Multivariate Analysis and Machine Learning for Ripeness Classification of Cape Gooseberry Fruits

De-la-Torre, Miguel; Zatarain, Omar A.; Ávila-George, Himer; Muñoz, Mirna; Oblitas, Jimy; Lozada, Russel; Mejía, Jezreel; Castro, Wilson

doi:10.3390/pr7120928

Cited by 19 publications

(12 citation statements)

References 36 publications

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“…where I gray is the image in grayscale format, and I R I G and I B are the Red, Green, and Blue channels of the image, respectively. Next, the gray scale images were enhanced using the Gaussian filter shown in Equation ( 2) to smooth visual artifacts [15,18]:…”

Section: Improvement and Enhancementmentioning

confidence: 99%

Classification of the Microstructural Elements of the Vegetal Tissue of the Pumpkin (Cucurbita pepo L.) Using Convolutional Neural Networks

et al. 2021

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Although knowledge of the microstructure of food of vegetal origin helps us to understand the behavior of food materials, the variability in the microstructural elements complicates this analysis. In this regard, the construction of learning models that represent the actual microstructures of the tissue is important to extract relevant information and advance in the comprehension of such behavior. Consequently, the objective of this research is to compare two machine learning techniques—Convolutional Neural Networks (CNN) and Radial Basis Neural Networks (RBNN)—when used to enhance its microstructural analysis. Two main contributions can be highlighted from this research. First, a method is proposed to automatically analyze the microstructural elements of vegetal tissue; and second, a comparison was conducted to select a classifier to discriminate between tissue structures. For the comparison, a database of microstructural elements images was obtained from pumpkin (Cucurbita pepo L.) micrographs. Two classifiers were implemented using CNN and RBNN, and statistical performance metrics were computed using a 5-fold cross-validation scheme. This process was repeated one hundred times with a random selection of images in each repetition. The comparison showed that the classifiers based on CNN produced a better fit, obtaining F1–score average of 89.42% in front of 83.83% for RBNN. In this study, the performance of classifiers based on CNN was significantly higher compared to those based on RBNN in the discrimination of microstructural elements of vegetable foods.

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Section: Improvement and Enhancementmentioning

confidence: 99%

Classification of the Microstructural Elements of the Vegetal Tissue of the Pumpkin (Cucurbita pepo L.) Using Convolutional Neural Networks

et al. 2021

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“…This technique requires the image acquisition of rice samples and computer vision algorithms to pre-process, analyze, and extract valuable information from the images to develop the classification models. Software, such as Matlab (Mathworks, Inc. Natick, MA, USA) [ 25 , 26 , 27 ] and LabVIEW (National Instruments, Austin, TX, USA) [ 28 , 29 , 30 ] and open-source libraries, such as OpenCV (Intel, Santa Clara, CA, USA) [ 31 , 32 , 33 ] and Python (Python Software Foundation, Wilmington, DE, USA) [ 34 ] are some of the most popular used among researchers. The artificial neural networks (ANNs) for supervised ML are well-known for solving multiclass classifications due to their ability to deal with non-linear data for pattern recognition to obtain high accuracy.…”

Section: Introductionmentioning

confidence: 99%

Computer Vision and Machine Learning Analysis of Commercial Rice Grains: A Potential Digital Approach for Consumer Perception Studies

Aznan

Viejo

Pang

et al. 2021

Sensors

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Rice quality assessment is essential for meeting high-quality standards and consumer demands. However, challenges remain in developing cost-effective and rapid techniques to assess commercial rice grain quality traits. This paper presents the application of computer vision (CV) and machine learning (ML) to classify commercial rice samples based on dimensionless morphometric parameters and color parameters extracted using CV algorithms from digital images obtained from a smartphone camera. The artificial neural network (ANN) model was developed using nine morpho-colorimetric parameters to classify rice samples into 15 commercial rice types. Furthermore, the ANN models were deployed and evaluated on a different imaging system to simulate their practical applications under different conditions. Results showed that the best classification accuracy was obtained using the Bayesian Regularization (BR) algorithm of the ANN with ten hidden neurons at 91.6% (MSE = <0.01) and 88.5% (MSE = 0.01) for the training and testing stages, respectively, with an overall accuracy of 90.7% (Model 2). Deployment also showed high accuracy (93.9%) in the classification of the rice samples. The adoption by the industry of rapid, reliable, and accurate methods, such as those presented here, may allow the incorporation of different morpho-colorimetric traits in rice with consumer perception studies.

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“…Machine learning has been used by [17] to determine the ripeness of yellow peach varieties with the Fluorescence Spectrometer using Partial Least Squares and Linear Discriminant Analysis machine learning methods. Machine learning predictions based on fruit colors are also used in [18] for classifying the maturity of cape gooseberry fruit. Neural Networks, Support Vector Machines, and Nearest Neighbors are used for the differentiation of fruit samples with the help of different color spaces.…”

Section: Introductionmentioning

confidence: 99%

Utilization of Explainable Machine Learning Algorithms for Determination of Important Features in ‘Suncrest’ Peach Maturity Prediction

et al. 2021

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Peaches (Prunus persica (L.) Batsch) are a popular fruit in Europe and Croatia. Maturity at harvest has a crucial influence on peach fruit quality, storage life, and consequently consumer acceptance. The main goal of this study is to develop a machine learning model that will detect the most important features for predicting peach maturity by first training models and then using the importance ratings of these models to detect nonlinear (and linear) relationships. Thus, the most important peach features at a given stage of its ripening could be revealed. To date, this method has not been used for this purpose, and at the same time, it has the potential to be applied to other similar peach varieties. A total of 33 fruit features are measured on the harvested peaches, and three imbalanced datasets are created using firmness thresholds of 1.84, 3.57, and 4.59 kg·cm−2. These datasets are balanced using the SMOTE and ROSE techniques, and the Random Forest machine learning model is trained on them. Permutation Feature Importance (PFI), Variable Importance (VI), and LIME interpretability methods are used to detect variables that most influence predictions in the given machine learning models. PFI shows that the h° and a* ground color parameters, COL ground color index, SSC/TA, and TA inner quality parameters are among the top ten most contributing variables in all three models. Meanwhile, VI shows that this is the case for the a* ground color parameter, COL and CCL ground color indexes, and the SSC/TA inner quality parameter. The fruit flesh ratio is highly positioned (among the top three according to PFI) in two models, but it is not even among the top ten in the third.

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Multivariate Analysis and Machine Learning for Ripeness Classification of Cape Gooseberry Fruits

Cited by 19 publications

References 36 publications

Classification of the Microstructural Elements of the Vegetal Tissue of the Pumpkin (Cucurbita pepo L.) Using Convolutional Neural Networks

Classification of the Microstructural Elements of the Vegetal Tissue of the Pumpkin (Cucurbita pepo L.) Using Convolutional Neural Networks

Computer Vision and Machine Learning Analysis of Commercial Rice Grains: A Potential Digital Approach for Consumer Perception Studies

Utilization of Explainable Machine Learning Algorithms for Determination of Important Features in ‘Suncrest’ Peach Maturity Prediction

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