Evaluasi Efisiensi Model Deep Learning pada Deteksi Penyakit Cabai Dengan DenseNet201 dan EfficientNetB0
Abstrak
This study compares the performance of DenseNet201 and EfficientNetB0 architectures in detecting five types of conditions on chili plant leaves, namely healthy, leaf curl, leaf spot, whitefly, and yellowish. Manual disease identification by farmers has accuracy limitations and is time-consuming, hence a transfer learning-based deep learning approach is proposed as a solution. A secondary dataset of 500 digital images was divided into 80% training data, 10% validation data, and 10% testing data. The training process utilized the Adam optimization function, an initial learning rate of 0.001, and data augmentation techniques to prevent overfitting. Both models were evaluated based on standard classification metrics and memory size efficiency. The results showed that DenseNet201 achieved the highest accuracy of 92.00% with a storage model size of 141.25 MB and a training time of 5.4 minutes. On the other hand, EfficientNetB0 produced an accuracy of 88.00% with a model size 2.2 times smaller at 62.25 MB and a shorter training time of 4.3 minutes. There is a significant trade-off in both architectures; DenseNet201 is recommended for cloud computing systems that prioritize high accuracy (92.00%) with a precision of 92.32%, while EfficientNetB0 is more prospective for development on mobile devices because the number of parameters is much smaller (8,068,200 compared to 24,347,653).
Identifier
- DOI: https://doi.org/10.24252/shift.v6i2.299
- OpenAlex: https://openalex.org/W7167332755