Klasifikasi Tanaman Umbi Berbasis Citra Digital Menggunakan Convolutional Neural Network dengan Transfer Learning EfficientNet
DOI:
https://doi.org/10.47065/bulletincsr.v6i5.1051Keywords:
Tuber Crops; Image Classification; CNN; Transfer Learning; Efficientnet; Confusion MatrixAbstract
Manual classification of tuber crops often suffers from misidentification due to the visual similarity among tuber types in terms of color, texture, and shape. Digital image analysis combined with deep learning provides a more objective approach for automated identification. This study aims to develop a tuber crop image classification model using a Convolutional Neural Network (CNN) with transfer learning based on the EfficientNet architecture. The research stages include building a tuber image dataset, performing image preprocessing (resizing and normalization), designing an EfficientNet model with pretrained weights as a feature extractor, training the model on the training set, and evaluating it on a separate testing set. Model performance is assessed using accuracy, precision, recall, and F1-score, while a confusion matrix is used to analyze the distribution of predictions across classes. The experimental results indicate that the EfficientNet-based CNN achieves strong classification performance, reflected by high accuracy and stable evaluation metrics across all classes. Misclassifications are relatively limited and mostly occur among classes with highly similar visual characteristics. These findings confirm that EfficientNet transfer learning is effective in improving both accuracy and training efficiency for tuber crop image classification and has potential to be further developed as a decision-support tool in smart agriculture applications.
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