针对无人机边缘计算场景下作物病虫害识别的实时性需求与资源约束矛盾,本研究基于TRIZ理论提出系统性解决方案。通过矛盾矩阵与物场模型,解决了高分辨率图像需求与无人机续航冲突、模型轻量化与精度失衡两大核心问题：设计分区域动态采样策略优化航拍能耗,构建轻量化双流网络架构实现边缘端部署,结合多光谱融合与自适应注意力机制,提升小目标特征提取能力。从技术可行性、经济性两方面对提出的13个解决方案进行综合评估,最终得到无人机边缘计算下病虫害识别问题在技术上可行、经济上节约的解决方案,为后续实践研究指出了技术突破的方向。实验表明,模型在自建数据集上平均精度达94.7%,较传统方法提升12.3%,模型可集成于农业无人机平台,为边缘计算场景提供高效解决方案。

Aiming at the contradiction between real-time requirements and resource constraints of crop pest identification in UAV edge computing scenarios, this research proposes a systematic solution based on TRIZ theory. By using the contradiction matrix and object field model, the two core problems of the conflict between high-resolution image requirements and drone endurance, as well as the imbalance between model lightweighting and accuracy, have been solved: designing a dynamic sampling strategy for sub regions to optimize aerial energy consumption, constructing a lightweight dual stream network architecture to achieve edge deployment, and combining multispectral fusion and adaptive attention mechanism to enhance the ability to extract small target features. The 13 proposed solutions are comprehensively evaluated from two aspects of technical feasibility and economy, and finally a technically feasible and economically economical solution for pest identification under UAV edge computing is obtained, which points out the direction of technological breakthrough for subsequent practical research. The experiment shows that the average precision of the model on the self built dataset is 94.7%, 12.3% higher than that of the traditional method. The model can be integrated into the agricultural UAV platform, providing an efficient solution for edge computing scenarios.