Improving chlorophyll content detection to suit maize dynamic growth effects by deep features of hyperspectral data

Real-time leaf chlorophyll content (LCC) is critical for managing farm inputs and monitoring crop growth, productivity and quality of the yield. Visible-near infrared spectroscopy is a non-destructive method for the LCC detection, which plays an increasingly substantial role in the high-throughput monitoring in field. Some detection methods use single or fixed bands, which are not sensitive to LCC at each growth stage and obtain low accuracy and robustness. Thus, we aim to improve the robustness of LCC detection models by exploring deep features of hyperspectral data which could be suitable for the dynamic growth effects. In experiments, the hyperspectral data of four growth stages of jointing, tasseling, silking and blister stages were measured in 2020 and 2021, respectively. Firstly, the LCC variation and spectral response at each growth stage were analyzed. Changes existed at different stages in typical vegetation indices (VIs), which included normalized difference vegetation index (NDVI), normalized difference red edge index (NDRE) and red edge position (REP). Secondly, to capture the features sensitive to LCC at each growth stage, a novel method was proposed to explore deep features hidden among the sensitive wavelengths by combining methods of competitive adaptive reweighted sampling (CARS) and long short-term memory (LSTM), which is labeled as CARS-LSTM. Finally, in order to compare the LCC detection performance of typical VIs and our proposed deep features, the partial least squares regression models were established based on NDVI, NDRE, REP, CARS, LSTM and hybrid deep features of CARS-LSTM, respectively. Result showed that REP performed better than NDVI and NDRE and obtained determination coefficient of prediction set (RP2) and root mean square error of prediction set (RMSEP) with 0.48 and 4.52 mg/L, respectively, which was possibly due to consistence between REP and LCC and the saturation of NDVI. The wavelengths selected by CARS obtained RP2 and RMSEP of 0.71 and 3.32 mg/L, respectively, and achieved better detection results than REP; The RP2 values of each growth stage were in the range of 0.41–0.72 and the RMSEP values were in the range of 1.23–5.40 mg/L. The proposed deep features of CARS-LSTM achieved the best detection results with RP2 of 0.94 and RMSEP of 1.54 mg/L; The RP2 values of each growth stage were in the range of 0.76–0.96 and the RMSEP values were in the range of 0.89–2.52 mg/L. The research demonstrated that the hybrid deep features of CARS-LSTM could capture the complex spectral changes and help improve the relationship between LCC and spectral data. The proposed method can improve the detection accuracy and robustness of LCC to suit the dynamic growth effects and provide guidance for field monitoring and management.