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Pixelwise Complex-Valued Neural Network Based on 1D FFT of Hyperspectral Data to Improve Green Pepper Segmentation in Agriculture

Authors :
Xinzhi Liu
Jun Yu
Toru Kurihara
Congzhong Wu
Zhao Niu
Shu Zhan
Source :
Applied Sciences, Vol 13, Iss 4, p 2697 (2023)
Publication Year :
2023
Publisher :
MDPI AG, 2023.

Abstract

It seems difficult to recognize an object from its background with similar color using conventional segmentation methods. An efficient way is to utilize hyperspectral images that contain more wave bands and richer information than only RGB components. Particularly in our task, we aim to separate a pepper from densely packed green leaves for automatic picking in agriculture. Given that hyperspectral imaging can be regarded as a kind of wave propagation process, we make a novel attempt of introducing a complex neural network tailored for wave-related problems. Due to the lack of hyperspectral data, pixelwise training is deployed, and 1D fast Fourier transform of the hyperspectral data is used for the construction of complex input. Experimental results have showcased that a complex neural network outperforms a real-valued one in terms of detection accuracy by 3.9% and F1 score by 1.33%. Moreover, it enables the ability to select frequency bands used such as low-frequency components to boost performance as well as prevent overfitting problems for learning more generalization features. Thus, we put forward a lightweight pixelwise complex model for hyperspectral-related problems and provide an efficient way for green pepper automatic picking in agriculture using small datasets.

Details

Language :
English
ISSN :
20763417
Volume :
13
Issue :
4
Database :
Directory of Open Access Journals
Journal :
Applied Sciences
Publication Type :
Academic Journal
Accession number :
edsdoj.9cb08d3c15546daa29ea88c94c30c5e
Document Type :
article
Full Text :
https://doi.org/10.3390/app13042697