Perbandingan Performa Custom CNN, MobileNetV2, dan InceptionV3 untuk Identifikasi Penyakit Daun Tanaman
DOI:
https://doi.org/10.47065/bulletincsr.v6i5.1294Keywords:
Deep Learning; Plant Disease Identification; Transfer Learning; MobileNetV2; Batch Size; Explainable AIAbstract
Batch-size sensitivity in deep learning architectures is often assumed to behave uniformly, yet this study finds an opposite-direction pattern between architectures trained from scratch and those relying on Transfer Learning. Using a large-scale multi-commodity dataset (72,000 images, 72 disease classes from 21 commodities), Custom CNN improves consistently as batch size grows, whereas MobileNetV2 and InceptionV3 built on ImageNet pretrained weights via two-phase Feature Extraction and Fine-Tuning achieve their best performance at the smallest batch size (8), consistent with the sharp-versus-flat minima theoretical framework. The pattern is confirmed statistically using two-proportion z-tests (p<0.0001 across all Custom CNN batch-size comparisons). At its optimal configuration, MobileNetV2 significantly outperforms InceptionV3 (test accuracy 97.57% vs. 96.35%; z=4.24, p<0.0001) while being far more compact (±2.96 million parameters, roughly 8 times smaller). Explainable AI analysis (Confusion Matrix, t-SNE, and Grad-CAM) confirms this advantage stems from more separable class-level feature representations and attention concentrated on lesion regions rather than dataset artifacts, making MobileNetV2 at a small batch size a strong candidate for mobile deployment in smart farming.
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