Identifying Aquatic Plants in Crab Ponds Based on Spectral Data, RGB Image Fusion, and Deep Learning

Aquatic vegetation is critical for maintaining water quality, dissolved oxygen levels, and suitable habitats for crabs in aquaculture ponds. However, traditional manual surveys for monitoring aquatic plant growth and identifying invasive weeds are inefficient, labor-intensive, and prone to subjective errors. To address these limitations, this study proposes an integrated framework combining unmanned aerial vehicles (UAVs), remote sensing, and deep learning (DL) for pixel-level monitoring of aquatic plants in crab ponds. High-resolution UAV RGB and multispectral (MS) images were collected by a drone in Changxing County, Zhejiang Province, China. A specialized dataset was constructed, including cultivated Elodea canadensis and Hydrilla verticillata and two dominant invasive weed species (Alternanthera philoxeroides and Lemna minor). Next, an improved U2Net was designed to fuse the RGB-MS images and provide more detailed information. We also propose a SAM-based model, PondSAM, which integrates three modules: (1) a multiscale vision transformer encoder, (2) a self-generated prompt module, and (3) a multilayer aggregation decoder. The Pond-SAM results are relatively better than those of other state-of-the-art methods (e.g., SAM), achieving a Dice score of 0.865, an mIoU score of 0.858, and a recall score of 0.907. Additionally, the proposed method has been tested on the dataset with different light intensities and training ratios, and its reliability was verified. This study confirms that UAV remote sensing combined with deep learning provides a rapid, nondestructive, large-scale solution for aquatic plant monitoring and invasive weed identification in crab ponds, supporting precision aquaculture and ecological regulation.