Data

Python and MATLAB script used for analysing (regression and classification) the data extracted from hyperspectral images of wheat grain samples infected by Fusarium graminearum

Queensland Department of Primary Industries
Tahmasbian, Iman ; Wang, Jing
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ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=info:doi10.60699/qvsp-0w71&rft.title=Python and MATLAB script used for analysing (regression and classification) the data extracted from hyperspectral images of wheat grain samples infected by Fusarium graminearum&rft.identifier=10.60699/qvsp-0w71&rft.publisher=Queensland Department of Primary Industries&rft.description=Python and MATLAB scripts were utilised to analyse hyperspectral imaging data from wheat grain samples, which were extracted and organised into Excel spreadsheets. The Python script was employed for classifying wheat grains using Support Vector Machine (SVC) for differentiating between levels of DON concentrations, whilst the MATLAB script was used for regression modelling with Artificial Neural Networks (ANN) for quantifying DON concentrations. Both script work with Excel spreadsheets.The following libraries are required to run the python script: pandas; numpy ; sklearn; matplotlibThere are three MATLAB script including one for combined VNIR and SWIR data and one for each dataset separately. The training function of the ANN regression model is trainbr, the transfer function is radbas. MATLAB machine learning and deep learning library is required for using the scriptPython and MATLAB scripts were utilised to analyse hyperspectral imaging data from wheat grain samples, which were extracted and organised into Excel spreadsheets. The Python script was employed for classifying wheat grains using Support Vector Machine (SVC) for differentiating between levels of DON concentrations, whilst the MATLAB script was used for regression modelling with Artificial Neural Networks (ANN) for quantifying DON concentrations.&rft.creator=Tahmasbian, Iman &rft.creator=Wang, Jing &rft.date=2025&rft.coverage=&rft_rights=AUSGoal Restrictive&rft_subject=Wheat&rft_subject=Mycotoxin&rft_subject=Fusarium head blight&rft_subject=Deoxynivalenol&rft_subject=DON&rft_subject=Hyperspectral imaging&rft_subject=Non-destructive analysis&rft_subject=Machine learning&rft_subject=Grain quality&rft_subject=Australia&rft_subject=Agronomy&rft_subject=real-time analysis&rft_subject=Fungal infection&rft_subject=Python&rft_subject=MATLAB&rft_subject=Fusarium graminearum&rft_subject=Eutiarosporella&rft_subject=White grain &rft_subject=FoR:300409 Crop and pasture protection (incl. pests, diseases and weeds)&rft_subject=FoR:300499 Crop and pasture production not elsewhere classified&rft_subject=Research. Experimentation&rft_place=Queensland Australia&rft.type=dataset&rft.language=English Access the data
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Brief description

Python and MATLAB scripts were utilised to analyse hyperspectral imaging data from wheat grain samples, which were extracted and organised into Excel spreadsheets. The Python script was employed for classifying wheat grains using Support Vector Machine (SVC) for differentiating between levels of DON concentrations, whilst the MATLAB script was used for regression modelling with Artificial Neural Networks (ANN) for quantifying DON concentrations.

Full description

Python and MATLAB scripts were utilised to analyse hyperspectral imaging data from wheat grain samples, which were extracted and organised into Excel spreadsheets. The Python script was employed for classifying wheat grains using Support Vector Machine (SVC) for differentiating between levels of DON concentrations, whilst the MATLAB script was used for regression modelling with Artificial Neural Networks (ANN) for quantifying DON concentrations.

Both script work with Excel spreadsheets.
The following libraries are required to run the python script: pandas; numpy ; sklearn; matplotlib
There are three MATLAB script including one for combined VNIR and SWIR data and one for each dataset separately. The training function of the ANN regression model is "trainbr", the transfer function is "radbas". MATLAB machine learning and deep learning library is required for using the script

Issued: 28 08 2025

Data time period: 2024 to 2024

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Subjects
Agronomy | Australia | DON | Deoxynivalenol | Eutiarosporella | FoR:300409 Crop and pasture protection (incl. pests, diseases and weeds) | FoR:300499 Crop and pasture production not elsewhere classified | Fungal infection | Fusarium graminearum | Fusarium head blight | Grain quality | Hyperspectral imaging | MATLAB | Machine learning | Mycotoxin | Non-destructive analysis | Python | Research. Experimentation | Wheat | White grain | real-time analysis |

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