Yuntao Ma

Introduction: Accurate extraction of maize seedling phenotypes is essential for early growth assessment, yet field-grown seedlings exhibit complex backgrounds, non-rigid leaf deformation, self-occlusion, and adhered leaves that challenge three-dimensional point-cloud analysis. Methods: Here, we developed a workflow integrating PMSPA-Net, an enhanced PointNet++ model, with geometric region decomposition for semantic and organ-level segmentation of field-grown maize seedlings. Multi-view RGB images of 65 complete plants were reconstructed with OpenMVS, and all data were partitioned at the whole-plant level before point-block sampling. Fifteen plants were retained as a fixed independent test set, while the remaining samples were used for training and validation across three predefined random seeds. Results: Under a common protocol, PMSPA-Net outperformed PointNet, PointNet++, DGCNN, and RandLA-Net, achieving an mIoU of 0.8286 ± 0.0149, a macro-F1 of 0.8981 ± 0.0238, and a balanced accuracy of 0.9561 ± 0.0223. A four-configuration ablation study showed complementary gains from the multi-scale spatial pyramid attention module and IOA-based hyperparameter optimization. Geometric region decomposition also achieved more accurate and complete adhered-leaf separation than DBSCAN in three annotated cases. Point-cloud-derived leaf length and width agreed well with manual measurements. Discussion: This workflow demonstrates the feasibility of integrated semantic and organ-level phenotyping under the evaluated field conditions; validation across additional sites, developmental stages, and acquisition conditions is required before broader generalization.

Forest ecosystems play a pivotal role in maintaining the balance of the global carbon cycle and conserving biodiversity. High-density point clouds derived from unmanned aerial vehicle (UAV) structure from motion (SfM) and multi-view stereo (MVS) technologies offer a cost-effective solution for data acquisition. These technologies have become efficient tools for facilitating precision forest resource management and extracting individual tree structural parameters. However, in complex forest scenarios during the leaf-off season, canopies exhibit unstructured branch network morphologies due to the absence of leaf occlusion, and adjacent crowns are heavily interlaced. Consequently, existing segmentation methods struggle to overcome challenges associated with fuzzy boundaries and instance adhesion. To address these challenges, this study proposes TreeSeg-Net, an end-to-end instance segmentation network designed to precisely separate individual trees directly from raw point clouds. The network incorporates a global context attention module (GCAM) to capture long-range feature dependencies, thereby compensating for the limitations of sparse convolution in perceiving global information. Simultaneously, a spatial proximity weighting module (SPWM) is designed. By introducing geometric center constraints and a distance penalty mechanism, this module effectively mitigates under-segmentation issues caused by the feature similarity of adjacent branches in high-canopy-density environments. Experimental results demonstrate that TreeSeg-Net achieves an average precision (AP) of 97.2% in instance segmentation tasks and a mean intersection over union (mIoU) of 99.7% in semantic segmentation tasks. Compared to mainstream networks, the proposed method exhibits superior segmentation accuracy, providing an efficient and automated technical solution for precise resource inventory in complex forest environments.

UAV remote sensing is widely used in the agricultural sector due to its non-destructive, rapid, and cost-effective advantages. This study utilized two years of field data with multisource fused imagery of soybeans to evaluate lodging conditions and investigate the impact of lodging grade information on yield prediction. Unlike traditional approaches that build empirical lodging models using band reflectance, vegetation indices, and texture features, this research introduces a transfer learning framework. This framework employs a ResNet18 encoder to directly extract features from raw images, bypassing the complexity of manual feature extraction processes. To address the imbalance in the lodging dataset, the Synthetic Minority Over-sampling Technique (SMOTE) strategy was employed in the feature space to balance the training set. The findings reveal that deep learning effectively extracts meaningful features from UAV imagery, outperforming traditional methods in lodging grade classification across all growth stages. On the 65 days after emergence (DAE), lodging grade classification using ResNet18 features achieved the highest accuracy (Accuracy = 0.76, recall = 0.76, F1 score = 0.73), significantly exceeding the performance of traditional methods. However, classification accuracy was relatively low in plots with higher lodging grades (lodging grades = 3, 5, 7), with an accuracy of 0.42 and an F1 score of 0.56. After applying the SMOTE module to balance the samples, the classification accuracy in plots with higher lodging grades improved to 0.65, marking an increase of 54.76%. To improve accuracy in yield prediction, this study integrates lodging information with other features, such as canopy spectral reflectance, vegetation indices, and texture features, using two multimodal data fusion strategies: input-level fusion (ResNet-EF) and intermediate-level fusion (ResNet-MF). The findings reveal that the intermediate-level fusion strategy consistently outperforms input-level fusion in yield prediction accuracy across all growth stages. Specifically, the intermediate-level fusion model incorporating measured lodging grade information achieved the highest prediction accuracy on the 85 DAE (R² = 0.65, RMSE = 529.56 kg/ha). Furthermore, when predicted lodging information was used, the model’s performance remained comparable to that of the measured lodging grades, underscoring the critical role of lodging factors in enhancing yield estimation accuracy.