<![CDATA[The grading of arecanuts before their sale is significant for enhancing profitability. The assessment of areca nut quality widely utilizes and respects both producer-level and wholesale dealer-level grading methods. This study proposes an advanced grading framework for white Chali-type arecanuts by developing a standardized image database and utilizing deep learning-based feature extraction. This research presents a novel approach by combining a representational deep neural network (ResNet) for automatic feature extraction with various spectral analysis methods, such as the Fourier transform and wavelet transform, to capture frequency-domain features. The support vector machine (SVM) model classifies these extracted features. The proposed system achieves an accuracy of 97.8%, which is significantly better than existing methods SVM with 72.5%, convolutional neural network (CNN) with 92.9%, AlexNet with 90.6%, and VGG19 with 90.2%. The results show that the proposed hybrid ResNet-SVM method improves accuracy, precision, recall, and F1-score, making it a more reliable and automated way to grade areca nuts. This method thus enhances efficiency, reduces manual effort, and ensures consistent quality assessment.]]>
Copyrights © 2025
