Resource-Efficient Continual Learning for Medicinal Plant Identification: A Periodic Retraining Approach for Edge-Deployed Agricultural IoT Applications

Smartphone-based plant identification increasingly serves as the edge tier of agricultural Internet of Things (IoT) systems, where models must adapt to crowdsourced data under bandwidth, memory, and energy constraints. No prior work has systematically investigated continual learning at the scale of thousands of fine-grained medicinal plant species, nor how retraining frequency affects the cost–performance trade-off in an IoT model-lifecycle setting. We evaluate three continual learning strategies—naïve fine-tuning, experience replay, and Learning without Forgetting—under periodic retraining schedules (updating every K increments), tested on 2,719 species (≥25 images each) from the Viet Medi Species 2026 dataset (310,647 images; 4,799 species total). All three strategies exhibit negative forgetting (performance improvement rather than degradation) in the instance-incremental setting, with naïve fine-tuning and LwF showing the strongest gains. Periodic retraining with K=2 reduces retraining operations by approximately 50% while maintaining performance. A baseline MobileNetV2 model achieves 54.07% top-10 accuracy across 2,719 species and has been deployed via TensorFlow Lite (FP16, ∼11.5 MB) in the Med Herb Lens Android application. Naïve fine-tuning is recommended as the practical default for instance-incremental agricultural IoT deployments.