Adaptive Edge Plant Model

--- language: - en license: mit tags: - agriculture - plant-pathology - mobilenetv4 - evidential-deep-learning - domain-adaptation - edge-cloud metrics: - accuracy pipeline_tag: image-classification model-index: - name: mobilenetv4_edge_best results: - task: type: image-classification name: Image Classification dataset: name: Plant Disease Classification Merged Dataset type: plant-disease-classification-merged-dataset metrics: - type: accuracy value: 92.23 name: Validation Accuracy --- # 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)](https://huggingface.co/Arko007/adaptive-edge-plant-model/blob/main/model_report.pdf)** --- ## 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  --- ## 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).* --- ## 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`).