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 segmentati

SUBMITTER: Zhang P 

PROVIDER: S-EPMC11654911 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature