Advances in the study of biochemical, morphological and physiological traits of wheat and sorghum crops in australia using hyperspectral data and machine learning

In this paper, we discuss the integration of systems such as multi-dimensional radiative transfer models (RTM) with deep learning (DL) algorithms to estimate plant biochemical, physiological, and morphological traits at canopy level using high-resolution hyperspectral imagery (361 bands in the 400-1000 nm spectral range). We applied the approaches to two case studies for dryland cropping in Australia (i.e., wheat and sorghum). Crop type averages for the early flight for leaf area index (LAI) varied between 2, for Canola, to as high as 4.3 for Lentils. Wheat and Barley had LAI of 4.1 and 3.8 (m(2)/m(2)), respectively. Chlorophyll a+b (Ca+b) averages for emerged crops were 18, 41, 44, 51 and 59 mu g/cm(2) for Faba beans, Wheat, Canola, Barley and Oats, respectively. The pigment Anthocyanin varied from 4.9 to 15.9 mu g/cm(2) for Lentils and Canola, respectively. Similar patterns were observed in the Carotenoid (Cx+c) levels (as high as 16.5 mu g/cm(2) for Oats). For sorghum plots, the integrated DL approaches showed significant high correlation in predicting sorghum LAI (R-2 = 0.84, RMSE = 0.65 m(2)/m(2)) and Ca+b (R-2 = 0.94, RMSE = 4.94 mu gcm(-2)). The maximum velocity carboxylation rates (Vcmax) varied between 45-75 mu mol m(-2)s(-1). For both studied periods, we yielded a R-2 > 0.78 and RMSE <= 5.35 mu mol m(-2)s(-1), being the RMSE lower when using the modelled fluorescence emission for retrieving the Vcmax. In addition, we derived the solar induced fluorescence emission hyperspectral narrowband (5.8 nm) sensing and radiative transfer models (RTM).