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Image Processing and Computational Intelligence for Pest Detection in Crops

  • Unique Paper ID: 176231
  • Volume: 7
  • Issue: 12
  • PageNo: 773-777
  • Abstract:
  • This research introduces an automated approach for early pest detection in agriculture. Agriculture is not only essential for human sustenance but also plays a crucial role in a country's economy. Every year, millions of dollars are spent on safeguarding crops from damage. Pests and insects pose a significant threat to crop health, potentially leading to substantial production losses. One of the most effective ways to mitigate this issue is early pest detection, which enables timely intervention and protection against pest infestations. Regular monitoring of crop health is essential, as early pest identification allows for appropriate countermeasures, reducing the risk of severe crop damage. Moreover, early detection helps minimize pesticide usage by ensuring that only necessary treatments are applied, thereby guiding farmers in selecting the most suitable pesticides. Given its importance, automatic pest detection has become a significant area of research, with numerous studies conducted globally to develop advanced detection methods. Traditionally, field monitoring has relied on manual inspection, where experts examine crops with the naked eye. However, this approach is inefficient for large agricultural fields, as it is both labor-intensive and time-consuming. To address these limitations, an automated pest detection system is required that can analyze crops, detect pest infestations, and classify pest types efficiently. Computer vision techniques provide an effective solution for analyzing leaf images to identify pest presence. This study employs Support Vector Machine (SVM) for image classification, distinguishing between pest-infected and healthy crops based on extracted image features. Compared to other automated methods, SVM offers a simpler yet highly efficient classification approach, making it a suitable choice for early pest detection in agriculture.
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Cite This Article

  • ISSN: 2349-6002
  • Volume: 7
  • Issue: 12
  • PageNo: 773-777

Image Processing and Computational Intelligence for Pest Detection in Crops

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