An Artificial Neural Network‐Based Method to Identify Five Classes of Almond According to Visual Features

J Food Process Engineering

2020

After harvesting almond crop, accurate measurement of almond kernel sizes is a significant specification to plan, develop and enhance almond processing operations. The size and mass of the individual almond kernels are vital parameters usually associated with almond quality and yield. In this study, we propose a novel methodology that combines image processing and machine‐learning ensemble that accurately measures the size and mass of whole raw almond kernels (classification—Nonpareil) simultaneously. We have developed an image‐processing algorithm using recursive method to identify the individual almond kernels from an image and estimate the size of the kernels based on the occupied pixels by a kernel. The number of pixels representing an almond kernel was used as its digital fingerprint to predict its size and mass. Various popular machine learning (ML) models were implemented to build a stacked ensemble model (SEM), predicting the mass of the individual almond kernels based on the features derived from the pixels of the individual kernels in the image. The prediction accuracy and robustness of image processing and SEM were analyzed using uncertainty quantification. The mean error in estimating the average length of 1,000 almond kernel was 3.12%. Similarly, mean errors associated with predicting the 1,000 kernel mass were 0.63%. The developed algorithm in almond imaging in this study can be used to facilitate a rapid almond yield and quality appraisals.

Section: Artificial Neural Networkmentioning

confidence: 99%

Stack ensembled model to measure size and mass of almond kernels

Vidyarthi

J Food Process Engineering

2020

“…To construct predictive models, Artificial Neural Network (ANN) method is getting very popular in the science and engineering field. Several researchers have used ANN to classify the almond kernel (Ding and Gunasekaran., 1995;Reshadsedghi and Mahmoudi, 2013;Teimouri et al, 2016). There are many activation functions available and they get distinguished according to the threshold and shape of the filter function.…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Size and mass prediction of almond kernels using machine learning image processing

2019

Preprint

After harvesting almond crop, accurate measurement of almond kernel sizes is a significant specification to plan, develop and enhance almond processing operations. The size and mass of the individual almond kernels are vital parameters usually associated with almond quality, particularly head almond yield. In this study, we propose a novel methodology that combines image processing and machine-learning ensemble that accurately measures the size and mass of whole raw almond kernels (classification -Nonpareil) simultaneously. We have developed an image-processing algorithm using recursive method to identify the individual almond kernels from an image and estimate the size of the kernels based on the occupied pixels by a kernel. The number of pixels representing an almond kernel was used as its digital fingerprint to predict its size and mass. Various popular machine learning (ML) models were implemented to build a stacked ensemble model (SEM), predicting the mass of the individual almond kernels based on the features derived from the pixels of the individual kernels in the image. The prediction accuracy and robustness of image processing and SEM were analyzed using uncertainty quantification. The mean error in estimating the average length of 1000 almond kernel was 3.12%. Similarly, mean errors associated with predicting the 1000 kernel mass were 0.63%. The developed algorithm in almond imaging in this study can be used to facilitate a rapid almond yield and quality appraisals.

“…Application of image processing conjugated with machine learning is relatively new which can be a useful technique to achieve rapid and accurate results in the area of nuts classification. Several researchers have explored the application of image processing and machine learning in the size and mass prediction, as well as classification of nuts (Halac, Sokic, & Turajilic, 2017;Mirzabe, Khazaei, Chegini, & Gholami, 2013;Singh, Bejagam, An, & Deshmukh, 2019;Teimouri, Omid, Mollazade, & Rajabipour, 2014;Teimouri, Omid, Mollazade, & Rajabipour, 2016;Vidyarthi, Tiwari, Singh, & Xiao, 2020). Mirzabe et al (2013)…”

Section: Application Of Image Processing and Machine Learning In Numentioning

confidence: 99%

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

Vidyarthi

J Food Process Engineering

et al. 2020

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).