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Updated: Jan 16, 2026

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优化深度学习框架用于使用自然灵感算法检测石榴病.

Anil Sandhi1, Rajeev Kumar2, Reeta Bhardwaj1

  • 1DAV Institute of Engineering and Technology, Jalandhar, Punjab, India.

Plant methods
|October 4, 2025
PubMed
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此摘要是机器生成的。

这项研究引入了一个先进的AI框架,用于准确检测石榴病,显著改善了农业的早期干预. 优化的模型提高了作物产量预测,并减少了植物病原体造成的经济损失.

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:

  • 农作物疾病对全球粮食安全和农业经济构成重大威胁.
  • 石榴种植面临大量的收获损失 (20-40%) 由于各种病原体.
  • 目前的疾病检测方法是劳动密集型,主观,缺乏效率,而现有的AI模型则在与现实世界的环境变化作斗争.

研究的目的:

  • 开发一个自动化,强大,计算效率高的框架来检测石榴病.
  • 提高农业环境中植物疾病识别的准确性和可靠性.
  • 在现场条件下克服传统方法和现有的深度学习模型的局限性.

主要方法:

  • 将修改后的ResNet101架构与混合基因算法-粒子群优化 (HGA-PSO) 集成.
  • 双流图像处理使用原始和噪声增强 (高斯式,盐和胡,斑点) 图像进行增强的稳定性.
  • 使用HGA-PSO来保持区分能力的特征融合和维度减少 (50-70%) .

主要成果:

  • 在五个类的5000张图像数据集上实现了99.10%的准确性,完美的1.00 ROC-AUC得分,以及高精度回忆指标.
  • 通过混矩阵和现实世界测试中的强烈概括,证明了接近零的错误分类.
关键词:
计算机视觉 计算机视觉 计算机视觉深度学习是一种深度学习.遗传算法 遗传算法 遗传算法粒子群集优化优化 粒子群集优化石榴果实疾病 石榴果实疾病在Resnet101中使用.

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  • 在关键绩效指标上表现优于PSO-YOLOv8 (98.86%) 和变压器模型 (93.13%) 等现有方法.

    • 结论:

    • 开发的框架为早期检测石榴疾病提供了可扩展和优化的解决方案.
    • 深度学习和以自然为灵感的优化相结合,显著提高了对环境变化的稳定性,并减少了计算负载.
    • 通过促进及时的疾病干预,实现精准农业,从而减轻经济损失和改善作物管理.