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基于物联网的智能虫害管理系统用于精准农业

Salman Ahmed¹, Safdar Nawaz Khan Marwat^2,3, Ghassen Ben Brahim⁴

¹Faculty of Computer Science and Engineering, GIK Institute, Swabi, 23640, Pakistan.

Scientific reports

|December 31, 2024

概括

本研究介绍了一种基于物联网的害虫检测系统,使用智能昆虫陷和卷积神经网络 (CNN) 来应对粮食不安全. 该技术准确识别害虫,帮助农民精准农业和综合害虫管理.

科学领域:

农业技术农业技术
计算机科学计算机科学
粮食安全粮食安全

背景情况:

传统的农业方法面临诸如人类错误和在害虫检测方面劳动力短缺等挑战,威胁着粮食安全,特别是在发展中国家.
精准农业 (PA) 提供技术驱动的解决方案,以提高作物保护和生产力.
现有的害虫识别方法往往是低效的,容易出现不准确的情况.

研究的目的:

提出和评估基于物联网 (IoT) 的智能害虫检测平台,用于综合性害虫管理.
开发一个系统,通过监测作物田地条件和准确检测害虫来帮助农民.
通过农业的技术进步,解决粮食不安全风险.

主要方法:

物理原型的开发:一个智能昆虫陷,嵌入式计算用于害虫检测和分类.
创建了一个数据集,包括1000多张东方果在瓜果园的不同照明条件下的图像.
训练一个卷积神经网络 (CNN) 分类器,使用Haralick特征,定向梯度 (HOG) 的直方图,Hu时刻和颜色直方图.

主要成果:

拟议的系统在真实测试图像上实现了86.2%的回忆率和97.3%的平均精度 (mAP).
对比分析表明,拟议的模型表现优于许多其他机器学习 (ML) 和深度学习 (DL) 模型.
该模型在准确性 (97.5%),精度 (92.82%),回忆 (98.92%),F1得分 (95.00%),特异性 (95.90%) 和低虚假阴性率 (FNR) (5.88%) 中表现出卓越的性能.

结论:

开发的基于物联网的害虫检测平台是有效的综合害虫管理和作物监测.
智能昆虫陷系统由CNN提供,为病虫识别提供可靠和准确的解决方案,减轻与传统方法相关的风险.
这项技术具有显著的潜力,可以提高农业生产率,并为全球粮食安全做出贡献.

关键词:

深度学习是一种深度学习.物联网的物联网,就是物联网.机器学习是机器学习.聪明的昆虫陷聪明的昆虫陷

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