Multiclass insect counting through deep learning-based density maps estimation-Reference-Cited by-同舟云学术

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Multiclass insect counting through deep learning-based density maps estimation

Published:2023-02 Issue: Volume:3 Page:100125
ISSN:2772-3755
Container-title:Smart Agricultural Technology
language:en
Short-container-title:Smart Agricultural Technology

Author:

Bereciartua-Pérez Arantza^ORCID,Gómez Laura,Picón Artzai,Navarra-Mestre Ramón,Klukas Christian,Eggers Till

Publisher

Elsevier BV

Reference49 articles.

1. Object counting with small datasets of large images;Aich;Comput. Vis. Pattern Recognit.,2018

2. Counting in the wild;Arteta,2016

3. Using digital image processing for counting whiteflies on soybean leaves;Barbedo;J. Asia Pac. Entomol.,2014

4. Insect counting through deep learning-based density maps estimation;Bereciartua-Pérez;Comput. Electron. Agric.,2022

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2. AgroCounters—A repository for counting objects in images in the agricultural domain by using deep-learning algorithms: Framework and evaluation;Computers and Electronics in Agriculture;2024-07

3. Can artificial intelligence be integrated into pest monitoring schemes to help achieve sustainable agriculture? An entomological, management and computational perspective;Agricultural and Forest Entomology;2024-05-16

4. A deep learning-based pipeline for whitefly pest abundance estimation on chromotropic sticky traps;Ecological Informatics;2023-12

5. Nymphstar: An accurate high‐throughput quantitative method for whitefly (Aleurotrachelus socialis Bondar) resistance phenotyping in cassava;The Plant Phenome Journal;2023-12

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