Enhancing Insect Species Identification in Agriculture using Fusion BiLSTM Network and VGG-16 CNN Architecture-Reference-Cited by-同舟云学术

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Enhancing Insect Species Identification in Agriculture using Fusion BiLSTM Network and VGG-16 CNN Architecture

Published:2024-05-09 Issue: Volume:9 Page:1-6
ISSN:
Container-title:2024 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI)
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Author:

Devi Dharani¹,Devakadacham Sterlin Rani²,Saveetha D.³,Manikandan J⁴

Affiliation:

1. Rajalakshmi Engineering College,Computer Science and Engineering,Chennai,India

2. R. M. D. Engineering College,Computer Science and Engineering,Kavaraipettai,India

3. SRM Institute of Science and Technology,Department of Networking and Communications,Kattankulathur,India

4. St. Joseph’s College of Engineering,Information Technology,Chennai,India

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10601631/10601667/10602372.pdf?arnumber=10602372

Reference15 articles.

1. A review of deep learning techniques used in agriculture

2. A Review of the Challenges of Using Deep Learning Algorithms to Support Decision-Making in Agricultural Activities

3. Insect classification and detection in field crops using modern machine learning techniques

4. Plant disease and pest detection using deep learning-based features

5. Identification of Crop Diseases and Insect Pests Based on Deep Learning

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