ArticleBMC plant biology2026
LKCAFormer: a lightweight transformer with large-kernel cooperative attention for the segmentation of field maize leaf diseases.
Article in BMC plant biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
In smart agriculture, accurate segmentation of maize-leaf diseases in real field imagery supports timely intervention, but remains challenging under cluttered backgrounds, uneven illumination, occlusion, and diverse lesion morphology. We present LKCAFormer, a lightweight encoder–decoder segmentation network that integrates two key components: (i) a three-stage Large-Kernel Cooperative Attention encoder (LK-COAT) that progressively enlarges the effective receptive field via large-kernel depthwise convolutions while preserving fine boundaries using cooperative channel–spatial gating; and (ii) a cross-scale decoder (CSDecoder) that fuses shallow edge/detail cues with deep semantics to refine lesion boundaries at low computational cost. We evaluate LKCAFormer on CD&S and a controlled single-leaf variant derived from it (Single-CD&S), using disease IoU as the primary endpoint. Robustness is further assessed on a 266-image complex-case subset curated from held-out test data, together with paired two-sided tests. On Single-CD&S, LKCAFormer achieves 76.23 ± 2.25 disease IoU and 86.70 ± 1.96 Dice, yielding a modest + 0.58 IoU gain over the strongest lightweight baseline (SwiftFormer). On the more challenging CD&S benchmark, LKCAFormer reaches 69.09 ± 1.65 disease IoU and 78.87 ± 2.13 Dice, outperforming the strongest baseline (SegFormer) by + 4.05 IoU; gains on the complex-case subset are statistically significant. LKCAFormer remains compact (3.68 M parameters; 1.13G FLOPs), corresponding to approximately 12.7% of U-Net’s parameters and 1.47% of its FLOPs, while retaining practical end-to-end throughput under a unified profiling protocol. Limitations include fixed dataset splits, the lack of cross-device latency/energy benchmarking, and the absence of multi-seed variability analysis. Future work will extend validation across crops and sensors and provide deployment-oriented, hardware-aware latency and energy evaluations.
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