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<p>The pulse beetle, <i>Callosobruchus maculatus</i> (F.) is a major pest responsible for significant damage to stored food legumes, leading to considerable post-harvest losses worldwide. Traditional pest monitoring techniques such as manual inspection and traps, often lack precision and are labor-intensive, making them inefficient for large-scale storage systems. This study focuses on leveraging an acoustic detection system as a novel approach for automated pest monitoring, integrating it into effective integrated pest management (IPM) strategies. By utilizing a digital MEMS microphone system, the study captures and analyzes the crawling and moving sounds of <i>C. maculatus</i> in green gram and cowpea storages. The experiments were conducted at varying infestation densities ranging from 0 to 500 insects, providing a comprehensive dataset for analysis. Results highlight the high sensitivity and accuracy of acoustic monitoring in detecting the presence of pest and estimating population density, even at low infestation levels. Principal component analysis (PCA) confirmed distinct clustering patterns corresponding to infestation levels, with PC1 explaining over 97% of the total variation in both legumes. These findings validate the system’s high sensitivity and accuracy in detecting and quantifying infestation levels. The method offers real-time monitoring capability, minimizes the need for chemical treatments, and supports early pest intervention in integrated pest management (IPM) programs. This scalable, sustainable solution has the potential to significantly enhance grain storage management, reduce economic losses, and improve food security. Such advancements contribute to improved grain quality, enhanced food security, and reduced economic losses in stored grain management systems.</p>

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Acoustic detection of Callosobruchus maculatus (F.) (Coleoptera: Chrysomelidae) in green gram and cowpea using MEMS microphone system

  • 期刊论文

  • 发表日期：

    2025年9月4日

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  • B. Keerthana,
  • G. Preetha,
  • K. Umapathi,
  • V. R. Saminathan,
  • T. Eevera,
  • J. Malathi,
  • S. Santhi

摘要

The pulse beetle, Callosobruchus maculatus (F.) is a major pest responsible for significant damage to stored food legumes, leading to considerable post-harvest losses worldwide. Traditional pest monitoring techniques such as manual inspection and traps, often lack precision and are labor-intensive, making them inefficient for large-scale storage systems. This study focuses on leveraging an acoustic detection system as a novel approach for automated pest monitoring, integrating it into effective integrated pest management (IPM) strategies. By utilizing a digital MEMS microphone system, the study captures and analyzes the crawling and moving sounds of C. maculatus in green gram and cowpea storages. The experiments were conducted at varying infestation densities ranging from 0 to 500 insects, providing a comprehensive dataset for analysis. Results highlight the high sensitivity and accuracy of acoustic monitoring in detecting the presence of pest and estimating population density, even at low infestation levels. Principal component analysis (PCA) confirmed distinct clustering patterns corresponding to infestation levels, with PC1 explaining over 97% of the total variation in both legumes. These findings validate the system’s high sensitivity and accuracy in detecting and quantifying infestation levels. The method offers real-time monitoring capability, minimizes the need for chemical treatments, and supports early pest intervention in integrated pest management (IPM) programs. This scalable, sustainable solution has the potential to significantly enhance grain storage management, reduce economic losses, and improve food security. Such advancements contribute to improved grain quality, enhanced food security, and reduced economic losses in stored grain management systems.