Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.1
2.7
Journals
Insects
Special Issues
Ecology, Behaviour, and Monitoring of Stored Product Insects
share announcement

Ecology, Behaviour, and Monitoring of Stored Product Insects

A special issue of Insects (ISSN 2075-4450). This special issue belongs to the section "Insect Pest and Vector Management".

Deadline for manuscript submissions: 30 June 2025 | Viewed by 1348

Special Issue Editors

Dr. Fuji Jian

E-Mail Website
Guest Editor
Department of Biosystems Engineering, Faculty of Agricultural & Food Science, University of Manitoba, Winnipeg, MB R3T5V6, Canada
Interests: modelling of grain storage ecosystems; grain drying; airflow resistance; distribution of dockages; insect ecology; insect control
Special Issues, Collections and Topics in MDPI journals
Dr. Vimala S. K. Bharathi

E-Mail Website
Guest Editor Assistant
Department of Biosystems Engineering, E2-376 EITC, 75 Chancellors Circle, University of Manitoba, Winnipeg, MB R3T 5V6, Canada
Interests: stored product insect; rusty grain beetle; grain storage; biopolymers; nanofibers

Special Issue Information

Dear Colleagues,

This Special Issue will cover any studies related to stored product insects such as their ecology, population dynamics, movement and flying behaviour, distribution, diet preference, development on different diets and under different environmental conditions, inter- and intra-species interaction, monitoring and detection in stored structures, and estimation of their density onsite. Both original research articles and reviews will be considered for publication.

Dr. Fuji Jian
Guest Editor

Dr. Vimala S. K. Bharathi
Guest Editor Assistant

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Insects is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • stored product insect
  • ecology
  • population dynamics
  • movement and flying behaviour
  • diet preference and development
  • monitoring and detection
  • density estimation

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (1 paper)

Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

17 pages, 16790 KiB  
Article
A YOLO-Based Model for Detecting Stored-Grain Insects on Surface of Grain Bulks
by Xueyan Zhu, Dandan Li, Yancheng Zheng, Yiming Ma, Xiaoping Yan, Qing Zhou, Qin Wang and Yili Zheng
Insects 2025, 16(2), 210; https://doi.org/10.3390/insects16020210 - 14 Feb 2025
Viewed by 753
Abstract
Accurate, rapid, and intelligent stored-grain insect detection and counting are important for integrated pest management (IPM). Existing stored-grain insect pest detection models are often not suitable for detecting tiny insects on the surface of grain bulks and often require high computing resources and [...] Read more.
Accurate, rapid, and intelligent stored-grain insect detection and counting are important for integrated pest management (IPM). Existing stored-grain insect pest detection models are often not suitable for detecting tiny insects on the surface of grain bulks and often require high computing resources and computational memory. Therefore, this study presents a YOLO-SGInsects model based on YOLOv8s for tiny stored-grain insect detection on the surface of grain bulk by adding a tiny object detection layer (TODL), adjusting the neck network with an asymptotic feature pyramid network (AFPN), and incorporating a hybrid attention transformer (HAT) module into the backbone network. The YOLO-SGInsects model was trained and tested using a GrainInsects dataset with images captured from granaries and laboratory. Experiments on the test set of the GrainInsects dataset showed that the YOLO-SGInsects achieved a stored-grain insect pest detection mean average precision (mAP) of 94.2%, with a counting root mean squared error (RMSE) of 0.7913, representing 2.0% and 0.3067 improvement over the YOLOv8s, respectively. Compared to other mainstream approaches, the YOLO-SGInsects model achieves better detection and counting performance and is capable of effectively handling tiny stored-grain insect pest detection in grain bulk surfaces. This study provides a technical basis for detecting and counting common stored-grain insect pests on the surface of grain bulk. Full article
(This article belongs to the Special Issue Ecology, Behaviour, and Monitoring of Stored Product Insects)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-1
Back to TopTop