PND-Net:使用图形卷积网络对植物营养不足和疾病进行分类
Asish Bera1, Debotosh Bhattacharjee2,3, Ondrej Krejcar3,4,5
1Department of Computer Science and Information Systems, BITS Pilani, Pilani Campus, Pilani, Rajasthan, 333031, India. asish.bera@pilani.bits-pilani.ac.in.
Scientific reports
|July 5, 2024
概括
使用深度学习早期发现植物疾病和营养缺乏,可以提高作物产量. 一个新的植物营养不足和疾病网络 (PND-Net) 结合了卷积神经网络 (CNN) 和图形卷积网络 (GNN) 进行准确的分类.
科学领域:
- 农业科学 农业科学
- 计算机科学 计算机科学
- 生物技术是生物技术.
背景情况:
- 植物健康监测对于农业生产率和粮食安全至关重要.
- 早期识别植物疾病和营养不足有助于及时干预和提高产量.
- 深度学习模型在自动化分析植物视觉症状方面表现有前途.
研究的目的:
- 提出一种新的深度学习方法,即植物营养缺乏和疾病网络 (PND-Net),用于准确地分类植物疾病和营养缺乏.
- 通过将区域特征学习与图形卷积网络 (GNN) 集成到卷积神经网络 (CNN) 骨架上来提高分类准确性.
- 评估PND-Net在植物健康和人类医学成像数据集上的概括能力.
主要方法:
- 开发了PND-Net,这是一个混合深度学习模型,结合了CNN用于全球特征提取和GNN用于详细的区域分析.
- 采用空间金字塔聚合用于基于区域的多尺度特征总结,以捕获歧视性特征.
- 对植物营养缺乏 (香,咖啡) 和植物疾病分类 (土豆,PlantDoc) 的公共数据集进行PND-Net评估,使用各种CNN骨干.
主要成果:
- PND-Net实现了高的分类准确率:香的90.00%,咖啡的90.54%,营养缺乏.
- 通过使用Xception骨干实现了土豆疾病的96.18%准确率和PlantDoc数据集的84.30%.
- 在医学成像数据集 (BreakHis,SIPaKMeD) 上展示了最先进的性能,表明了强大的概括性.
结论:
- 拟议的PND-Net有效地改善了自动化植物健康分析,有助于农业增长.
- 混合CNN-GNN方法与区域特征学习增强了植物疾病和缺陷的分类准确性.
- PND-Net显示出各种应用的潜力,包括人类癌症分类,突出其强度.
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