CNN Aided Surface Inspection for SMT Manufacturing

Loo, Mee Chun; Logeswaran, Rajasvaran; Salam, Zailan Arabee Abdul

doi:10.1109/dese58274.2023.10099694

Cited by 2 publications

(1 citation statement)

References 14 publications

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“…This can assist in retaining model performance on small datasets without the need for a high number of parameters by reducing the likelihood of the model being overly accurate. The fact that GoogleNet was developed with hardware capabilities in mind, such as the Single Instruction Multiple Data (SIMD) instructions that are present on many recent mobile devices [35], is another key aspect to take into consideration. This helps to maximize the usage of the resources available in the hardware.…”

Section: Introductionmentioning

confidence: 99%

A Convolutional Neural Network Algorithm for Pest Detection Using GoogleNet

Yulita,

Rambe,

Sholahuddin

et al. 2023

AgriEngineering

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The primary strategy for mitigating lost productivity entails promptly, accurately, and efficiently detecting plant pests. Although detection by humans can be useful in detecting certain pests, it is often slower compared to automated methods, such as machine learning. Hence, this study employs a Convolutional Neural Network (CNN) model, specifically GoogleNet, to detect pests within mobile applications. The technique of detection involves the input of images depicting plant pests, which are subsequently subjected to further processing. This study employed many experimental methods to determine the most effective model. The model exhibiting a 93.78% accuracy stands out as the most superior model within the scope of this investigation. The aforementioned model has been included in a smartphone application with the purpose of facilitating Indonesian farmers in the identification of pests affecting their crops. The implementation of an Indonesian language application is a contribution to this research. Using this local language makes it easier for Indonesian farmers to use it. The potential impact of this application on Indonesian farmers is anticipated to be significant. By enhancing pest identification capabilities, farmers may employ more suitable pest management strategies, leading to improved crop yields in the long run.

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Section: Introductionmentioning

confidence: 99%