Acoustic dataset of coconut (Cocos nucifera) based on tapping system

Caladcad, June Anne; Piedad, Eduardo Jr

doi:10.1016/j.dib.2023.108936

Cited by 3 publications

(1 citation statement)

References 9 publications

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“…We used an open-access dataset [10] consisting of a total of 122 raw acoustic signals, which were generated by knocking 122 coconut samples on their ridges. The type and number of data samples used are as follows (see Table 1):…”

Section: Datamentioning

confidence: 99%

Predicting Maturity of Coconut Fruit from Acoustic Signal with Applications of Deep Learning

Sattar

2024

The 2nd International Online Conference on Agriculture

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Section: Datamentioning

confidence: 99%

Predicting Maturity of Coconut Fruit from Acoustic Signal with Applications of Deep Learning

Sattar

2024

The 2nd International Online Conference on Agriculture

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Estimation of coconut maturity based on fuzzy neural network and sperm whale optimization

El-Shafeiy

Abohany

Elmessery

et al. 2023

Neural Comput & Applic

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Coconut water is the clear liquid found inside coconuts, famous for rehydrating after exercise or while suffering from a minor sickness. The essential issue tackled in this paper is how to estimate the appropriate stage of maturity of coconut water, which is a time-consuming task in the beverage industry since, as the coconut age increases, the coconut water flavor varies. Accordingly, to handle this issue, an adaptive model based on Fuzzy Neural Network and Sperm Whale Optimization, dubbed FNN–SWO, is developed to assess coconut water maturity. The Sperm Whale Optimization (SWO) algorithm is a meta-heuristic optimization algorithm. It is embedded in this model along with neural networks and fuzzy techniques (FNN system), which can be employed as an essential building block in the beverage industry. The proposed FNN–SWO model is trained and tested utilizing fuzzy rules with an adaptive network. In contrast, the SWO algorithm is adopted to determine the optimal weights for the fuzzy rules. Three subsets of data divided according to three levels of coconut water maturity-tender, mature, and very mature, are used to validate the combined FNN–SWO model. Depending on these three subsets of data, a comparison of the proposed FNN–SWO model has been conducted against a set of the most common conventional techniques. These techniques include Support Vector Machine, Naïve Bayes, FNN, Artificial Neural Network, as well as their embedding with other meta-heuristic optimization algorithms. For various key performance indicators, such as recall, F1-score, specificity, and accuracy, the proposed FNN–SWO model provides the best prediction outcomes compared to the current time-consuming techniques. The dominance of the proposed FNN–SWO model is evident from the final findings compared to its time-consuming peers for estimating coconut water maturity on time. As a result, the proposed FNN–SWO model is an effective heuristic for locating optimal solutions to classification problems. It can thereby be reassuringly applicable to other similar prediction problems. Additionally, it would benefit the scientific community interested in evaluating coconut water.

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Deep learning classification system for coconut maturity levels based on acoustic signals

Caladcad,

Piedad

2024

2024 IEEE 12th Region 10 Humanitarian Technology Conference (R10-Htc)

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Acoustic dataset of coconut (Cocos nucifera) based on tapping system

Cited by 3 publications

References 9 publications

Predicting Maturity of Coconut Fruit from Acoustic Signal with Applications of Deep Learning

Predicting Maturity of Coconut Fruit from Acoustic Signal with Applications of Deep Learning

Estimation of coconut maturity based on fuzzy neural network and sperm whale optimization

Deep learning classification system for coconut maturity levels based on acoustic signals

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