Classification of Apples Based on the Shelf Life Using ANN and Data Fusion

Fathizadeh, Zahed; Aboonajmi, Mohammad; Hassan-Beygi, Seyed Reza

doi:10.1007/s12161-020-01913-1

Cited by 10 publications

(5 citation statements)

References 31 publications

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Section: Prediction Of Shelf-lifementioning

confidence: 99%

“…In a study described by Fathizadeh et al (2021), the classification of shelf-life of apples was predicted. They performed the study with vibration sensors using fast Fourier transformation, a following pattern recognition technique, and an artificial NN [108]. Furthermore, efforts have also been made to investigate the proteome during storage to identify peptide markers of different foods, e.g., milk products and oysters.…”

Section: Prediction Of Shelf-lifementioning

confidence: 99%

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Diving Deep into the Data: A Review of Deep Learning Approaches and Potential Applications in Foodomics

Class

Kuhnen

Rohn

et al. 2021

Foods

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Deep learning is a trending field in bioinformatics; so far, mostly known for image processing and speech recognition, but it also shows promising possibilities for data processing in food analysis, especially, foodomics. Thus, more and more deep learning approaches are used. This review presents an introduction into deep learning in the context of metabolomics and proteomics, focusing on the prediction of shelf-life, food authenticity, and food quality. Apart from the direct food-related applications, this review summarizes deep learning for peptide sequencing and its context to food analysis. The review’s focus further lays on MS (mass spectrometry)-based approaches. As a result of the constant development and improvement of analytical devices, as well as more complex holistic research questions, especially with the diverse and complex matrix food, there is a need for more effective methods for data processing. Deep learning might offer meeting this need and gives prospect to deal with the vast amount and complexity of data.

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Section: Prediction Of Shelf-lifementioning

confidence: 99%

Section: Prediction Of Shelf-lifementioning

confidence: 99%

Diving Deep into the Data: A Review of Deep Learning Approaches and Potential Applications in Foodomics

Class

Kuhnen

Rohn

et al. 2021

Foods

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“…Applying a training set of features allows for objectively classifying cases belonging to a test set [Krug and Hutschenreuther 2023]. Apple fruit features obtained non-destructively can be helpful for processing, extraction, pattern recognition, development of classification models using machine learning, and decision-making [Fathizadeh et al 2021]. Furthermore, in addition to apple fruit, leaf image analysis can help identify the apple cultivar [Liu et al 2020, Chen et al 2022.…”

Section: Introductionmentioning

confidence: 99%

A comparative study of distinguishing apple cultivars and a clone based on features of selected fruit parts and leaves using image processing and artificial intelligence

Ropelewska,

Lewandowski

2024

Acta Sci. Pol. Hortorum Cultus

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This study aimed to identify the most useful white-fleshed apple samples to distinguish apple cultivars and a clone. Whole apples, apple slices, seeds, and leaves belonging to ‘Free Redstar’, clone 118, ‘Ligolina’, ‘Pink Braeburn’, and ‘Pinokio’ were imaged using a digital camera. The texture parameters were extracted from images in color channels L, a, b, R, G, B, X, Y, Z, U, V, and S. The classification models were built using traditional machine learning algorithms. Models developed using selected image seed textures allowed the classification of apple cultivars and a clone with the highest average accuracy of up to 97.4%. The apple seeds ‘Free Redstar’ were distinguished with the highest accuracy, equal to 100%. Machine learning models built based on the textures of apple skin allowed for the clone and cultivar classification with slightly lower correctness, reaching 94%. Meanwhile, the average accuracies for models involving selected flesh and leave textures reached 86.4% and 88.8%, respectively. All the most efficient models for classifying individual apple fruit parts and leaves were developed using Multilayer Perceptron. However, models combining selected image textures of apple skin, slices (flesh), seeds, and leaves produced the highest average accuracy of up to 99.6% in the case of Bayes Net. Thus, it was found that including features of different parts of apple fruit and apple leaves in one model can allow for the correct distinguishing of apples in terms of cultivar and clone.

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“…In the aspect of analyzing the texture of agricultural products with acoustic technology, Fathizadeh, Aboonajmi, and Beygi (2020) and Fathizadeh, Aboonajmi, and Hassan‐Beygi (2020) constructed the relationship between sound signals and apple hardness, so as to realize the hardness prediction of apple in shelf life. Liu et al (2020) analyzed and processed the percussion and vibration sound signals of sweet potato, and obtained the sound features that were significantly correlated with the total sugar content, thus realizing the nondestructive detection of the total sugar content of sweet potato.…”

Section: Introductionmentioning

confidence: 99%

Crack identification of Yunnan walnuts based on acoustic vibration and feature fusion

Zhang,

Li,

Tian

et al. 2023

J Food Process Engineering

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Cracks in walnuts during processing and storage can adversely affect their quality and cause economic losses. To achieve efficient identification of cracked walnuts, this study proposed a method of walnut crack identification based on acoustic vibration and feature fusion. First, the sound signals of intact and cracked walnuts were collected using an acoustic signal acquisition system, and 44 time‐domain features, 13 frequency‐domain features, and 768 Mel spectrogram features (the number of pixel frequencies corresponding to the gray‐scale values of R, G, and B channels) of the sound signals were extracted. Then, the classification models of support vector machines (SVM), least squares support vector machines (LSSVM), and extreme learning machines (ELM) were established based on single class features data and fusion of different feature groups data respectively. The results indicated that the LSSVM model with the fusion of the three feature sets was optimal, with an accuracy of 85% in the testing set. Next, three feature selection methods were employed to reduce the dimensionality of the best fused feature data. Subsequently, the LSSVM classification model was established based on the feature selection data. Finally, arithmetic optimization algorithm (AOA), particle swarm optimization (PSO), and gray wolf optimization (GWO) were introduced to optimize the parameters c and of the classification model. The results indicated that the best classification model was VISSA‐IRIV‐GWO‐LSSVM, with 95% accuracy in the testing set. This study provides theoretical support for the research and development of online detection equipment for walnut crack in Yunnan Yangbi.Practical applicationsCracks in walnut during harvesting, transportation and peeling may lead to economic losses and food safety problems. Aiming at the difficulty and low accuracy of crack identification in Yunnan walnut, this paper proposed a method for crack detection based on acoustic vibration identification combine with feature fusion. The features of time domain, frequency domain and Mel spectrogram were extracted from the effective sound signal, and the features were fused. The influence of three feature selection methods, three models and three optimization algorithms on walnut sound signal recognition was analyzed and compared. The results indicated that the best classification model was VISSA‐IRIV‐GWO‐LSSVM, and the method proposed in this study provides theoretical support for the research and development of online detection equipment for walnut crack in Yunnan Yangbi.

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Classification of Apples Based on the Shelf Life Using ANN and Data Fusion

Cited by 10 publications

References 31 publications

Diving Deep into the Data: A Review of Deep Learning Approaches and Potential Applications in Foodomics

Diving Deep into the Data: A Review of Deep Learning Approaches and Potential Applications in Foodomics

A comparative study of distinguishing apple cultivars and a clone based on features of selected fruit parts and leaves using image processing and artificial intelligence

Crack identification of Yunnan walnuts based on acoustic vibration and feature fusion

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