Citation

Sungheetha, Akey and R., Rajesh Sharma (2026) Advanced Hybrid Deep Learning Framework for Multi-Scale Apple Disease Detection and Classification with Ensemble Learning. Procedia Computer Science, 283. pp. 5378-5388. ISSN 18770509

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Abstract

This paper proposes a multi-modal ensemble framework for apple leaf disease classification that integrates a bottleneck ResNet-101 backbone augmented with Squeeze-and-Excitation (SE) attention, Feature Pyramid Network (FPN), and four-level Spatial Pyramid Pooling (SPP) to expand feature representations from 2,048-d to 102,400-d (2048×50). Three-modality fusion (RGB, HSV, Augmented) with temperature-scaled calibration (T=2.0, wRGB=0.72, wHSV=0.68, wAUG=0.64) is applied across 2,848 apple leaf images spanning six disease categories, augmented to 7,292 training samples. The proposed ensemble achieves 97.77% accuracy, macro-F1 of 0.953, and macro-average AUC of 0.981 on a 539-sample test set at 52 ms per image, surpassing EfficientNet-B4 (93.0%) by 4.77 percentage points. Ablation confirms cumulative gains from SE attention (+1.6%), FPN (+1.3%), SPP (+0.8%), and ensemble calibration (+0.87%) over the 92.7% baseline, validating each component’s indispensable contribution to precision agriculture deployment. Experimental validation on Plant Village Dataset containing 3642 images across six disease categories demonstrates superior performance with precision of 96.4 percent, recall of 97.2 percent, and F1-score of 96.8 percent. The proposed framework processes images at 42 frames per second enabling real-time deployment in precision agriculture applications. Comparative analysis reveals 8.3 percent improvement over standalone deep learning models and 12.7 percent enhancement compared to traditional machine learning approaches

Item Type:	Article
Uncontrolled Keywords:	Deep Learning Framework, Precision Agriculture
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	02 Sep 2026 07:25
Last Modified:	02 Sep 2026 07:25
URII:	http://shdl.mmu.edu.my/id/eprint/16539

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