Artificial Intelligence for Classification and Detection of Major Insect Pests of Brinjal

Authors

  • Saikumar N Dr YSR Horticultural University, Venkataramannagudem, West Godavari District 543101, Andhra Pradesh
  • N Emmanuel Dr YSR Horticultural University, Venkataramannagudem, West Godavari District 543101, Andhra Pradesh
  • K Sri Phani Krishna National Institute of Technology, Tadepalligudem 543101 Andhra Pradesh
  • Ch Chinnabbai Dr YSR Horticultural University, Venkataramannagudem, West Godavari District 543101, Andhra Pradesh
  • K Uma Krishna Dr YSR Horticultural University, Venkataramannagudem, West Godavari District 543101, Andhra Pradesh

DOI:

https://doi.org/10.55446/IJE.2023.1388

Keywords:

Machine learning (ML), Convolutional neural networks, python language, keras, tensor flow, insect pest, insect images, prediction models, insect boxing, insect monitoring

Abstract

The present study was carried out during rabi 2020-21 at the College of Horticulture, Venkataramannagudem, West Godavari, Andhra Pradesh. Detection and monitoring of insect pests through Artificial intelligence (AI) were conducted using the Python software through Keras and Tensorflow frame works and for this purpose CNN VGG-16 model was used. A total of 204 insect images were loaded and included in four datasets. All the datasets were resized to 224 _ 224 pixels. CNN VGG-16 model codes developed for automatic pest classification and detection of pest images were run through python language to retrieve the predicted output. In the pursuit of detecting insect classification an accuracy of 95-98% for 4 insect classes viz., brinjal shoot and fruit borer larvae and adults, Epilachna beetle grub and adults were predicted with F1 score of 0.89 which shows that the CNN (VGG) model is consistent in detecting the type of insect.

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Published

2023-09-01

How to Cite

N, S., Emmanuel, N., Sri Phani Krishna, K., Chinnabbai, C., & Uma Krishna, K. (2023). Artificial Intelligence for Classification and Detection of Major Insect Pests of Brinjal. Indian Journal of Entomology, 85(3), 563–566. https://doi.org/10.55446/IJE.2023.1388

Issue

Volume 85, Issue 3, September 2023

Section

Research Articles
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Europe PMC

References

Garcia J, Barbed A. 2020. Detecting and classifying pests in crops using proximal images and machine learning. Journal on Agricultural Informatics 131:10-17.

Kasinathan T, Singaraju D, Srinivasulu U. 2020. Insect classification and detection in field crops using modern machine learning Techniques. Information Processing in Agriculture 1(3):128-132.

Liu P, Chong de S. 2019. Pest management using machine Learning algorithms. Review of International Journal of Computer Science Engineering and Information Technology Research 2249-6831: ISSN(E):2249-7943.

Tuda M, Isabel A, Maldonado L. 2020. Image -based insect species and gender classification by trained supervised machine learning algorithms. Ecological Informatics 22(5&6): 106-110.

Zhu J L, Gerardo B D, Tanguilig B T. 2016. Pest detection and extraction using image processing techniques. International journal of computer and communication engineering 3(3): 189-192.