Cambium Plant Disease Detector

CPU-optimized MobileNetV2 image classifier exported to ONNX and validated with ONNX Runtime.

Model details

• Runtime: ONNX Runtime
• Execution provider: CPUExecutionProvider
• Input: pixel_values, float32 NCHW tensor with shape [1, 3, 224, 224]
• Preprocessing: RGB, resize to 257px, center crop to 224px, then ImageNet normalization.
• Output: logits; map output indices using idx_to_class.json.

Intended use

Research and demonstration plant-disease image classification only. This is not a substitute for advice from a qualified agronomist or plant pathologist.

Files

• model.onnx: portable ONNX model.
• model_optimized.onnx: ONNX Runtime graph-optimized CPU model.
• idx_to_class.json: output-index mapping.
• metadata.json: parity and latency validation results.

Validation

The export script compares PyTorch and ONNX logits and fails if maximum absolute error exceeds 1e-3. It also benchmarks CPU inference and records mean and p95 latency in metadata.json.