UAV imaging hyperspectral for barnyard identification and spatial distribution in paddy fields

Abstract

Barnyard grass (Echinochloa crus-galli), a noxious weed prevalent in rice fields, poses a significant threat to rice growth and development, ultimately leading to substantial yield reductions. Its morphological resemblance to rice plants complicates its identification and management. Consequently, accurately identifying and mapping barnyard grass in intricate rice field environments holds substantial research value and significance. Furthermore, most existing weed detection studies rely on single-day data, limiting the models’ generalizability. Therefore, incorporating multi-temporal data into modeling is highly desirable. This study utilized an unmanned aerial vehicle (UAV) to capture multi-temporal, low-altitude hyperspectral imagery of paddy fields, which were subsequently stitched, calibrated, and filtered using the SG convolution filter. The continuous projection algorithm (SPA) was employed to extract sensitive bands for discriminating between rice and barnyard grass. Initially, single-time data was utilized for barnyard grass identification, with models such as support vector machine (SVM), random forest (RF), 1D convolutional neural network (1DCNN), and 3D convolutional neural network (3DCNN) constructed using both full-spectrum and feature bands. The results indicated that the SPA-3DCNN model exhibited the highest performance in identifying rice (F1-score: 0.942) and barnyard grass (F1-score: 0.8936). Seven selected feature bands (482.5234, 546.5415, 675.0806, 709.1382, 762.0431, 922.0157, and 944.6371 nm) were found to be highly discriminative for distinguishing barnyard grass from rice. Subsequently, multi-temporal data modeling was employed to enhance the models’ generalization ability, resulting in improved overall accuracy (OA) and Kappa coefficients for each model, along with reduced misclassification rates. The spatial distribution maps also exhibited better performance with increased temporal data. The infestation trend of barnyard grass was visualized using multi-temporal difference maps, and a density map of barnyard grass was generated through image binarization. This study successfully demonstrated the feasibility of utilizing UAV-borne hyperspectral imagery for identifying barnyard grass in complex paddy field environments. By improving the model’s generalization ability through multi-temporal modeling, the study provided robust data support for managing and preventing barnyard grass infestations, including the creation of spatial distribution and density maps for barnyard grass.