TinyML-Enabled IoT Edge Framework with Knowledge Distillation for Weed Classification

Abstract

Weed classification is a fundamental perception task for agricultural robots and an essential enabler of precision and sustainable farming. Existing solutions often rely on high-power edge computing platforms, which limit long-term autonomous operation in Internet of Things (IoT) environments. Meanwhile, the computational complexity of high-accuracy deep learning models hinders their deployment on resource-constrained micro-controllers (MCUs), a critical component of IoT edge nodes. To address these challenges, this paper proposes a TinyML-enabled energy-efficient IoT framework for on-device weed classification, integrating a novel Three-Dimensional Alignment Knowledge Distillation (TDA-KD) strategy with a lightweight multi-layer dilated-convolution student network. The framework enhances knowledge transfer by jointly aligning (i) individual predictions, (ii) inter-sample correlations, and (iii) class semantics, further strengthened through a multi-temperature calibration mechanism. Experimental results on the DeepWeeds and 4Weeds datasets demonstrate that the proposed student model achieves over 95% classification accuracy with only 240K parameters and 87.51 MFLOPs. The model is successfully deployed on an OpenMV H7 Plus board with an STM32H7 MCU, requiring just 105.68 KB Flash memory and achieving an inference time of 378.3 ms with 510.7 mJ energy consumption per sample. A runtime analysis on the Vitirover horticultural robot shows that, compared with a Jetson Nano-based implementation, the proposed IoT pipeline extends operational time by approximately 30.5%. These results highlight the feasibility of deploying high-accuracy weed classification directly on ultra-low-power IoT devices, thereby significantly enhancing the autonomy, energy efficiency, and scalability of agricultural robots.