&lt;b&gt;Recognition and classification of White Wholes (WW) grade cashew kernel using artificial neural networks

Ganganagowdar, Narendra Veranagouda; Siddaramappa, Hareesha Katiganere

doi:10.4025/actasciagron.v38i2.27861

Cited by 9 publications

(5 citation statements)

References 16 publications

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“…Inception-V3, ResNet50, and VGG-16 had the highest values for all the three measures (approximately 96, 99, and 96%, respectively) and the custom model had the least values (88, 97, and 88%, respectively). The classification accuracy of different DCNN models used in this study was higher than that obtained by Arora and Devi (2018) who used SVM and random forest classifiers to classify cashews and obtained 90.6% and 94.28% accuracies, respectively, as well as, Ganganagowdar and Siddaramappa (2016) such as drop out and batch normalization can enhance the fitness of the custom model. Moreover, a low learning rate of the model can cause slow convergence, whereas a very high learning rate can lead to a fast but overall inaccurate solution.…”

Section: Resultscontrasting

confidence: 67%

“…Inception‐V3, ResNet50, and VGG‐16 had the highest values for all the three measures (approximately 96, 99, and 96%, respectively) and the custom model had the least values (88, 97, and 88%, respectively). The classification accuracy of different DCNN models used in this study was higher than that obtained by Arora and Devi (2018) who used SVM and random forest classifiers to classify cashews and obtained 90.6% and 94.28% accuracies, respectively, as well as, Ganganagowdar and Siddaramappa (2016) who used ANN model to classify cashews and achieved 88.93% of classification accuracy. In another study, Thakkar et al (2011) implemented fuzzy logic‐based computer vision system for classification of whole cashew kernels and achieved 89% classification accuracy.…”

Section: Resultscontrasting

confidence: 66%

“…Authors concluded that the accuracy of the model improved from 86% to 93% by considering three cashew kernel's features, length, width, and thickness compared to just two features, length and width. Ganganagowdar and Siddaramappa (2016) Arora and Devi (2018) performed cashew grading and classification using various image processing techniques in conjunction with several machine learning models. It was found that support vector machines (SVM) with "one-vs-one" classification and adaptive directed acyclic graph (ADAG) learning models provided satisfactory results.…”

Section: Application Of Image Processing and Machine Learning In Camentioning

confidence: 99%

“…Authors concluded that the accuracy of the model improved from 86% to 93% by considering three cashew kernel's features, length, width, and thickness compared to just two features, length and width. Ganganagowdar and Siddaramappa (2016) recognized and classified white whole cashew kernels using ANN machine learning model. They included 16 morphological features and 24 color features from the input cashew kernel images and fed to Multilayer Perceptron ANN to recognize and classify them.…”

Section: Introductionmentioning

confidence: 99%

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Classification of first quality fancy cashew kernels using four deep convolutional neural network models

Vidyarthi

Singh

Tiwari

et al. 2020

J Food Process Engineering

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In this study, we proposed deep convolutional neural networks (DCNNs) combined with image processing to classify cashew kernels in five categories based on the adulteration of first-class fancy whole cashew kernels with butts and pieces. Four DCNN models, including Inception-V3, ResNet50, VGG-16, and a custom model were implemented, and their performances were compared using model evaluators, such as sensitivity, specificity, precision, accuracy, and F1-score. Overall, all the models showed a high performance with a minimum accuracy of 95.1% and the training and validation data curves demonstrated a good fitting of the models. Although all the models demonstrated promising potential for cashew classification, Inception-V3 and ResNet50 neural networks delivered the most promising outcome with the highest accuracies (each 98.4%) and F1-scores (each 96%). Custom-built model showed the least accuracy (95.1%) and F1-score (87.9%). The findings of current work indicate that the developed DCNN models are capable of achieving automatic, fast, and accurate cashew classification. Practical application Currently, the sorting and grading of cashew kernel are mostly manual and timeconsuming process and the deep convolutional neural networks (DCNN) or Deep Learning implemented in this study can facilitate a speedy and automated cashew classification in cashew industry worldwide. 1 | INTRODUCTION 1.1 | General overview of cashew nuts and their geographical production/distribution Cashew (Anacardium occidentale L.) nuts belong to a tropical evergreen tree from the Anacardiaceae family of plants, which also includes mango, pistachio, poison oak, and poison ivy. They contain about 35-45% seeds and 55-65% shells by weight (Mgaya et al., 2019). Cashew nuts are kidney-shaped nuts which are rich in unsaturated fatty acids, high protein, and low levels of saturated fats and sugars (Rao, 1998). Cashew nuts are an important ingredient in snack and confectionary industries and are generally consumed in raw, roasted, salted or flavored forms (Papadenetriou, 1998). About 60% of the cashew kernels are consumed in the form of snacks and remaining 40% are included in other food preparations, mainly confectionary (Azam-Ali & Judge, 2001). In 2017, the worldwide production of raw cashew nuts was about 4 million metric tons and it is expected that cashew nuts will take over 29% of total nut market by 2021 (Yun, 2018). The main cashew production regions are Vietnam, India, Africa, and Philippines. Vietnam is considered the largest cashew exporter, followed by India, Netherlands, and African countries (Yun, 2018). Cashew nuts have an established market in the US with a wide range of uses and is considered the largest importer in the world accounting for about 35% of the total import (Azam-Ali & Judge, 2001).

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Section: Resultscontrasting

confidence: 67%

Section: Resultscontrasting

confidence: 66%

Section: Application Of Image Processing and Machine Learning In Camentioning

confidence: 99%

“…Authors concluded that the accuracy of the model improved from 86% to 93% by considering three cashew kernel's features, length, width, and thickness compared to just two features, length and width. Ganganagowdar and Siddaramappa (2016) recognized and classified white whole cashew kernels using ANN machine learning model. They included 16 morphological features and 24 color features from the input cashew kernel images and fed to Multilayer Perceptron ANN to recognize and classify them.…”

Section: Introductionmentioning

confidence: 99%

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Classification of first quality fancy cashew kernels using four deep convolutional neural network models

Vidyarthi

Singh

Tiwari

et al. 2020

J Food Process Engineering

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“…In this study, a Multilayer Feed-Forward Neural Network was used, and the accuracy of this algorithm was 90% (Ganganagowdar and Siddaramappa, 2011b). A novel intelligent model extracted 24 colour and 16 morphological features of the cashew kernel and used a Multilayer Perceptron ANN to recognize and classify white wholes into different grades using a Backpropagation learning algorithm and attained a classification of 88.93% (Ganganagowdar and Siddaramappa, 2016). Vidyarthi et al (2020) applied four different deep Convolutional Neural Network models, which are VGG-16, ResNet50, Inception-V3 and a custom model to classify cashew kernels into five categories.…”

Section: Figure 1 Cashew Production In Tanzania Between 2014 and 2020...mentioning

confidence: 99%

Traditional Machine Learning-Based Classification of Cashew Kernels Using Colour Features

BAİTU

GADALLA

Öztekin

2023

Tekirdağ Ziraat Fakültesi Dergisi

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Cashew is one of the major commercial commodities contributing to the national economy of Tanzania as foreign revenue. And yet still the processing of cashew is run locally using manual labour for a big part. If processed well under ideal conditions, cashews kernels are expected to be white in colour. But due to various factors like prolonged roasting in the steam chambers or over-drying, some cashew kernels tend to have a slight brown colour, and these are referred to as scorched cashews. Despite sharing the same characteristics with white cashew kernels, including nutritional quality, these cashew kernels are supposed to be graded differently. In many places around the world, particularly in Tanzania, the sorting and grading process of cashew kernels is performed by hand. In international trade, cashew grading is very important and this means more effective and consistent methods need to be applied in this stage of production in order to increase the quality of the products. The objective of this study was to evaluate the use of traditional Machine Learning techniques in the classification of cashew kernels as white or scorched by using colour features. In this experiment, various colour features were extracted from the images. The extracted features include the means (μ), standard deviations (σ), and skewness (γ) of the channels in RGB and HSV colour spaces. The relevant features for this classification problem were selected by applying the wrapper approach using the Boruta Library in Python, and the irrelevant ones were removed. 5 models are studied and their efficiencies analysed. The studied models are Logistic Regression, Decision Tree, Random Forest, Support Vector Machine and K-Nearest Neighbour. The Decision Tree model recorded the least accuracy of 98.4%. The maximum accuracy of 99.8% was obtained in the Random Forest model with 100 trees. Due to simplicity in application and high accuracy, the Random Forest is recommended as the best model from this study.

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Perspective on Power Smart Customer Service Based on AI

Huang

Yin

et al. 2022

Digital TV and Wireless Multimedia Communications

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<b>Recognition and classification of White Wholes (WW) grade cashew kernel using artificial neural networks

Cited by 9 publications

References 16 publications

Classification of first quality fancy cashew kernels using four deep convolutional neural network models

Classification of first quality fancy cashew kernels using four deep convolutional neural network models

Traditional Machine Learning-Based Classification of Cashew Kernels Using Colour Features

Perspective on Power Smart Customer Service Based on AI

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