A Task-Oriented Segmentation Algorithm Based on Residual Super-Resolution and Edge-Aware Learning for Real-Time Agricultural Vision

Accurate vegetation-soil segmentation is a key component of intelligent agricultural vision systems operating under limited computational resources. Existing CNN-based approaches generally improve segmentation accuracy by increasing network complexity or relying on high-resolution imagery, limiting their suitability for real-time embedded applications. This paper proposes a lightweight task-oriented CNN segmentation algorithm that integrates residual structural reconstruction, spectral augmentation, semantic segmentation, and edge-aware refinement within a unified multi-task optimization framework. Unlike conventional super-resolution methods, the residual module is specifically designed to recover segmentation-relevant structural features, including vegetation contours, crop-row geometry, and vegetation-soil transitions, without increasing spatial resolution. The algorithm combines four computational stages-spectral augmentation, pseudo low-resolution generation, residual enhancement, and edge-guided refinement-to improve internal feature representations while preserving computational efficiency. Experiments on the Crop Row Benchmark Dataset demonstrate competitive segmentation performance (BF1 = 0.95, mIoU = 0.92) with real-time inference (54.7 FPS). Comparative experiments, ablation studies, computational complexity analysis, and Grad-CAM-based interpretability analysis demonstrate the effectiveness of the proposed algorithm and the complementary contribution of its computational modules. The proposed formulation provides an efficient and interpretable CNN-based solution for resource-constrained agricultural vision systems.