AAUConvNeXt: Enhancing Crop Lodging Segmentation with Optimized Deep Learning Architectures

Abstract

Rice lodging, a phenomenon precipitated by environmental factors or crop characteristics, presents a substantial challenge in agricultural production, notably impacting yield prediction and disaster assessment. Despite that the application of conventional methodologies like visual assessment, mathematical models, and satellite remote sensing technologies has been employed in the segmentation of crop lodging, these approaches are still constrained in precision, immediacy, and capacity for large-scale evaluation. This study introduces an innovative convolutional neural network architecture, AFOA + APOM + UConvNeXt, that integrates intelligent optimization algorithms for automatic selection of optimal network parameters, thereby enhancing the accuracy and efficiency of crop lodging segmentation. The proposed model, empirically validated, outperforms recent state-of-the-art models in crop lodging segmentation, demonstrating higher accuracy, lower computational resource requirements, and greater efficiency, thereby markedly reducing the cost of segmentation. In addition, we investigated the segmentation on half lodging rice, and the results indicate that the model exhibits commendable performance on the half lodging dataset. This outcome holds significant implications for the prediction of rice lodging trends. The fusion of deep learning with intelligent optimization algorithms in this study offers a new effective tool for crop lodging monitoring in agricultural production, providing strong technical support for accurate crop phenotypic information extraction, and is expected to play a significant role in agricultural production practices.