Plant disease detection model for edge computing devices
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Plant disease detection model for edge computing devices. / Khan, Ameer Tamoor; Jensen, Signe Marie; Khan, Abdul Rehman; Li, Shuai.
I: Frontiers in Plant Science, Bind 14, 1308528, 2023.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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TY - JOUR
T1 - Plant disease detection model for edge computing devices
AU - Khan, Ameer Tamoor
AU - Jensen, Signe Marie
AU - Khan, Abdul Rehman
AU - Li, Shuai
N1 - Publisher Copyright: Copyright © 2023 Khan, Jensen, Khan and Li.
PY - 2023
Y1 - 2023
N2 - In this paper, we address the question of achieving high accuracy in deep learning models for agricultural applications through edge computing devices while considering the associated resource constraints. Traditional and state-of-the-art models have demonstrated good accuracy, but their practicality as end-user available solutions remains uncertain due to current resource limitations. One agricultural application for deep learning models is the detection and classification of plant diseases through image-based crop monitoring. We used the publicly available PlantVillage dataset containing images of healthy and diseased leaves for 14 crop species and 6 groups of diseases as example data. The MobileNetV3-small model succeeds in classifying the leaves with a test accuracy of around 99.50%. Post-training optimization using quantization reduced the number of model parameters from approximately 1.5 million to 0.93 million while maintaining the accuracy of 99.50%. The final model is in ONNX format, enabling deployment across various platforms, including mobile devices. These findings offer a cost-effective solution for deploying accurate deep-learning models in agricultural applications.
AB - In this paper, we address the question of achieving high accuracy in deep learning models for agricultural applications through edge computing devices while considering the associated resource constraints. Traditional and state-of-the-art models have demonstrated good accuracy, but their practicality as end-user available solutions remains uncertain due to current resource limitations. One agricultural application for deep learning models is the detection and classification of plant diseases through image-based crop monitoring. We used the publicly available PlantVillage dataset containing images of healthy and diseased leaves for 14 crop species and 6 groups of diseases as example data. The MobileNetV3-small model succeeds in classifying the leaves with a test accuracy of around 99.50%. Post-training optimization using quantization reduced the number of model parameters from approximately 1.5 million to 0.93 million while maintaining the accuracy of 99.50%. The final model is in ONNX format, enabling deployment across various platforms, including mobile devices. These findings offer a cost-effective solution for deploying accurate deep-learning models in agricultural applications.
KW - classifier
KW - deep learning
KW - edge computing
KW - MobileNetV3
KW - PlantVillage
U2 - 10.3389/fpls.2023.1308528
DO - 10.3389/fpls.2023.1308528
M3 - Journal article
C2 - 38143571
AN - SCOPUS:85180519020
VL - 14
JO - Frontiers in Plant Science
JF - Frontiers in Plant Science
SN - 1664-462X
M1 - 1308528
ER -
ID: 378184078