Using mobile edge AI to detect and map diseases in citrus orchards.
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Date
2023
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Abstract
Deep Learning models have presented promising results when applied to Agriculture
4.0. Among other applications, these models can be used in disease detection and fruit counting.
Deep Learning models usually have many layers in the architecture and millions of parameters.
This aspect hinders the use of Deep Learning on mobile devices as they require a large amount of
processing power for inference. In addition, the lack of high-quality Internet connectivity in the
field impedes the usage of cloud computing, pushing the processing towards edge devices. This
work describes the proposal of an edge AI application to detect and map diseases in citrus orchards.
The proposed system has low computational demand, enabling the use of low-footprint models
for both detection and classification tasks. We initially compared AI algorithms to detect fruits
on trees. Specifically, we analyzed and compared YOLO and Faster R-CNN. Then, we studied
lean AI models to perform the classification task. In this context, we tested and compared the
performance of MobileNetV2, EfficientNetV2-B0, and NASNet-Mobile. In the detection task, YOLO
and Faster R-CNN had similar AI performance metrics, but YOLO was significantly faster. In
the image classification task, MobileNetMobileV2 and EfficientNetV2-B0 obtained an accuracy of
100%, while NASNet-Mobile had a 98% performance. As for the timing performance, MobileNetV2
and EfficientNetV2-B0 were the best candidates, while NASNet-Mobile was significantly worse.
Furthermore, MobileNetV2 had a 10% better performance than EfficientNetV2-B0. Finally, we
provide a method to evaluate the results from these algorithms towards describing the disease spread
using statistical parametric models and a genetic algorithm to perform the parameters’ regression.
With these results, we validated the proposed pipeline, enabling the usage of adequate AI models to
develop a mobile edge AI solution.
Description
Keywords
Mobile edge computing, Deep learning
Citation
SILVA, J. C. F. da et al. Using mobile edge AI to detect and map diseases in citrus orchards. Sensors, v. 23, n. 4, artigo 2165, 2023. Disponível em: <https://www.mdpi.com/1424-8220/23/4/2165>. Acesso em: 06 jul. 2023.