一个轻量级的深度学习模型,用于对多种植物生物应激的分类和检测,用于可持续农业
Wasswa Shafik1,2, Ali Tufail3, Liyanage Chandratilak De Silva1
1School of Digital Science, Universiti Brunei Darussalam, Gadong, BE1410, Brunei.
Scientific reports
|April 9, 2025
概括
一个新的轻量级深度学习模型,AgarwoodNet,有效地使用新型数据集检测植物害虫和疾病. 该模型为可持续农业提供了可扩展的解决方案,克服了现有的重型深度学习方法的局限性.
科学领域:
- 农业科学 农业科学
- 计算机科学 计算机科学
- 植物病理学 植物病理学
背景情况:
- 植物病原体和害虫显著降低作物产量,影响全球粮食安全并造成经济损失.
- 现有的深度学习 (DL) 模型用于检测植物疾病往往需要大量的计算资源,导致部署挑战.
研究的目的:
- 引入一个强大而轻量级的深度学习模型,AgarwoodNet,以有效地检测植物害虫和疾病.
- 解决当前DL模型的局限性,包括高资源需求和不灵活性.
主要方法:
- 开发和培训 AgarwoodNet 模型使用 Agarwood 病虫害和疾病数据集 (APDD) 和土耳其植物病虫害和疾病 (TPPD) 数据集.
- 使用精度,回忆,F1分数和科恩的卡帕等指标进行绩效评估.
主要成果:
- AgarwoodNet在APDD数据集上实现了高性能,宏观平均精度,回忆和F1分数分别为0.9666,0.9714,0.9859.
- 该模型在TPPD数据集上表现出强的表现,准确率为95.85%,卡帕分数为0.9684.
- AgarwoodNet的模型大小为37兆字节,比许多预训练的卷积神经网络要小得多.
结论:
- AgarwoodNet为植物疾病和害虫检测提供了一种轻量级和有效的解决方案,适合在低内存设备上部署.
- 该模型通过精确准确的分类来支持可持续农业,克服了较重的DL模型的资源限制.
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