Feature Extraction Methods on Convolutional Neural Network Models for Objective Classification of Cacao Pods

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Abstract

The classification of cacao pods in the agricultural sector is crucial in determining the quality and validation of the cacao product. This study presents a classification system for healthy and unhealthy cacao pods using Convolutional Neural Networks (CNNs) integrated with three feature extraction methods: Local Binary Pattern (LBP), Edge Feature (EF), and Gabor Filter (GF). Performance of formulated models is then evaluated based on four classification metrics: Accuracy (Acc), Precision (P), Recall (R), Kappa Score (K), and F1-measure (F1). Experimental results showed that the CNN model with EF obtained the highest classification performance with Acc=0.9615, P=0.9615, R=0.9615, K=0.923, and F1=0.9615. The CNN+EF model showed perfect predictive power in classifying healthy and unhealthy cacao pods. The model obtained a high accuracy, considering that the images were trained with their actual environment, making it an advantage over other scholarly works where the pods were captured with a uniform background.

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