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Insect Recognition and Classification Using Optimized Densely Connected Convolutional Neural Network

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12th International Conference on Information Systems and Advanced Technologies “ICISAT 2022” (ICISAT 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 624))

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Abstract

Image-based object identification like Insect recognition depends on manual assessment and traps. Automation such as combination of pattern recognition and machine vision have been implemented and therefore the convolutional neural network, has reached some important applications in various areas like fruit sorting, robotic harvesting, quality observation, etc. Recently, insect recognition work based on deep learning shows the most effective outcome to classification performance. A densely connected convolutional neural network is used to classify pests and inspect the outcome of classification performance with other models using the DenseNet201 model. The experiment has been performed on 19 insect classes. Experimental result shows that the proposed method ensures best accuracy and classification performance where insects are well depicted at the middle of the image and ranging degrees of background clutter are reduced. The mean accuracy for classification ranges from 87% which has provided an optimized approach for insect pest detection and recognition.

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Author information

Authors and Affiliations

  1. Prime University, Dhaka, Bangladesh

    Rezoana Akter & Md Imtiaz Ahmed

  2. Jahangirnagar University, Dhaka, Bangladesh

    Mohammad Shahidul Islam & Kazi Sohan

Authors
  1. Rezoana Akter
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  2. Mohammad Shahidul Islam
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  3. Kazi Sohan
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  4. Md Imtiaz Ahmed
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Corresponding author

Correspondence to Md Imtiaz Ahmed .

Editor information

Editors and Affiliations

  1. LAMIS Laboratory, Université Larbi Tébessi, Tebessa, Algeria

    Mohamed Ridda Laouar

  2. Automation and Applied Informatics, Aurel Vlaicu University of Arad, Arad, Arad, Romania

    Valentina Emilia Balas

  3. University of El-Oued, EL-Oued, Algeria

    Brahim Lejdel

  4. Department of Accounting, Southeast Missouri State University, Cape Girardeau, MO, USA

    Sean Eom

  5. Taher Moulay University of Saida, Saida, Algeria

    Mohamed Amine Boudia

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Akter, R., Islam, M.S., Sohan, K., Ahmed, M.I. (2023). Insect Recognition and Classification Using Optimized Densely Connected Convolutional Neural Network. In: Laouar, M.R., Balas, V.E., Lejdel, B., Eom, S., Boudia, M.A. (eds) 12th International Conference on Information Systems and Advanced Technologies “ICISAT 2022”. ICISAT 2022. Lecture Notes in Networks and Systems, vol 624. Springer, Cham. https://doi.org/10.1007/978-3-031-25344-7_23

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