Hyperspectral remote sensing to assess weed competitiveness in maize farmland ecosystems

Abstract

Weed competition causes serious economic losses to maize production. Timely and accurate assessment of pressure from competition is crucial for ecological weed management. In this work, we apply hyperspectral remote sensing (HRS) technology to conduct a competitive experiment in Harbin, China, in 2021, with 5-leaf maize as the study target. A weed competition assessment method that combines comprehensive competition indices (CCI) and deep learning is proposed. For the comprehensive competition assessment, the relationship between different weed competitive pressures (Levels 1–5) and changes in the structural and physiological information of maize was analyzed. The accumulative/transient competition indices CCI-A and CCI-T were designed for accurate quantification. The results showed that parameters such as plant height, stalk thickness and nutrient elements of maize decreased with increasing competition level. Parameters, such as stomatal conductance and transpiration rate, showed a fluctuating change of increasing and then decreasing with increasing competition level. Compared with the traditional relative competitive intensity (RCI), the standard deviation of CCI is 0.303 and 0.499. The dispersion effect of CCI is better and more suitable for quantifying the competition response. HRS images combined with 3D-CNN model were then applied to reveal the spectral response to different weed competition pressures (Levels 1–5) and to make early predictions of weed competition. The first-order derivative showed that the spectral reflectance exhibited significant differences at 520–525 nm peak, 570–655 nm trough, and near 700 nm red edge. For hyperspectral spatial-spectral features, the 3D-CNN model is proposed for prediction of competing indices CCI. In addition, the VIP method is used to select the characteristic wavelengths. The 3D-CNN model achieves a prediction accuracy of RMSE = 0.106 and 0.152 using 13 feature bands, which can accurately quantify the subtle changes in competition indices. Overall, this study shows that the combination of CCI and deep learning can provide a multivariate and comprehensive assessment of weed competition pressure.