Concealed Object Detection (COD) aims to identify objects visually embedded in their background. Existing COD datasets and methods predominantly focus on animals or humans, ignoring the agricultural domain, which often contains numerous, small, and concealed crops with severe occlusions. In this paper, we introduce Concealed Crop Detection (CCD), which extends classic COD to agricultural domains. Experimental study shows that unimodal data provides insufficient information for CCD. To address this gap, we first collect a large-scale RGB-D dataset, ACOD-12K, containing high-resolution crop images and depth maps. Then, we propose a foundational framework named Recurrent Iterative Segmentation Network (RISNet). To tackle the challenge of dense objects, we employ multi-scale receptive fields to capture objects of varying sizes, thus enhancing the detection performance for dense objects. By fusing depth features, our method can acquire spatial information about concealed objects to mitigate disturbances caused by intricate backgrounds and occlusions. Furthermore, our model adopts a multi-stage iterative approach, using predictions from each stage as gate attention to reinforce position information, thereby improving the detection accuracy for small objects. Extensive experimental results demonstrate that our RISNet achieves new state-of-the-art performance on both newly proposed CCD and classic COD tasks. All resources will be available at https://github.com/Kki2Eve/RISNet.

Unmanned aerial vehicle (UAV) hyperspectral imaging offers an efficient and cost-effective way to map tree species at the individual tree levels. Conventional methods mostly rely on large samples of natural RGB images of tree crowns, lacking the ability to distinguish species, particularly for trees with overlapping crowns. This study proposed a novel scale pyramid graph network (SPGN) for instance segmentation that can simultaneously apply pixel-level (node) classification for discriminating species and edge prediction for delineating individual trees. Based on a graph-in-graph (GiG) convolution, we built a scale pyramid module (SPM) that extracts multiscale features at pixels, superpixels, and subgraph levels to aggregate the over-segmented superpixels into the same species and the same tree. We also proposed an innovative concept of subgraph positional encoding (SPE) to represent the natural spatial relationship of graph-structured data. The SPGN method was evaluated in a case study involving eleven subtropical broadleaf species under an urban environment in south China. The accuracy of species classification achieved 93%, and the area under the curve (AUC) of individual tree segmentation reached 0.96. Compared with state-of-the-art methods such as DeepForest, Detectree2, and segment anything model (SAM), SPGN presented fewer errors in tree detection and outperformed in instances of crown overlaps. Ablation studies proved the effectiveness of SPM and SPE modules, which improved segmentation by 10% and classification by 7% in accuracy, respectively. The findings confirm the benefits of incorporating spatial context, such as crown textures and tree positional relationships, for species differentiation; in return, accurate species identification combined with spectral information assists the individual tree segmentation. This effective strategy can be potentially extended to a broader range of regions and forest types.