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

Abstract

ABSTRACT.A novel intelligent automated model to recognize and classify a cashew kernels using Artificial Neural Network (ANN). The model primarily intends to work on two phases. The phase one, built with a proposed method to extract features, which includes 16 morphological features and also 24 color features from the input cashew kernel images. In phase two, a Multilayer Perceptron ANN is being used to recognize and classify the given white wholes grades using back propagation learning algorithm. The proposed method achieves a classification accuracy of 88.93%. This study also reveals that the combination of morphological and color features outperforms rather using any one set of features separately to grade cashew kernels.Keywords: White Wholes (WW) grade cashew kernel images, feature extraction, artificial neural networks, classification.Detectar e classificar castanhas de caju, tipo inteiro branco, através de rede neural artificial RESUMO. Analisa-se um novo modelo automatizado para detectar e classificar castanhas de caju por uma rede neural artificial. O modelo funciona em duas frases. Fase 1 é construída para detectar as características, as quais incluem 16 fatores morfológicos e 24 características de cor a partir de imagens da castanha do caju. Na Fase 2 emprega-se a rede neural artificial multicamada para detectar e classificar os graus inteiros brancos por algoritmo de propagação reversa. O método classifica com uma exatidão de 88,93%. Essa investigação também revela que a combinação das características morfológicas e de cor vai além de que quando se usa uma série de características separadas para classificar as castanhas de caju.Palavras-chave: imagens de castanhas de caju serie brancos inteiros, características de extração, rede neural artificial, classificação.

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

“…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.…”

“…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.…”

“…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.…”

“…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.…”

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

“…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.…”