检索结果-内蒙古大学图书馆

Advanced sound-based pest detection in agriculture using deep learning and adaptive optimization

International Journal of Information Technology (Singapore) 2025年 1-11页

作者： R, Sahana Lokesh SailajaKumar, K. S.Soundariya, R. K S, Kavitha Reshma, S. Department of Computer Science & Engineering (Cyber Security) Ramaiah Institute of Technology Bengaluru India SoCSE RV University Bangalore India Department of Computer Technology Bannari Amman Institute of Technology Tamil Nadu Sathyamangalam India Department of Computer Science and Engineering Dayananda Sagar College of Engineering Bengaluru India Department of Artificial Intelligence and Machine Learning Dayananda Sagar College of Engineering Bengaluru India

Implementing sound-based pest detection systems in large fields comes with challenges, including the complexity of acoustic data, interference from environmental noise, and the need for scalable, real-time solutions capable of processing data over vast areas. To address these challenges, we propose an innovative pest detection system that integrates advanced sound analytics with deep learning techniques. This study proposes a feature selection technique that includes the real-valued discrete Gabor transform algorithm and linear frequency cepstrum coefficients (LFCC) algorithm, which helps analyze the frequency spectrum and capture critical features. The approach operates in four stages: pre-processing, feature selection, feature matrix generation and sound classification. This study proposes primarily, that data is collected and pre-processed, followed by the feature selection technique including the Real-Valued Discrete Gabor Transform algorithm and linear frequency cepstrum coefficients (LFCC) algorithm, which help analyze the frequency spectrum and capture critical features. These selected features are then generated as a feature matrix. After that, the pest sound is classified using Modified EfficientNet. To optimize the model’s parameters and enhance its performance and accuracy, the Hippopotamus Optimization algorithm (HOA) is integrated with EfficientNet. The dataset was utilized to validate the suggested approach, with accuracy levels of over 98% and specificity is 97%, respectively. These results demonstrate superior classification efficiency compared to existing pest detection systems. © Bharati Vidyapeeth's Institute of Computer Applications and Management 2025.

关键词： Feature matrix Hippopotamus Optimization algorithm linear frequency cepstrum coefficients algorithm Modified EfficientNet Real-Valued Discrete Gabor Transform algorithm

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：