Field detection of pests based on adaptive feature fusion and evolutionary neural architecture search-Reference-Cited by-同舟云学术

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Field detection of pests based on adaptive feature fusion and evolutionary neural architecture search

Published:2024-06 Issue: Volume:221 Page:108936
ISSN:0168-1699
Container-title:Computers and Electronics in Agriculture
language:en
Short-container-title:Computers and Electronics in Agriculture

Author:

Ye Yin,Chen Yaxiong,Xiong Shengwu^ORCID

Funder

Wuhan University of Technology

Guangxi Key Research and Development Program

National Natural Science Foundation of China

National Key Research and Development Program of China

Sanya Yazhou Bay Science and Technology City

Natural Science Foundation of Hainan Province

Publisher

Elsevier BV

Reference68 articles.

1. IoT-based cotton plant pest detection and smart-response system;Azfar;Appl. Sci.,2023

2. A review of neural architecture search;Baymurzina;Neurocomputing,2022

3. Cai, Z., Vasconcelos, N., 2018. Cascade r-cnn: Delving into high quality object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 6154–6162.

4. Diffusiondet: Diffusion model for object detection;Chen,2023

5. Non-convergence of stochastic gradient descent in the training of deep neural networks;Cheridito;J. Complexity,2021

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Innovative lightweight deep learning architecture for enhanced rice pest identification;Physica Scripta;2024-08-12

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