Determining Philippine coconut maturity level using machine learning algorithms based on acoustic signal

Caladcad, June Anne; Cabahug, Shiela; Catamco, Mary Rose; Villaceran, Paul Elyson; Cosgafa, Leizel; Cabizares, Karl Norbert; Hermosilla, Marfe; Piedad, Eduardo Jr

doi:10.1016/j.compag.2020.105327

Cited by 49 publications

(30 citation statements)

References 25 publications

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“…Previous works have reported on the feasibility of ANN in evaluating the food quality of food and agricultural products such as potatoes (Rady et al, 2015), coconuts (Caladcad et al, 2020), durians (Kumar et al, 2017), and olive oil (Sanaeifar & Jafari, 2019). Kumar et al (2017) investigated the leaf estimation of durian using the ANN model.…”

Section: Annmentioning

confidence: 99%

“…In the proposed method, the VGG-16 architecture was selected for the establishment of the CNN model which successfully classified the fresh tea with the testing dataset obtained accuracy of 0.68. Caladcad et al (2020) studied the classification of coconuts according to the maturity levels using machine learning methods. Three machine learning tools were selected; ANN, SVM, and random forest for the maturity estimation of coconuts with an overall classification accuracy of up to 80%.…”

Section: Applications Of Ai In Quality Determination Of Food and Agricultural Productsmentioning

confidence: 99%

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Quality Inspection of Food and Agricultural Products using Artificial Intelligence

Ali

Hashim²,

Aziz³

et al. 2021

Adv Agric Food Res J

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A rising awareness for quality inspection of food and agricultural products has generated a growing effort to develop rapid and non-destructive techniques. Quality detection of food and agricultural products has prime importance in various stages of processing due to the laborious processes and the inability of the system to measure the whole of the food production. The detection of food quality has previously depended on various destructive techniques that require sample destruction and a large amount of postharvest losses. Artificial Intelligence (AI) has emerged with big data technologies and high-performance computation to create new opportunities in the multidisciplinary agri-food domain. This review presents the key concepts of AI comprising an expert system, artificial neural network (ANN), and fuzzy logic. A special focus is laid on the strength of AI applications in determining food quality for producing high and optimum yields. It was demonstrated that ANN provides the best result for modelling and effective in real-time monitoring techniques. The future use of AI for assessing quality inspection is promising which could lead to a real-time as well as rapid evaluation of various food and agricultural products.

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Section: Annmentioning

confidence: 99%

Section: Applications Of Ai In Quality Determination Of Food and Agricultural Productsmentioning

confidence: 99%

Quality Inspection of Food and Agricultural Products using Artificial Intelligence

Ali

Hashim²,

Aziz³

et al. 2021

Adv Agric Food Res J

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“…Caladcad et al [8] studied coconut separation using sound for sorting and they developed a tapping system relying on both software and hardware to record coconut sound. The three most widely used machine learning tools were artificial neural network (ANN), support vector machine (SVM), and random forest (RF).…”

Section: Proliferation Of Deep Learning In Acoustic Sensingmentioning

confidence: 99%

Acoustic Sensing for Quality Edible Evaluation of Sriracha Pineapple Using Convolutional Neural Network

Pongpinigpinyo

Phawiakkharakun

Taeprasartsit

2021

Curr. Appl. Sci. Technol.

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Sriracha pineapple is a pineapple of the Smooth Cayenne variety and is one of Thailand's favorite tropical fruits. It is not only one of the most favorite agricultural products for the pineapple processing and canning industries but it is also widely consumed as a fresh fruit. Many exporters and consumers prefer to measure juiciness before processing food products and consuming, respectively. The traditional method used by pineapple sellers or farmers for fruit juiciness classification is to tap the pineapple using force impulse techniques with a rubber-tipped stick or a person's middle finger tapping. However, these traditional methods of classification require a lot of expertise and experience. Thus, the inspector's perception of accomplishing the classification process may be inclined to errors and uneven results. This paper proposes a combination of acoustic sensing and convolutional neural network (CNN). The tapping sound of 30 Sriracha pineapple samples using force impulse techniques was recorded on smartphone. The tapping sounds were processed into a juiciness classification system of three classes: (1) Juiciness 1, (2) Juiciness 2, and (3) Juiciness 3. The system involved a combination of acoustic sensing and CNN to compare the results between Mel Frequency Cepstral Coefficient (MFCC) and Mel-Spectrogram features extraction, with replication of the same CNN model, to evaluate the pineapple's edibility from its juiciness level. Experimental results showed that MFCC combined with CNN performed the best, with an accuracy of 96.67% and F1-score of 0.97. It outperformed the Mel-spectrogram combined with CNN.

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“…In 2020, Caladcad, J. A., S. Cabahug, et al introduced a system to classify the maturity of Philippine coconut using ML techniques [16]. They classified the Philippine coconut into three different maturity levels (pre-mature, mature, and over-mature) using random forest and support vector machine (SVM) classification systems.…”

Section: Literature Reviewmentioning

confidence: 99%

IHDS: Intelligent Harvesting Decision System for Date Fruit Based on Maturity Stage Using Deep Learning and Computer Vision

et al. 2020

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Determining Philippine coconut maturity level using machine learning algorithms based on acoustic signal

Cited by 49 publications

References 25 publications

Quality Inspection of Food and Agricultural Products using Artificial Intelligence

Quality Inspection of Food and Agricultural Products using Artificial Intelligence

Acoustic Sensing for Quality Edible Evaluation of Sriracha Pineapple Using Convolutional Neural Network

IHDS: Intelligent Harvesting Decision System for Date Fruit Based on Maturity Stage Using Deep Learning and Computer Vision

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