I-GhostNetV3:一个轻量级的深度学习框架,用于以视觉传感器为基础的智能农业大米叶病检测
Puyu Zhang1, Rui Li1, Yuxuan Liu2
1College of Electronic Information Engineering, Guangdong University of Petrochemical Technology, Maoming 525000, China.
Sensors (Basel, Switzerland)
|February 13, 2026
概括
这项研究介绍了I-GhostNetV3,一种高效的AI模型,用于使用计算机视觉识别叶疾病. 它在复杂的现场条件下实现高精度,帮助智能农业应用.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 农业技术 农业技术
背景情况:
- 准确的叶疾病诊断对于智能农业至关重要.
- 轻量级卷积神经网络 (CNN) 在复杂的现场环境中面临挑战,原因是病变,背景杂乱和照明变化.
研究的目的:
- 开发一个改进的轻量级CNN,I-GhostNetV3,用于RGB叶疾病识别.
- 为了增强病变的表现和抑制背景干扰,以便更强大的疾病识别.
主要方法:
- I-GhostNetV3 结合了适应式并行注意 (APA) 来增强损伤特征和融合坐标通道注意 (FCCA) 来进行背景抑制.
- 该模型基于GhostNetV3,具有用于可控计算开销的模块化增强功能.
主要成果:
- 在米叶细菌和真菌病 (RLBF) 数据集上,I-GhostNetV3实现了90.02%的Top-1准确度.
- 该模型有1.831万个参数和248.694万个FLOP,性能优于MobileNetV2和EfficientNet-B0.0.
- 性能在PlantVillage-Corn数据集上被验证为转移学习检查.
结论:
- I-GhostNetV3在识别叶疾病方面表现出高效率和准确性.
- 该模型显示,作为精密农业边缘部署的高效支柱,该模型具有前景.
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