The classification flesh aromatic coconuts in daylight

Tantrakansakul, Piyaphat; Khaorapapong, Thanate

doi:10.1109/ecticon.2014.6839757

Cited by 5 publications

(3 citation statements)

References 6 publications

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“…To provide an accurate identification of maturity of aromatic coconut fruit without opening them, image processing technology has been developed [54,55]. This identification process is based on mathematical correlations between the fruit's morphology and the percentage of white pixels formed on the image.…”

Section: Utilisationmentioning

confidence: 99%

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Biology, propagation and utilization of elite coconut varieties (makapuno and aromatics)

Nguyen

Bandupriya

Foale

et al. 2016

Plant Physiology and Biochemistry

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Coconut farming is not only a vital agricultural industry for all tropical countries possessing humid coasts and lowlands, but is also a robust income provider for millions of smallholder farmers worldwide. However, due to its longevity, the security of production of this crop suffers significantly from episodes of natural disasters, including cyclone and tsunami, devastating pest and disease outbreaks, while also affected by price competition for the principal products, especially the oil. In order to reduce these pressures, high-value coconut varieties (makapuno and aromatics) have been introduced in some regions, on a limited scale, but with positive outcomes. Even though these two varieties produce fruit with delicious solid or flavoursome liquid endosperm, their distinct biochemical and cellular features unfortunately prevent their in situ germination. In fact, embryo rescue and culture have been developed historically to nurture the embryo under in vitro conditions, enabling effective propagation. In an attempt to provide a comprehensive review featuring these elite coconut varieties, this paper firstly introduces their food values and nutritional qualities, and then discusses the present knowledge of their biology and genetics. Further possibilities for coconut in general are also highlighted, through the use of advanced tissue culture techniques and efficient seedling management for sustainable production of these highly distinct and commercially attractive varieties of coconut.

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Section: Utilisationmentioning

confidence: 99%

“…The precision of the technique is significantly improved under natural lighting conditions. It was shown that the classifying power of this system could achieve an accuracy of at least 77% in identifying aromatic fruit [55].…”

Section: Utilisationmentioning

confidence: 99%

Biology, propagation and utilization of elite coconut varieties (makapuno and aromatics)

Nguyen

Bandupriya

Foale

et al. 2016

Plant Physiology and Biochemistry

View full text Add to dashboard Cite

show abstract

“…The Raspberry Pi products as a cheap microprocessor have now been used for applications that utilize neural networks (Monteiro, 2018). Its capabilities for image processing have been proven of good use when it comes to feature extraction and classification in coconut based on this technique has been made (Arboleda et al, 2020;Tantrakansakul & Khaorapapong, 2014). Studies centered in coconut researches find its way to the realm of machine learning techniques to determine its quality (Alonzo et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Coconut Maturity Detector Using Spectrogram and Convolutional Neural Network

Abadies,

Romulo,

Arboleda

et al. 2024

Preprint

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Coconut is an important multipurpose crop wherein different stages of its maturity are used for different products. Traditional approach of its classification use sound formed by tapping the fruit, and the tapper will base its judgment through the sound he heard. The procedure was highly subjective and attempts had been made by other researchers to automate and classify the adulthood of the coconut objectively. Nowadays, deep learning techniques are being utilized to solve classification problems. One such method is Convolutional Neural Network (CNN) that takes raw pixel data, learns to extract features, and ultimately classifies the input. In this study, a portable device was developed that takes audio signals generated from tapping the fruit mechanically. This audio clip was converted to spectrogram and was used as input to a LeNet CNN architecture. We argue that CNN classifier has improved accuracy compared to the non-deep learning system. Our evaluation confirmed that the use of CNN had improved the accuracy of classifying coconut by about 15%.

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Adapting a Stereo Camera System to Determine Coconut Ages

Lertchuwongsa

Parinyavuttichai

2019

2019 16th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technolo

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The classification flesh aromatic coconuts in daylight

Cited by 5 publications

References 6 publications

Biology, propagation and utilization of elite coconut varieties (makapuno and aromatics)

Biology, propagation and utilization of elite coconut varieties (makapuno and aromatics)

Coconut Maturity Detector Using Spectrogram and Convolutional Neural Network

Adapting a Stereo Camera System to Determine Coconut Ages

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