A Dehusked Areca Nut Classification Algorithm Based on 10-Fold Cross-Validation of Convolutional Neural Network

Patil, Sameer; Naik, Aparajita; Sequeira, Marlon; Parab, Jivan S.

doi:10.1007/978-3-031-28183-9_3

Cited by 1 publication

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“…Danti and Suresha (2012) have worked on segmenting and classifying raw areca nuts based on three sigma control limits with an accuracy of 97.63%. Chandrashekhara (Patil et al, 2023). This proposed work created a dataset containing 300 images of five categories of areca nuts employing two deep learning algorithms, a customized CNN and AlexNet to categorize Areca nuts.…”

Section: Introductionmentioning

confidence: 99%

Efficient Deep Learning model for de-husked Areca nut classification

Patil,

Naik,

Parab

2023

JANS

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Areca nut is a widely used agricultural product in India and even over the globe. Areca nut, a fruit of areca palm (Areca catechu) is grown widely in the Asia-Pacific region.. Areca nut segregation is of prime importance in the areca nut industry. The quality segregation of peeled/de-husked nuts requires skilled workers. This process of manual segregation is time-consuming and can lead to erroneous classification. Recent deep learning (DL) advances have improved the performance in multi-class problems. The present work presents the classification of de-husked areca nut among five classes using an efficient deep learning customized Convolutional Neural Network (CNN) and the results of this model were compared with the standard AlexNet architecture. The new CNN model was customized to obtain classification accuracy higher than the existing ones. A dataset of 300 nuts (60 per class) was created using a specially designed instrumentation setup. The areca nut images were then pre-processed and fed to these models to learn the features of the areca nut from different classes. The confusion matrix and Area Under the Curve - Receiver Operating Characteristics (AUC- ROC) were employed to assess the results of these models and cross-validated with 5 and 10-fold. The experimental results show that the CNN outperformed the AlexNet model with an average accuracy of 97.33% and 98.34%, F1 score of 97.48%, and 98.45% for 5 and 10 folds, respectively.

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Section: Introductionmentioning

confidence: 99%