less suitable for particular environments i.e. non-destructive, supermarkets and robotic harvesting [17,18,28].…”

Section: Data Acquisitionmentioning

confidence: 99%

A comprehensive review of fruit and vegetable classification techniques

Hameed

Chai

Rassau

2018

Image and Vision Computing

147

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Recent advancements in computer vision have enabled wide-ranging applications in every field of life. One such application area is fresh produce classification, but the classification of fruit and vegetable has proven to be a complex problem and needs to be further developed. Fruit and vegetable classification presents significant challenges due to interclass similarities and irregular intraclass characteristics. Selection of appropriate data acquisition sensors and feature representation approach is also crucial due to the huge diversity of the field. Fruit and vegetable classification methods have been developed for quality assessment and robotic harvesting but the current state-ofthe-art has been developed for limited classes and small datasets. The problem is of a multi-dimensional nature and offers significantly hyperdimensional features, which is one of the major challenges with current machine learning approaches. Substantial research has been conducted for the design and analysis of classifiers for hyperdimensional features which require significant computational power to optimise with such features. In recent years numerous machine learning techniques for example, Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Decision Trees, Artificial Neural Networks (ANN) and Convolutional Neural Networks (CNN) have been exploited with many different feature description methods for fruit and vegetable classification in many real-life applications. This paper presents a critical comparison of different state-of-the-art computer vision methods proposed by researchers for classifying fruit and vegetable.

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Section: Data Acquisitionmentioning

confidence: 99%

A comprehensive review of fruit and vegetable classification techniques

Hameed

Chai

Rassau

2018

Image and Vision Computing

147

View full text Add to dashboard Cite

show abstract

“…Recent studies analyzing watermelon have mainly focused on determining the complex relative permittivity ( Yoiyod, 2017 ) and sweetness or sugar content ( Chawgien and Kiattisin, 2021 ; Isa et al, 2009 ) using destructive methods. Thus, the need for rapid, noninvasive microwave imaging is evident.…”

Section: Introductionmentioning

confidence: 99%