GNViT-使用视觉变压器 (ViT) 模型进行基于图像的增强地虫害分类
Venkatasaichandrakanth P1, Iyapparaja M1
1School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore, Tamilnadu, India.
PloS one
|March 25, 2024
概括
一个新的花生视觉变压器 (GNViT) 模型准确地检测和分类花生害虫. 这种人工智能方法显著改善了害虫识别,有助于减少作物损失和提高粮食安全.
科学领域:
- 农业科学 农业科学
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 由于害虫和疾病造成的作物损失对全球农业构成重大威胁.
- 地瓜作物特别容易受到与害虫有关的损害,影响粮食安全.
研究的目的:
- 引入和评估花生视觉变压器 (GNViT) 模型,用于检测和分类花生害虫.
- 使用标准可靠性指标评估GNViT的有效性,并将其性能与现有方法进行比较.
主要方法:
- 在ImageNet数据集上使用预训练的视觉转换器 (ViT) 来开发GNViT模型.
- 在IP102数据集上训练和评估模型,包括诸如Thrips,Aphids,Armyworms和Wireworms等害虫.
- 采用数据增强技术来提高模型训练和性能.
主要成果:
- 在数据增强后,GNViT模型实现了99.52%的训练精度.
- 使用F1得分,回忆和整体准确度评估模型性能.
- 与害虫分类中最先进的方法相比,证明了更高的准确性.
结论:
- GNViT模型提供了一种可靠的深度学习解决方案,用于对影响花生作物的害虫进行分类.
- 这项技术有可能显著减少作物损失,并为全球粮食安全做出贡献.
- 先进的AI解决方案对于应对农业挑战和支持不断增长的全球人口至关重要.
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