Knowledge distillation with group convolution mapping layer for plant pest classification
Date Issued
2024
Author(s)
Khabarlak K.
Abstract
The increasing demand for high-quality food requires efficient monitoring and control
of greenhouse environments. One crucial aspect is the detection and classification of plant
pests, which can be achieved using deep learning-based computer vision techniques.
However, deploying these models on edge devices like Raspberry PI 4 in a stationary
greenhouse setup poses significant challenges due to limited computational resources.
To address this challenge, we employ knowledge distillation, a technique that transfers
knowledge from a large teacher network to a smaller student network. The goal is to retain the
accuracy of the teacher network while reducing the complexity of the student network.
of greenhouse environments. One crucial aspect is the detection and classification of plant
pests, which can be achieved using deep learning-based computer vision techniques.
However, deploying these models on edge devices like Raspberry PI 4 in a stationary
greenhouse setup poses significant challenges due to limited computational resources.
To address this challenge, we employ knowledge distillation, a technique that transfers
knowledge from a large teacher network to a smaller student network. The goal is to retain the
accuracy of the teacher network while reducing the complexity of the student network.
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