Attention Augmented Deep Learning Model for Enhanced Feature Extraction in Cacao Disease Recognition - Robet, Robet; Perangin Angin, Johanes Terang Kita; Siregar, Tarq Hilmar (Politeknik Ganesha Medan, 2025)
Accurate cacao disease recognition is critical for safeguarding yields and reducing losses. Prior cacao studies primarily rely on handcrafted descriptors (eg, Color Histogram, LBP, GLCM) or standard CNN/transfer-learning pipelines, often limited to ≤ 3 classes and a single plant organ; explicit channel-spatial attention and comprehensive multiclass evaluation remain uncommon. To the best of our knowledge, no prior work integrates Squeeze-and-Excitation (SE) and the Convolutional Block Attention Module (CBAM) on a ResNeXt50 backbone for six-class cacao disease classification, accompanied by a standardized ablation study and t-SNE-based interpretability. We propose a six-class classifier (five diseases + healthy) built on ResNeXt-50 enhanced with SE (channel recalibration) and CBAM (channel-spatial emphasis) to highlight lesion-relevant cues. The dataset comprises labeled leaf and pod images from public sources collected under field-like conditions; preprocessing includes resizing to 224x224, normalization, and augmentation (flips, small rotations, color jitter, random resized crops). Trained with Adam and early stopping, ResNeXt50+SE+CBAM attains 97% test accuracy and 0.97 macro-F1, surpassing a ResNeXt50 baseline of 94% and 0.95 and SE-only/CBAM-only variants. Confusion matrix and t-SNE analyses show fewer mix-ups among visual classes and clearer separability, while the ablation validates complementary benefits of SE and CBAM. On a desktop-hosted, web-based setup, batch-1 inference at 224x224 is 7.46 ms/image (134 FPS), demonstrating real-time capability. The findings support deployment as browser-based decision-support tools for farmers and integration into continuous field-monitoring systems.