Enhancing fusarium head blight detection in wheat crops using hyperspectral indices and machine learning classifiers

Fusarium head blight (FHB) pathogen jeopardizes the quality and yield of wheat crops at critical grain formation stages. Given these, the capability of near-infrared hyperspectral imaging and non-imaging data was explored to develop wheat fusarium spectral indices (WFSI) and wheat fusarium texture indices (WFTI) on independent three-year datasets using consistently selected wavelet features (WFs) and texture features (TFs). Subsequently, five classifiers – Knn, RF, SVM, NN and Xgboost – were employed to fully explore the selected features and evaluate the newly developed indices for FHB classification accuracy. The frequent measurement results indicated that the biochemical and spectral changes were consistent for whole spike-pathogen interaction with the development of FHB. At the pre-symptomatic scale, the WFSI1, WFSI2, WFTI1, and WFTI2 individually demonstrated the average classification accuracy (ACA) in all classifiers of 78.90 %, 72.0 %, 73.60 %, and 71.0 %, respectively. At the disease scale (DS) two (3–5 % disease prevalence), the ACA of WFSI1, WFSI2, WFTI1, and WFTI2 increased to 90.10 %, 90.30 %, 88.10 %, and 85.40 %, respectively, for imaging data. Furthermore, the fusion of all four developed indices in 2019, 2020, and 2021 showed enhanced ACA of 79.05 %, 76.75 % and 78.59 %, respectively. The ACA of the fused four developed indices in the three years increased to 98.06 %, 89.68 % and 94.83 % at DS2, respectively, and for higher DS (10–100 % disease prevalence) also exhibited higher accuracy. Among all the classifiers, neural net (NN) performed better after Xgboost. The developed indices were also employed and successfully proved on independent datasets acquired from imaging and non-imaging sensors. This work reveals the promising implementation of hyperspectral information in improving FHB early monitoring in precision agriculture applications.