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利用物联网和优化深度残留网络,改善果园的叶病检测

Sameera Kuppam¹, Swarnalatha Purushotham¹

¹School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamilnadu, India.

Network (Bristol, England)

|March 24, 2025

概括

这项研究引入了一种新的深度学习模型,用于果叶病的分类,达到97%的准确性. 基于Tunicate Swarm Sine Cosine算法的深度残留网络 (基于TSSCA的DRN) 显著改善了农业中的疾病检测.

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:

植物疾病通过降低作物产量和质量,对全球粮食安全构成重大威胁.
准确和早期发现植物疾病对于有效的管理和缓解策略至关重要.

研究的目的:

开发和评估一种新的深度学习模型,用于准确地分类果植物的叶病.
通过使用混合优化算法来提高深度残留网络的性能,以改善疾病识别.

主要方法:

一个基于Tunicate Swarm Sine Cosine算法的深度残留网络 (基于TSSCA的DRN) 被提议用于叶病的分类.
图像预处理涉及使用DeepJoint模型进行高斯过和细分.
连衣群算法 (TSA) 和正弦共弦算法 (SCA) 合并为TSSCA,以优化分类器.
植物病理学2020 - FGVC7数据集被用于培训和验证.

主要成果:

基于TSSCA的DRN实现了97%的高精度,具有94.666%的特异性和96.888%的灵敏度.
该模型与现有的各种深度学习和机器学习方法相比,表现优越,高达8.97%的表现优于它们.
最大能耗记录在0.0442 J,表明计算效率.

结论:

拟议的基于TSSCA的DRN提供了一个高度准确和有效的解决方案,用于自动化果叶病疾病分类.
这种深度学习方法具有改善农业监测和疾病管理的巨大潜力,从而有助于粮食安全.
混合优化算法与深度学习架构的集成显示了促进植物病理学诊断的前景.

关键词:

在DeepJoint模型中,DeepJoint的模型是叶片病是一种叶片病.深度残留网络深度残留网络物联网的东西互联网.

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