TrIncNet:用于识别植物疾病的轻量级视觉转换器网络
Pushkar Gole1, Punam Bedi1, Sudeep Marwaha2
1Department of Computer Science, University of Delhi, New Delhi, India.
Frontiers in plant science
|August 14, 2023
概括
一个名为TrIncNet的新型轻量级视觉变压器 (ViT) 提高了植物疾病识别的准确性. 该网络是高效的,适合使用物联网设备在现场层面检测疾病.
科学领域:
- 农业技术 农业技术
- 计算机视觉 计算机视觉 计算机视觉
- 机器学习是机器学习.
背景情况:
- 准确的植物疾病鉴定对于作物生产和农民收入至关重要.
- 现有的视觉变压器 (ViT) 是有前途的,但具有计算上昂贵的MLP模块.
- 从患病植物图像中提取特征的低效率限制了当前ViT的性能.
研究的目的:
- 提出一个轻量化和改进的视觉转换器网络,TrIncNet,用于植物疾病的识别.
- 提高特征提取效率,降低与现有ViT相比的计算成本.
- 评估TrIncNet在各种植物疾病数据集上的表现.
主要方法:
- 开发了一种新的TrIncNet架构,其中包括一个Trans-Inception块,取代了标准的MLP模块.
- 在每个Trans-Inception区块内集成跳过连接,以减轻消失梯度的问题.
- 使用PlantVillage和玉米疾病数据集评估TrIncNet的性能.
主要成果:
- TrIncNet的性能优于包括VGG-19,GoogLeNet,ResNet-50,Xception,InceptionV3和MobileNet在内的最先进的卷积神经网络 (CNN) 架构.
- 拟议的TrIncNet在各自的数据集上实现了比现有ViT网络更高的测试准确度,分别为5.38%和2.87%.
- 在从图像中识别植物疾病方面,TrIncNet表现出卓越的性能.
结论:
- 轻量级的TrIncNet为植物疾病识别提供了更好的预测准确性.
- 它的效率和性能使其适合与物联网设备集成,用于实时,现场级疾病检测.
- 在农业中,TrIncNet为早期检测植物疾病的挑战提供了可行的解决方案.
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