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Real-time on-device weed identification using a hardware-efficient lightweight CNN.

Yuxuan Zhang^1,2, Yuchen Lu³, Luciano Sebastian Martinez-Rau^2,4

¹College of Intelligent Science and Engineering, Beijing University of Agriculture, Beijing, China.

Frontiers in Plant Science

|March 4, 2026

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This summary is machine-generated.

A new lightweight deep learning model, TinyWeedNet, enables real-time, on-device weed identification for autonomous farming. This energy-efficient solution is ideal for low-power agricultural devices, enhancing precision agriculture strategies.

Area of Science:

Agricultural Engineering
Computer Science
Machine Learning

Background:

Accurate weed identification is crucial for sustainable crop management in autonomous agricultural systems.
Existing deep learning models often require significant computational resources (e.g., GPUs), limiting their deployment on low-power field devices.

Purpose of the Study:

To develop a hardware-efficient, lightweight convolutional neural network (CNN) for real-time, on-device weed identification.
To enable the deployment of advanced weed recognition capabilities in resource-constrained precision agriculture platforms.

Main Methods:

Proposed TinyWeedNet, a CNN integrating multi-scale feature extraction, depthwise separable inverted residual blocks, and compact channel attention.
Trained and tested the model on the DeepWeeds dataset.

Keywords:

TinyML embedded systems energy-efficient computing lightweight convolutional neural network (CNN)on-device inference precision agriculture weed identification

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Implemented TinyWeedNet on an STM32H7 microcontroller using the TinyML workflow for embedded execution.

Main Results:

Achieved 97.26% classification accuracy with only 0.48 million parameters.
Demonstrated sub-90 ms inference time and low energy consumption on the microcontroller.
TinyWeedNet showed a favorable balance between accuracy, speed, and energy efficiency.

Conclusions:

TinyWeedNet offers a practical solution for integrating real-time, low-power weed identification into field robots, UAVs, and sensors.
This contributes to more autonomous and energy-aware weed management strategies in precision agriculture.