Crop classification with UAV multispectral remote sensing, employing an enhanced ResNet50 residual network.

ABSTRACT: The ResNet50 residual network model is characterized by its rapid training speed and high classification accuracy, demonstrating significant advantages in extracting and classifying crop features. However, the correlation and redundant information among different spectral bands in multispectral data can negatively affect classification accuracy. Additionally, the ResNet50 model does not meet the requirements for training with multi-band data, and its classification accuracy in small-scale, high-precision scenarios needs further improvement. This study focuses on flue-cured tobacco and maize to address these challenges, identifying the optimal classification band combination. Enhancements to the ResNet50 model were made by incorporating Batch Normalization (BN) layers, pyramid pooling layer

SUBMITTER: Xu C 

PROVIDER: S-EPMC12752950 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature