深度网络:一个端到端的框架,用于高性能农作物害虫识别
K S Guruprakash1, P Siva Karthik2, A Ramachandran3
1Department of Computer Science and Engineering, K.Ramakrishnan College of Engineering, Trichy, Tamil Nadu 621112, India.
Computational biology and chemistry
|January 31, 2026
概括
一个新的深度学习模型DeeppestNet通过提高图像质量和提取多尺度特征来增强作物害虫识别. 这种准确高效的方法超过了现有的可持续农业算法.
科学领域:
- 农业科学 农业科学
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 准确的农作物害虫识别对于可持续农业至关重要,但受到噪音背景和有限数据的阻碍.
- 现有的方法与特征提取和虫害识别算法复杂性作斗争.
研究的目的:
- 提出DeeppestNet,一个新的基于注意力的双向长期短期记忆 (GPA-BiLSTM) 模型,用于准确有效地识别农作物害虫.
- 为了应对噪音背景,有限的数据和害虫识别算法复杂性的挑战.
主要方法:
- 使用对比度适应性有限图形图形平衡 (CLAHE) 的图像质量增强.
- 使用具有交叉阶段部分 (AP-CSP) 骨干和多层次金字塔结构的自适应金字塔注意力模块进行特征提取.
- 使用基于图的BiLSTM (G-BiLSTM) 进行分类,并使用灰狼-鱼群优化 (GW-SSO) 进行优化.
主要成果:
- 在IP-102数据集上,DeeppestNet的准确性比EfficientNet高4.6%.
- 与最先进的深度学习算法相比,提出的方法显示出更高的性能.
- 该模型显示了准确性,效率和计算效率.
结论:
- DeeppestNet提供了一个准确而高效的解决方案,用于作物害虫识别,克服了以前方法的局限性.
- 整合CLAHE,AP-CSP,G-BiLSTM和GW-SSO有效地提高了害虫识别能力.
- 拟议的模型通过改进的害虫管理技术,有助于推进可持续农业.
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