改进了MobileNetV2作物疾病识别模型,用于智能农业
Jianbo Lu1,2, Xiaobin Liu1, Xiaoya Ma2,3
1School of Computer and Information Engineering, Nanning Normal University, Nanning, Guangxi, China.
PeerJ. Computer science
|October 9, 2023
概括
一个改进的MobileNetV2模型提高了作物疾病识别准确性,用于智能农业. 这种轻量级模型提供了更高的精度和更快的速度,使其成为具有有限计算资源的移动和边缘设备的理想选择.
科学领域:
- 农业科学 农业科学
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 智能农业对于高质量的发展至关重要.
- 准确的作物疾病识别至关重要,但在有限的计算资源下具有挑战性.
研究的目的:
- 开发一个轻量级的MobileNetV2模型,用于在移动和边缘设备上准确识别作物疾病.
- 提高模型效率和识别精度.
主要方法:
- 在MobileNetV2.2中使用了改进的瓶结构.
- 嵌入点对点卷积以减少通道和参数.
- 引入了重新参数化的多层感知子 (RepMLP) 模块,以增强功能依赖性.
- 集成了一个高效的通道注意力机制和Hardswish激活功能.
主要成果:
- 在PlantVillage数据集上实现了99.53%的准确性,改善了0.3%.
- 将模型参数减少到0.9M (59%的减少).
- 与原始模型相比,推断速度增加了8.5%.
结论:
- 提议的改进的MobileNetV2模型显著提高了作物疾病识别的准确性和效率.
- 该模型适合在智能农业中的资源有限的移动和边缘设备上部署.
- 这项研究为农业边缘计算应用提供了宝贵的参考.
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