Leveraging machine learning to discriminate wheat scab infection levels through hyperspectral reflectance and feature selection methods

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Real-time or pre-symptomatic wheat scab (WS) detection is inevitable for precision agriculture to secure yield and quality at the critical grain formation stage. For this, feature selection (FS) techniques and machine learning (ML) have demonstrated their capabilities. However, for the same type and size of dataset, all FS and ML techniques behave differently due to their diverse primary constituents. This study attempts to leverage ML for WS classification and prediction employing different FS techniques on hyperspectral data of wheat spikes. The spectral features were selected and assessed to regress and classify disease occurrence. Relief-F-neural net (NN) manifested the best results with classification accuracy (CA) of 67 % and 89 % at the pre-symptomatic scale and 3 days after inoculation (DAI), respectively. Followed by continuous wavelet transform (CWT)-NN with 63 % CA at the pre-symptomatic scale and CWT-Xgboost with 89 % CA at 3DAI. For prediction, random forest regression revealed best accuracy of R2 = 0.94 and RMSE = 7.70, followed by partial least squares regression with R2 = 0.90 and RMSE = 10.37. The results offer a precise quantitative benchmark for future investigations into the capacity of hyperspectral data and FS for the real-time quantification of plant diseases.