Accelerating Image-based Pest Detection on a Heterogeneous Multi-core Microcontroller
Luca Bompani, Luca Crupi, Daniele Palossi, Olmo Baldoni, Davide, Brunelli, Francesco Conti, Manuele Rusci, Luca Benini

TL;DR
This paper demonstrates that a novel heterogeneous microcontroller can efficiently perform energy-efficient CNN inference for pest detection in field sensor nodes, significantly improving speed and power consumption over previous solutions.
Contribution
It introduces the GreenWaves GAP9 MCU with CNN hardware acceleration for real-time pest detection, achieving high accuracy and low energy consumption in embedded systems.
Findings
CNN outperforms Viola-Jones in accuracy and generalization.
Inference time is reduced to 147 ms per image with CNN acceleration.
Power consumption is significantly lower, enabling long-term field deployment.
Abstract
The codling moth pest poses a significant threat to global crop production, with potential losses of up to 80% in apple orchards. Special camera-based sensor nodes are deployed in the field to record and transmit images of trapped insects to monitor the presence of the pest. This paper investigates the embedding of computer vision algorithms in the sensor node using a novel State-of-the-Art Microcontroller Unit (MCU), the GreenWaves Technologies' GAP9 System-on-Chip, which combines 10 RISC-V general purposes cores with a convolution hardware accelerator. We compare the performance of a lightweight Viola-Jones detector algorithm with a Convolutional Neural Network (CNN), MobileNetV3-SSDLite, trained for the pest detection task. On two datasets that differentiate for the distance between the camera sensor and the pest targets, the CNN generalizes better than the other method and achieves…
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Taxonomy
TopicsSmart Agriculture and AI · Advanced Chemical Sensor Technologies
MethodsConvolution
