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README.md

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+---
+language:
+- en
+license: mit
+tags:
+- image-classification
+- pytorch
+- convnext
+- plant-disease
+- agriculture
+- edge-cloud
+pipeline_tag: image-classification
+model-index:
+- name: ConvNeXt-Large Cloud Plant Classifier
+  results:
+  - task:
+      type: image-classification
+      name: Image Classification
+    dataset:
+      name: Plant Disease Classification Merged Dataset (88 Classes)
+      type: plant-disease-classification-merged-dataset
+    metrics:
+    - type: accuracy
+      value: 96.42
+      name: Validation Accuracy
+---
+
+# ConvNeXt-Large Cloud Plant Classifier
+
+This repository hosts the **ConvNeXt-Large** cloud classifier model trained as part of the *Confidence-Aware Adaptive Edge–Cloud Framework for Reliable Plant Disease Diagnosis in Low-Connectivity Agricultural Environments*.
+
+The cloud model serves as the high-capacity, heavy diagnostician tier of the cooperative inference pipeline. It is triggered dynamically by the edge node when a captured leaf image exhibits high epistemic uncertainty (vacuity $u > 	au_{vac}$) or low conformal confidence ($p_{max} < 	au_{conf}$).
+
+## Model Architecture
+- **Backbone**: ConvNeXt-Large (originally pre-trained on ImageNet-1K)
+- **Classifier Head**: Modified Linear Layer projecting to 88 pathological crop classes
+- **Input Dimension**: $384 	imes 384$ pixels (higher resolution to capture fine-grained leaf spots)
+
+## Training Setup & Parameters
+- **Dataset**: Merged Plant Disease Dataset (88 unique classes spanning laboratory and field-captured folders).
+- **Optimization Strategy**: Parameter-Efficient Fine-Tuning (PEFT) with convolutional stages 1–3 frozen, training only the 4th stage and classifier head.
+- **Optimizer**: AdamW (Learning Rate: 1e-4, Weight Decay: 1e-3)
+- **Loss Function**: Cross-Entropy Loss
+- **Train/Val Split**: 85% Train / 15% Validation (Stratified per-folder split)
+
+## Operational Performance (Epoch 7 Results)
+Below are the recorded metrics from the training session:
+
+| Metric | Score / Value | Description |
+| :--- | :--- | :--- |
+| **Training Loss** | `0.1039` | Cross-Entropy loss at epoch 7 |
+| **Training Accuracy** | `96.47%` | Accuracy over the training split |
+| **Validation Loss** | `0.1028` | Cross-Entropy loss on the held-out split |
+| **Validation Accuracy** | **`96.42%`** | Calibrated validation classification rate |
+
+## Integration & Deployment
+The model checkpoint is designed to be hosted in a FastAPI-based Docker container on Hugging Face Spaces (Port 7860), responding to offloading queries via HTTP REST or binary JSON over MQTT v5 brokers.