language:
- en
license: mit
tags:
- agriculture
- plant-pathology
- convnext
- deep-learning
- edge-cloud
metrics:
- accuracy
pipeline_tag: image-classification
model-index:
- name: convnext_large_cloud_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: 96.42
name: Validation Accuracy
ConvNeXt-Large Cloud Plant Disease Diagnostician
This repository hosts the high-capacity Cloud Classifier (ConvNeXt-Large) for the Adaptive Edge-Cloud Plant Disease Diagnosis framework. The model is dynamically queried by edge nodes running MobileNetV4-EDL when classification confidence is low or uncertainty is high.
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)
2. Model Architecture and Training Details
- Model Type: ConvNeXt-Large
- Number of Classes: 88 (spanning various crop types including Apple, Tomato, Wheat, Soybean, Sugarcane, Tea, etc.)
- Resolution: $384 imes 384$ pixels
- Optimization Strategy: Stages 1-3 of the backbone were frozen to optimize GPU efficiency.
- Optimizer: AdamW (Learning Rate: $10^{-4}$, Weight Decay: $10^{-3}$)
- Loss Function: Categorical Cross-Entropy
Training & Validation Loss Curves
Training & Validation Accuracy Curves
3. Convergence Metrics Summary
The model was trained for 7 epochs on Kaggle GPU environments using a stratified dataset split (30,103 training images, 5,347 validation images):
| Epoch | Training Loss | Training Accuracy (%) | Validation Loss | Validation Accuracy (%) |
|---|---|---|---|---|
| 1 | 0.6872 | 82.83% | 0.1986 | 93.44% |
| 2 | 0.2168 | 93.04% | 0.1420 | 95.05% |
| 3 | 0.1629 | 94.61% | 0.1330 | 95.37% |
| 4 | 0.1394 | 95.45% | 0.1153 | 95.80% |
| 5 | 0.1235 | 95.83% | 0.1167 | 96.08% |
| 6 | 0.1104 | 96.24% | 0.1152 | 95.84% |
| 7 | 0.1039 | 96.47% | 0.1028 | 96.42% |
Note: Checkpoints for all epochs, including the best-performing convnext_large_cloud_best.pth, are stored directly in the Hugging Face model repository.
4. Collaborative Gating Mechanism
The Cloud model acts as a secondary diagnostician in the cooperative pipeline. The lightweight edge node decides whether to query this model by checking:
- Evidential Vacuity Threshold ($u > \tau_{vac}$): Triggers if the leaf image has out-of-distribution patterns or high epistemic uncertainty.
- Conformal Confidence Threshold ($p_{max} < \tau_{conf}$): Triggers if the calibrated categorical prediction confidence is low.