The stakes are higher than they might sound. Lodging, the permanent bending or displacement of wheat stems from their upright position, can substantially reduce grain yield, decrease harvesting efficiency, and increase uncertainty in field management. Timely and accurate monitoring of lodging areas is essential for disaster assessment, yield loss estimation, and management optimization. Unmanned aerial vehicles equipped with RGB cameras have become the workhorse for this task, capturing high-resolution orthomosaic maps of entire fields that deep neural networks can scan for lodged regions. But most existing studies evaluate their models using images collected from a single acquisition date, a specific growth stage, or randomly divided datasets. That approach implicitly assumes training and testing samples follow similar distributions, an assumption the real world routinely violates.

The research team, led by Gan Zhang of the Anhui Academy of Agricultural Sciences, conducted field experiments at the Baihu Wheat Comprehensive Experimental Station in Lujiang County, Hefei City, in China’s main wheat-producing region. The subtropical monsoon climate, with humid springs and frequent rainfall during the reproductive period, favors wheat lodging, making it an ideal natural laboratory. The team surveyed experimental fields covering approximately 4.2 hectares planted with winter wheat varieties including Yangmai 8, Yangmai 13, and Ningmai 13. UAV lodging-monitoring campaigns were conducted in 2019, 2020, 2021, 2023, and 2024, covering anthesis, grain filling, and maturity stages. Two drone platforms were used: the DJI Phantom 4 Pro for the earlier years and the DJI Mavic 3E for 2023 and 2024, both acquiring RGB imagery but differing in sensor size and focal length, which introduced realistic sensor-related variation into the multi-year dataset.

Raw images were processed using DJI Terra to generate georeferenced orthomosaic maps, yielding seventeen orthomosaics grouped by acquisition year. Lodged wheat regions were manually annotated using Labelme software, producing binary segmentation masks where lodged areas were the target class and non-lodged areas the background. The orthomosaics and masks were cropped into 256-by-256-pixel image tiles, invalid samples removed, and the remaining tiles became the basic units for training, inference, and area-error statistics. The 2023 dataset, containing the largest number of tiles at 9,761 and covering grain-filling and maturity stages, served as the source domain, while 2019, 2020, 2021, and 2024 were treated as temporal target domains. Lodging area ratios ranged from 4.49 percent in 2019 to 44.59 percent in 2024, a more than ninefold difference that alone would challenge any model.

To quantify how much the appearance of wheat fields changed across years, the team calculated Bhattacharyya distances between class-wise grayscale histogram distributions of lodged and lodging-free pixels in the source and target years. The 2019 dataset showed the largest appearance discrepancy, with a lodging-region Bhattacharyya distance of 0.8968, while 2024 showed the smallest at 0.0417. Complementary metrics captured other dimensions of shift: frozen ResNet50 features revealed relatively high maximum mean discrepancy in 2021 and 2019, while the Wasserstein distance of tile-level lodging proportions was largest in 2024 at 0.311, indicating that 2024 was visually closer to the source domain but still differed substantially in label distribution. No single metric could summarize target-year difficulty, a nuance the authors emphasize throughout.

Nineteen representative semantic segmentation models from six architecture families were evaluated, spanning Transformer-based architectures, MLP-decoder-based networks, and fully convolutional methods, including Swin Transformer and SegFormer variants, U-Net models, UPerNet models with different ResNet backbones, DeepLabv3+, and PSPNet. All models were trained on the 2023 source-domain training subset using the MMSegmentation framework based on PyTorch, with AdamW optimization, a learning rate of 6 times 10 to the negative fifth, weight decay of 0.01, batch size 2, and 100 epochs. A model passed convergence only when the Dice coefficients and class-wise accuracies of both lodged and non-lodged classes reached predefined thresholds and the Gap between overall and mean class accuracy remained acceptable. Nine models passed, including four Swin Transformer models, three SegFormer models, U-Net+FCN, and UPerNet-R50. Several CNN-based models showed severe class bias, identifying lodging-free regions with high accuracy while poorly detecting lodged regions, reflecting the challenge of class imbalance in UAV lodging datasets.

The eight baseline models that passed both source-domain convergence and within-year generalization were then directly applied to prediction subsets of the target-year datasets without additional training, a zero-shot test of temporal generalization. All experienced performance degradation, with the largest decline in 2019. Swin-L, the largest Swin Transformer model with 237.57 million parameters, achieved 82.46 percent lodging Dice on 2024 but only 31.40 percent on 2019, with lodging recall dropping from 73.98 percent to 38.10 percent. UPerNet-R50 fared worse, achieving just 12.99 percent lodging Dice on 2019. The cross-year performance trend broadly matched the domain-shift results, with 2019, which had the largest Bhattacharyya distance, showing the most severe degradation and 2024, with the smallest, showing the best direct transfer. When Swin-L was instead trained with 2024 as the source domain, 2019 remained the hardest target year, confirming the ranking was not an artifact of source-year choice.

Target-domain fine-tuning with limited newly annotated samples offered a partial rescue. Using 5, 10, and 20 percent target-year settings, independently sampled and split into training and validation subsets at a 3:2 ratio, the team evaluated adaptation performance. On the 2024 prediction subset with 20 percent fine-tuning, Swin-L achieved 87.05 percent lodging Dice, 89.92 percent lodging-free Dice, 84.37 percent lodging recall, and 92.20 percent lodging-free recall, indicating high and balanced segmentation. On the 2019 prediction subset with the same setting, Swin-L reached 70.47 percent lodging Dice and 68.91 percent lodging recall, a substantial improvement over direct deployment but still below 2024 performance. Three independent runs under the 20 percent setting revealed that stability varied with target-year difficulty: Swin-L’s mean micro lodging Dice was 0.882 with a standard deviation of 0.010 in 2024, versus 0.614 with a standard deviation of 0.099 in 2019, where boundary errors, missed lodged regions, and noise responses persisted even after fine-tuning.

The authors also tested two unsupervised pre-adaptation baselines for 2019 that required no target-year segmentation labels. Fourier domain adaptation, which replaces low-frequency amplitude statistics of test images with those of the training domain while retaining phase information, provided only limited gains, raising Swin-B’s lodging F1 from 34.26 to 35.74 percent. Standard CycleGAN, which learns an unpaired image-to-image translation using adversarial and cycle-consistency losses, actually reduced performance to 29.21 percent, suggesting that task-unconstrained style transfer can damage lodging-boundary and texture cues. A class-weighted sensitivity analysis for Swin-L in the 2019 20 percent setting showed that adding cross-entropy class weights increased micro Dice from 0.509 to 0.619 and recall from 0.389 to 0.753, but reduced precision from 0.735 to 0.526, illustrating the precision-recall trade-off inherent in addressing class imbalance.

The implications reach beyond wheat. The study demonstrates that high within-year accuracy cannot guarantee cross-year reliability, and that temporal generalization testing should be regarded as an essential step before deploying agricultural AI models in real production environments. Architecture clearly affected temporal transferability, with attention-based models generally showing stronger robustness than fully convolutional networks, likely because hierarchical window-based self-attention can integrate longer-range context while convolution emphasizes local patterns. But even the best architecture suffered severe degradation under large temporal shift, showing that stronger representation alone is insufficient. The authors note limitations: the dataset came from one experimental area over five years and cannot fully represent regional differences in varieties, management, soil, climate, or field structure, and the study focused on RGB imagery, leaving multispectral, hyperspectral, thermal, or LiDAR data as future directions. Still, the framework offers a practical basis for assessing cross-year reliability, and the message is unambiguous: for long-term UAV-based crop monitoring, evaluating a model only on the year it was trained is no longer good enough.

Subject of Research: Temporal generalization evaluation of deep semantic segmentation models for UAV-based wheat lodging monitoring under transfer learning

Article Title: Temporal generalization evaluation of deep semantic segmentation models for UAV-based wheat lodging monitoring under transfer learning

Article References: Zhang, G., Wang, X., Wang, D., Sun, D., Zhang, Y., & Zhang, D. (2026). Temporal generalization evaluation of deep semantic segmentation models for UAV-based wheat lodging monitoring under transfer learning. Smart Agricultural Technology, 15, Article 102631. https://doi.org/10.1016/j.atech.2026.102631

Image Credits: AI Generated

DOI: Not provided

Keywords: Temporal, generalization, evaluation, deep, semantic, segmentation, models, UAV-based, wheat, lodging, monitoring, under