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- W4288097308 abstract "The appearance of fruits and vegetables has a significant impact on their market value and consumer preference. Manual identification and sorting are time-consuming and costly. Many machine learning and deep learning models have been proposed in the past for fruit categorization which were deployed on GPUs. In this work, deep learning models for fruit classification are built on GPU and tested to be deployed on the ESP-32 microcontroller. This paper examines machine learning approaches based on CNN, Mobile net models for low cost and low power embedded devices to detect fruits and vegetables. Fruits 360 data set is used in this study where models are built to classify fruits into multi classes. The MobileNetV1 model fared well in distinguishing fruits and vegetables for 17 classes and achieved 94% testing accuracy on a google colab (GPU) environment. The model was deployed onto the ESP32 Cam environment and tested for its performance. The accuracy in prediction classes by the model on ESP32 was 77% with an inferencing speed of 51 ms and memory usage of 66.1 kb. Experimental results show that a low-cost ESP32 microcontroller can be deployed for agricultural product classification." @default.
- W4288097308 created "2022-07-28" @default.
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- W4288097308 date "2022-01-01" @default.
- W4288097308 modified "2023-09-29" @default.
- W4288097308 title "Real Time Classification of Fruits and Vegetables Deployed on Low Power Embedded Devices Using Tiny ML" @default.
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- W4288097308 doi "https://doi.org/10.1007/978-3-031-12413-6_27" @default.
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