A novel intelligent mobile application using human-centered AR: A case study in orange inspection

Silva, Jonathan C. F. da; Silva, Mateus Coelho; Delabrida, Saul; Luz, Eduardo; Oliveira, Ricardo Augusto Rabelo

doi:10.5753/ihc_estendido.2022.224837

Cited by 2 publications

(1 citation statement)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We considered the possibility of ground-based sampling. As supported by some previous studies [ 41 , 47 , 48 ], this sampling can be supported by wearable computing solutions. Thus, we considered that a user wearing such solutions can perform these measurements in the field.…”

Section: Methodssupporting

confidence: 56%

Using Mobile Edge AI to Detect and Map Diseases in Citrus Orchards

Silva

Luz

et al. 2023

Sensors

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Methodssupporting

confidence: 56%