轻量级网络实现玉米病害高精度实时识别，适合手机无人机部署。
LWMSCNN-SE: A Lightweight Multi-Scale Network for Efficient Maize Disease Classification on Edge Devices
- 融合多尺度特征与注意力机制的轻量化CNN结构。
- 仅24万参数、0.666 GFLOPs下达到96.63%准确率。
- 专为边缘设备设计，适合田间实时病害诊断。
玉米病害分类对减少产量损失、保障粮食安全至关重要。然而，传统病害检测模型在智能手机和无人机等资源受限环境中的部署面临计算成本高的挑战。为此，本文提出LWMSCNN-SE，一种结合多尺度特征提取、深度可分离卷积和挤压-激励（SE）注意力机制的轻量级卷积神经网络。该模型在仅241,348个参数和0.666 GFLOPs计算量下，实现了96.63%的分类准确率，具备实时部署能力。本方法有效平衡了精度与效率，在精准农业系统中展现出高效玉米病害诊断的潜力。
原文摘要 · Abstract (English)
Maize disease classification plays a vital role in mitigating yield losses and ensuring food security. However, the deployment of traditional disease detection models in resource-constrained environments, such as those using smartphones and drones, faces challenges due to high computational costs. To address these challenges, we propose LWMSCNN-SE, a lightweight convolutional neural network (CNN) that integrates multi-scale feature extraction, depthwise separable convolutions, and squeeze-and-Excitation (SE) attention mechanisms. This novel combination enables the model to achieve 96.63% classification accuracy with only 241,348 parameters and 0.666 GFLOPs, making it suitable for real-time deployment in field applications. Our approach addresses the accuracy--efficiency trade-off by delivering high accuracy while maintaining low computational costs, demonstrating its potential for efficient maize disease diagnosis on edge devices in precision farming systems.
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