Deep learning driven methodology for the prediction of mushroom moisture content using a novel LED-based portable hyperspectral imaging system
This study proposes a deep-learning driven methodology for the analysis of mushroom moisture content (MC) datasets acquired using a novel portable hyperspectral imaging (HSI) system. One-dimensional convolutional neural network (1D-CNN) was developed and validated to process the raw HSI data of whit...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-03-01
|
| Series: | Smart Agricultural Technology |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772375524003514 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850041514432921600 |
|---|---|
| author | Kai Yang Ming Zhao Dimitrios Argyropoulos |
| author_facet | Kai Yang Ming Zhao Dimitrios Argyropoulos |
| author_sort | Kai Yang |
| collection | DOAJ |
| description | This study proposes a deep-learning driven methodology for the analysis of mushroom moisture content (MC) datasets acquired using a novel portable hyperspectral imaging (HSI) system. One-dimensional convolutional neural network (1D-CNN) was developed and validated to process the raw HSI data of white button mushrooms (Agaricus bisporus) for MC prediction. For comparison purposes, state-of-the-art machine learning algorithms, i.e., support vector machine regression (SVMR) and partial least squares regression (PLSR) were also investigated for the model development based on five spectra pre-processed methods using two different lighting systems i.e., enhanced light-emitting diode (LED) and tungsten halogen (TH). Overall, the predictive models based on the HSI data acquired using the LED lights (Rp2 of 0.977, RMSEP of 4.27 %, and RPDp of 6.89) exhibited better performances on the prediction of mushroom MC than those models developed using the TH-HSI data (Rp2 of 0.868, RMSEP of 10.69 %, and RPDp of 2.75). Specifically, the 1D-CNN model based on the raw LED-HSI data (Rp2 of 0.972, RMSEP of 4.70 % and RPDp of 6.29) and the SVMR model based on multiplicative scatter correction (MSC) pretreated LED-HSI data (Rp2 of 0.977, RMSEP of 4.27 %, and RPDp of 6.89) achieved exceptional predictive accuracy for mushroom MC. This finding highlights the effectiveness of the 1D-CNN model in the analysis of HSI data, which performed similarly to the SVMR model without requiring complex data preprocessing steps. In addition, the feasibility of employing a novel LED illumination system in conjunction with a portable HSI camera for the precise MC monitoring of button mushrooms was demonstrated in the present work. |
| format | Article |
| id | doaj-art-9d134371cafd4d4bac417083e9b5bd4b |
| institution | DOAJ |
| issn | 2772-3755 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Smart Agricultural Technology |
| spelling | doaj-art-9d134371cafd4d4bac417083e9b5bd4b2025-08-20T02:55:45ZengElsevierSmart Agricultural Technology2772-37552025-03-011010074710.1016/j.atech.2024.100747Deep learning driven methodology for the prediction of mushroom moisture content using a novel LED-based portable hyperspectral imaging systemKai Yang0Ming Zhao1Dimitrios Argyropoulos2Corresponding author.; UCD School of Biosystems and Food Engineering, University College Dublin, Belfield, Dublin 4, IrelandUCD School of Biosystems and Food Engineering, University College Dublin, Belfield, Dublin 4, IrelandUCD School of Biosystems and Food Engineering, University College Dublin, Belfield, Dublin 4, IrelandThis study proposes a deep-learning driven methodology for the analysis of mushroom moisture content (MC) datasets acquired using a novel portable hyperspectral imaging (HSI) system. One-dimensional convolutional neural network (1D-CNN) was developed and validated to process the raw HSI data of white button mushrooms (Agaricus bisporus) for MC prediction. For comparison purposes, state-of-the-art machine learning algorithms, i.e., support vector machine regression (SVMR) and partial least squares regression (PLSR) were also investigated for the model development based on five spectra pre-processed methods using two different lighting systems i.e., enhanced light-emitting diode (LED) and tungsten halogen (TH). Overall, the predictive models based on the HSI data acquired using the LED lights (Rp2 of 0.977, RMSEP of 4.27 %, and RPDp of 6.89) exhibited better performances on the prediction of mushroom MC than those models developed using the TH-HSI data (Rp2 of 0.868, RMSEP of 10.69 %, and RPDp of 2.75). Specifically, the 1D-CNN model based on the raw LED-HSI data (Rp2 of 0.972, RMSEP of 4.70 % and RPDp of 6.29) and the SVMR model based on multiplicative scatter correction (MSC) pretreated LED-HSI data (Rp2 of 0.977, RMSEP of 4.27 %, and RPDp of 6.89) achieved exceptional predictive accuracy for mushroom MC. This finding highlights the effectiveness of the 1D-CNN model in the analysis of HSI data, which performed similarly to the SVMR model without requiring complex data preprocessing steps. In addition, the feasibility of employing a novel LED illumination system in conjunction with a portable HSI camera for the precise MC monitoring of button mushrooms was demonstrated in the present work.http://www.sciencedirect.com/science/article/pii/S2772375524003514Portable hyperspectral imagingLED lighting systemMushroom moisture contentConvolutional neural networkSupport vector machine regression |
| spellingShingle | Kai Yang Ming Zhao Dimitrios Argyropoulos Deep learning driven methodology for the prediction of mushroom moisture content using a novel LED-based portable hyperspectral imaging system Smart Agricultural Technology Portable hyperspectral imaging LED lighting system Mushroom moisture content Convolutional neural network Support vector machine regression |
| title | Deep learning driven methodology for the prediction of mushroom moisture content using a novel LED-based portable hyperspectral imaging system |
| title_full | Deep learning driven methodology for the prediction of mushroom moisture content using a novel LED-based portable hyperspectral imaging system |
| title_fullStr | Deep learning driven methodology for the prediction of mushroom moisture content using a novel LED-based portable hyperspectral imaging system |
| title_full_unstemmed | Deep learning driven methodology for the prediction of mushroom moisture content using a novel LED-based portable hyperspectral imaging system |
| title_short | Deep learning driven methodology for the prediction of mushroom moisture content using a novel LED-based portable hyperspectral imaging system |
| title_sort | deep learning driven methodology for the prediction of mushroom moisture content using a novel led based portable hyperspectral imaging system |
| topic | Portable hyperspectral imaging LED lighting system Mushroom moisture content Convolutional neural network Support vector machine regression |
| url | http://www.sciencedirect.com/science/article/pii/S2772375524003514 |
| work_keys_str_mv | AT kaiyang deeplearningdrivenmethodologyforthepredictionofmushroommoisturecontentusinganovelledbasedportablehyperspectralimagingsystem AT mingzhao deeplearningdrivenmethodologyforthepredictionofmushroommoisturecontentusinganovelledbasedportablehyperspectralimagingsystem AT dimitriosargyropoulos deeplearningdrivenmethodologyforthepredictionofmushroommoisturecontentusinganovelledbasedportablehyperspectralimagingsystem |