<![CDATA[Edge deployment of deep learning models for plant disease classification requires model compression to fit resource-constrained hardware, yet post-training quantization (PTQ) may introduce accuracy loss that is not always well characterized for compact architectures. This progress report presents interim results from a wider study optimizing a fine-tuned MobileNetV3-Small model for tomato leaf disease classification. After training reached 99.71% validation accuracy on a 10-class PlantVillage subset, standard INT8 static PTQ using ONNX Runtime and TensorFlow Lite produced a severe accuracy collapse (10.4-27.4%) despite systematically testing eleven quantization configurations, including per-channel/per-tensor granularity, calibration methods, operator exclusion, and export settings. Diagnostic analysis revealed a persistent class-wise failure pattern rather than random noise, and an explicit shape-mismatch error confirmed a quantizer defect in per-channel bias requantization for grouped convolutions. As a mitigation, dynamic weight-only and dynamic-range quantization schemes were adopted, restoring laboratory accuracy to 99.4% with 29.3-72.0% size reduction, although field-condition accuracy on secondary open field data dropped to 43.1-43.9%, indicating a substantial domain generalization gap that motivates the next research phase.]]>
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