Estimation of Leaf Chlorophyll Content of Maize from Hyperspectral Data Using E2D-COS Feature Selection, Deep Neural Network, and Transfer Learning

Leaf chlorophyll content (LCC) serves as a vital biochemical indicator of photosynthetic activity and nitrogen status, critical for precision agriculture to optimize crop management. While UAV-based hyperspectral sensing offers maize LCC estimation potential, current methods struggle with overlapping spectral bands and suboptimal model accuracy. To address these limitations, we proposed an integrated maize LCC estimation framework combining UAV hyperspectral imagery, simulated hyperspectral data, E2D-COS feature selection, deep neural network (DNN), and transfer learning (TL). The E2D-COS algorithm with simulated data was used to identify structure-resistant spectral bands strongly correlated with maize LCC: Big trumpet stage: 418 nm, 453 nm, 506 nm, 587 nm, 640 nm, 688 nm, and 767 nm; Spinning stage: 418 nm, 453 nm, 541 nm, 559 nm, 688 nm, 723 nm, and 767 nm. Combining the E2D-COS feature selection with TL and DNN significantly improves the estimation accuracy: the R 2 of the proposed Maize-LCNet model is improved by 0.06–0.11 and the RMSE is reduced by 0.57–1.06 g/cm compared with LCNet-field. Compared to the existing studies, this study not only clarifies the spectral bands that are able to estimate maize chlorophyll, but also presents a high-performance, lightweight (fewer input) approach to achieve the accurate estimation of LCC in maize, which can directly support growth monitoring nutrient management at specific growth stages, thus contributing to smart agricultural practices.