Paper
20 October 2022 Fruit detection research based on near-infrared spectroscopy and lightweight neural network
Jinfu Zhang, Bin Tang, Jianxu Wang, Linfeng Cai, Junfeng Miao, Qing Chen, Hang Liu, Mingfu Zhao
Author Affiliations +
Proceedings Volume 12451, 5th International Conference on Computer Information Science and Application Technology (CISAT 2022); 124511A (2022) https://doi.org/10.1117/12.2656581
Event: 5th International Conference on Computer Information Science and Application Technology (CISAT 2022), 2022, Chongqing, China
Abstract
Spectroscopy is an important method in the field of nondestructive testing of fruits, and spectral pretreatment and feature screening are the difficulties and keys, which have a greater impact on the accuracy of the model, so this paper aims to use the lightweight neural network (MoblieNetV1) combined with near-infrared spectroscopy to improve the accuracy and stability of the model, reduce its computational amount, and verify its feasibility and effect. First, the fruit dataset is classified and labeled, and apples, pears, citrus, and peaches are labeled as 1, 2, 3, and 4, respectively, and then the sample spectra are put into model training in batches. MoblieNetV1 extracts features through the bottom layer, and then further extracts features at a deeper level, and finally obtains the classification of the target. Experimental results show that the moblieNetV1 used has achieved a high recognition accuracy, which is more suitable for use in the field of spectral detection than other neural networks, and avoids the cumbersome process of manually extracting features.
© (2022) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Jinfu Zhang, Bin Tang, Jianxu Wang, Linfeng Cai, Junfeng Miao, Qing Chen, Hang Liu, and Mingfu Zhao "Fruit detection research based on near-infrared spectroscopy and lightweight neural network", Proc. SPIE 12451, 5th International Conference on Computer Information Science and Application Technology (CISAT 2022), 124511A (20 October 2022); https://doi.org/10.1117/12.2656581
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KEYWORDS
Convolution

Data modeling

Near infrared spectroscopy

Neural networks

Convolutional neural networks

Performance modeling

Spectroscopy

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