Distinguishing Pickled and Fresh Cucumber Slices Using Digital Image Processing and Machine Learning

Ropelewska, Ewa; Sabancı, Kadir; Aslan, Muhammet Fatih

doi:10.3390/iecho2022-12477

Cited by 2 publications

(2 citation statements)

References 19 publications

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“…The study found that the ANN had the most accurate design in a linear function for the output layer and a hidden layer for the logistic sigmoid transfer function. Similarly, (Ropelewska et al, 2023) explored the impact of machine learning algorithms and image processing techniques to non-destructively discriminate vacuum-dried banana samples. The study found that combining all color channels after selecting image textures achieved a high accuracy of 96.89% in classifying the dried banana samples using the group of trees from the RF algorithm.…”

Section: Introductionmentioning

confidence: 99%

Far‐infrared drying influence on machine learning algorithms in improving corn drying process with graphene irradiation heating plates

Jibril,

Zhang,

Wang

et al. 2024

J Food Process Engineering

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Drying methods often suffer from appropriate drying process modeling, low heating efficiency, and high‐energy consumption. However, graphene may provide significant improvements during the drying process due to its ability to save energy and convert electro‐thermal energy effectively. In this study, random forest (RF), surface vector machine (SVM), artificial neural network (ANN), and k‐nearest neighbor (kNN) were the four machine‐learning algorithms employed to analyze the drying process of two types of corn (ZD88 and RP909) at varying temperatures of 35, 45, and 55°C in a graphene far‐infrared drying. The study found that the drying process of corn showed a decrease in moisture ratio during the falling rate period from 35 to 55°C. The moisture diffusivity increased from 35 to 55°C with the rise in the temperature, ranging from 2.74 to 4.36 × 10−8 m2/s for ZD88 and 2.05 to 3.07 × 10−8 m2/s for RP909. The ZD88 showed a heat efficiency of 81.62% at 55°C. The results of the machine learning algorithms revealed that 1–6 ANN was the best architecture. Normalized PUK was the best for SVM filters. A k = 3 and s‐fold = 5 were the best values for k‐NN and RF, respectively. Among the computed algorithms, the tested data for normalized Pearson universal kernel SVM filters proved to be the most effective in computing the process of drying corn. Overall, a graphene far‐infrared dryer showed improved heating efficiency and less energy consumption, providing a new concept for developing a drying process through machine learning algorithms strategies.Practical applicationsDrying methods often suffer from appropriate drying process modeling, low heating efficiency, and high‐energy consumption. This study provides significant improvements due to the ability of graphene irradiation heating plates to save energy and convert electro‐thermal energy effectively. The study achieved a heating efficiency of 81.62% at an infrared temperature of 55°C. Machine learning algorithms are intelligent models that minimize the limitations in describing the drying process of agricultural materials. The study found the normalized Pearson universal kernel was the most effective computation SVM filter. Therefore, using a graphene far‐infrared dryer showed a better drying alternative for industrial applications, as it has the potential to improve heating efficiency and consume less energy, providing a new concept for developing drying equipment through machine learning algorithms modeling strategies.

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Section: Introductionmentioning

confidence: 99%

Far‐infrared drying influence on machine learning algorithms in improving corn drying process with graphene irradiation heating plates

Jibril,

Zhang,

Wang

et al. 2024

J Food Process Engineering

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“…Plant leaf diseases have garnered significant attention due to their considerable effects on agriculture and daily life [10,11]. The conventional methods of disease identification are timeconsuming and prone to errors, leading to the development of innovative techniques based on deep learning.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Classification of Mulberry Leaf Diseases

YASIN,

KURSUN,

KOKLU

2024

icisna

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This research examines the potential of machine learning methods in the classification of Mulberry leaf diseases. By applying SqueezeNet's deep feature extraction, the study aimed to identify disease patterns efficiently. The dataset used in the study consisted of ten distinct classes of Mulberry leaf diseases, which was divided into an 80% training set and a 20% testing set. The Support Vector Machine (SVM) supervised machine learning algorithm was used to classify the diseases, and the classification model achieved an accuracy of 77.5%. The results of the study demonstrate the effectiveness of machine learning approaches in aiding the detection and management of Mulberry leaf diseases, which can contribute to advancements in agricultural disease monitoring and mitigation strategies.

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Distinguishing Pickled and Fresh Cucumber Slices Using Digital Image Processing and Machine Learning

Abstract: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

Cited by 2 publications

References 19 publications

Far‐infrared drying influence on machine learning algorithms in improving corn drying process with graphene irradiation heating plates

Far‐infrared drying influence on machine learning algorithms in improving corn drying process with graphene irradiation heating plates

Machine Learning-Based Classification of Mulberry Leaf Diseases

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