---
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.