Arko007/adaptive-edge-plant-model

MobileNetV4 Edge Plant Classifier (with EDL & GRL)

This repository hosts the lightweight Edge Classifier (MobileNetV4-Conv-Medium) for the Adaptive Edge-Cloud Plant Disease Diagnosis framework. The model features:

1. Evidential Deep Learning (EDL) Head to calculate epistemic uncertainty (vacuity $u$) in a single forward pass.
2. Gradient Reversal Layer (GRL) Domain Adaptation to reconcile lab-to-field domain shifts.
3. Conformal Temperature Scaling configuration parameters to provide distribution-free confidence guarantees.

1. Mathematical and Framework Documentation

A complete mathematical report detailing the Evidential Deep Learning (EDL), Unsupervised Domain Adaptation (UDA), and Conformal Calibration models is compiled as a PDF and available in this repository: 👉 Read the Mathematical Report (PDF)

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2. Model Architecture and Training Details

• Model Type: MobileNetV4-Conv-Medium with Evidential Classification Head & Domain Discriminator
• Number of Classes: 88 (spanning various crop types including Apple, Tomato, Wheat, Soybean, Sugarcane, Tea, etc.)
• Optimization Strategy: Trained from scratch with all layers unfrozen to adapt features to the plant classification domain.
• Optimizer: AdamW (Learning Rate: $10^{-3}$, Weight Decay: $10^{-3}$)
• Loss Function: Multi-Task Evidential Loss ($\mathcal{L}{mse} + \lambda_t \mathcal{L}{kl}$) with auxiliary Cross-Entropy ($\gamma = 0.1$) to prevent gradient vanishing.
• Domain Adaptation: Unsupervised Domain Adaptation (UDA) with Gradient Reversal Layer (GRL) mapping source (lab) to target (field) domains.

Training & Validation Loss Curves

Accuracy & Validation Vacuity Dynamics

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3. Convergence Metrics Summary

The model was trained for 10 epochs on Kaggle GPU environments using a stratified dataset split (30,103 training images, 5,347 validation images):

Epoch	Train Cls Loss	Train Dom Loss	Train Accuracy (%)	Validation Loss	Validation Accuracy (%)	Val Avg Vacuity ($u$)
1	2.0984	0.0245	67.60%	1.5441	81.49%	0.9606
2	1.5398	0.0119	81.84%	1.5426	82.20%	0.9557
3	1.4037	0.0110	85.19%	1.4969	83.47%	0.9433
4	1.3306	0.0118	86.85%	1.2157	88.87%	0.8990
5	1.2750	0.0122	87.91%	1.2090	88.82%	0.9011
6	1.2260	0.0201	88.87%	1.1313	90.27%	0.8808
7	1.1976	0.0199	89.49%	1.0826	91.61%	0.8664
8	1.1430	0.0199	90.36%	1.1093	90.55%	0.8598
9	1.1305	0.0212	90.40%	0.9965	92.23%	0.8219
10	1.1171	0.0213	90.99%	1.0341	91.97%	0.8321

Note: The best-performing checkpoint was recorded at Epoch 9 with 92.23% validation accuracy and the lowest average evidential uncertainty (vacuity = 0.8219).

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4. Collaborative Gating Mechanism

The Edge classifier is designed to run locally on resource-constrained devices. It makes predictions and computes the epistemic uncertainty (vacuity $u$) in a single forward pass.

• If vacuity exceeds the threshold ($u > au_{vac}$) OR maximum calibrated conformal confidence is below the threshold ($p_{max} < au_{conf}$), the diagnostic request is offloaded to the heavy cloud model (ConvNeXt-Large).