MS-Net:一种新的轻量级和精确的模型用于植物疾病识别
Siyu Quan1,2, Jiajia Wang1,2, Zhenhong Jia1,2
1School of Computer Science and Technology, Xinjiang University, Urumqi, China.
Frontiers in plant science
|November 15, 2023
概括
一个新的轻量级深度学习模型显著提高了植物疾病识别的准确性. 这种高效的模型需要更少的参数和更少的计算能力,因此非常适合在农业中部署移动设备.
科学领域:
- 计算机视觉 计算机视觉
- 农业技术 农业技术
- 机器学习 机器学习
背景情况:
- 深度学习是植物疾病识别的主要方法,由图像处理和计算能力的进步推动.
- 增加神经网络深度可以提高性能,但会增加计算复杂性,阻碍移动部署.
- 现有的模型往往需要大量的资源,这限制了它们在边缘设备上的实际应用.
研究的目的:
- 开发一种新的,轻量级的卷积神经网络,以高效准确地检测植物疾病.
- 增强特征表示和优化模型参数,以提高分类准确度.
- 创建一个强大的模型,适合在具有有限计算资源的移动设备上部署.
主要方法:
- 通过将跳过连接集成到MobileNetV3.3中,设计了一个轻量级的卷积神经网络.
- 采用了改进的鱼优化算法来优化跳过连接中的特征融合重量.
- 使用偏差损失,而不是交叉损失,以减轻冗余数据的干扰.
- 该模型在植物分类数据集上进行了预训练,以提高性能和稳定性.
主要成果:
- 拟议的模型在PlantVillage数据集上实现了99.8%的准确性,参数显著减少.
- 它在复杂的户外背景的果叶病数据集 (97.8%准确率) 上表现出强的性能.
- 与现有的先进植物疾病诊断模型相比,该模型的复杂性较低,识别准确度更高.
结论:
- 开发的轻量级卷积神经网络为植物疾病检测提供了卓越的准确性,效率和减少复杂性的平衡.
- 该模型的设计,包括优化的跳过连接和偏差损失,有效地解决了资源密集型深度学习方法的局限性.
- 这项研究提供了一个有前途的解决方案,用于在现实农业环境中在移动和边缘设备上部署先进的植物疾病识别系统.
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