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Insects
Special Issues
AI and Cloud Computing for Insect Ecology and Management
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AI and Cloud Computing for Insect Ecology and Management

A Special Issue of Insects (ISSN 2075-4450).

Deadline for manuscript submissions: 31 December 2026 | Viewed by 1170

Editors

Dr. Denis S. Willett

E-Mail Website
Guest Editor
North Carolina Institute for Climate Studies, Department of Applied Ecology, NC State University, 151 Patton Avenue, Asheville, NC 28801, USA
Interests: data science; data engineering; cloud computing; chemical ecology; insect behavior; plant–insect interactions; integrated pest management; applied entomology; digital agriculture; AI applications in ecology
Dr. Camila C. Filgueiras

E-Mail Website
Guest Editor
Department of Biology, University of North Carolina Asheville, One University Heights, Asheville, NC 28804, USA
Interests: biological control; biodiversity science; plant-insect-parasite interactions; applied entomology; digital agriculture; AI applications in ecology

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) and cloud computing are rapidly transforming the biological sciences, yet their full potential in entomology remains largely untapped. From deep learning models that automate species identification in camera trap images to cloud-based platforms that aggregate and analyze pest monitoring data across continental scales, these technologies offer unprecedented opportunities for insect ecology and management. This Special Issue of Insects aims to bring together researchers at the intersection of entomology and digital technology. We welcome original research articles, reviews, and perspectives that address the application of machine learning, computer vision, natural language processing, remote sensing, Internet of Things (IoT) sensor networks, and cloud computing infrastructure to entomological questions. Topics of interest include, but are not limited to, automated insect identification and classification, predictive modeling of pest outbreaks and species distributions, real-time monitoring systems for agricultural and ecological applications, large-scale data integration and analysis pipelines, and AI-assisted decision support tools for integrated pest management. We are particularly interested in interdisciplinary papers that combine entomological approaches with complementary data from other disciplines including weather/climate, engineering, human systems, and management. We encourage contributions that demonstrate novel computational workflows, validate AI tools against expert benchmarks, or explore the challenges and ethical considerations of deploying these technologies in field settings.

Dr. Denis S. Willett
Dr. Camila C. Filgueiras
Guest Editors

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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized 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

  • artificial intelligence
  • machine learning
  • cloud computing
  • entomology
  • big data
  • insect ecology
  • pest management
  • computer vision
  • species identification
  • remote sensing
  • IoT sensor networks

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

Published Papers (2 papers)

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Research

25 pages, 3738 KB  
Article
ESD-YOLO: A Method for Small-Target Termite Detection Under Complex Backgrounds
by Weiling Lu, Yuting Meng, Shan Wu and Hangjun Wang
Insects 2026, 17(8), 874; https://doi.org/10.3390/insects17080874 - 21 Aug 2026
Viewed by 295
Abstract
Timely and accurate termite detection is essential for effective termite control. To address the challenges posed by the small size of termite individuals and the susceptibility of target features to background texture interference under complex backgrounds, this study proposes ESD-YOLO, a fine-grained feature-enhanced [...] Read more.
Timely and accurate termite detection is essential for effective termite control. To address the challenges posed by the small size of termite individuals and the susceptibility of target features to background texture interference under complex backgrounds, this study proposes ESD-YOLO, a fine-grained feature-enhanced object detection model. Using YOLO11n as the baseline, ESD-YOLO redesigns the feature extraction, deep feature aggregation, and multi-scale feature fusion stages to improve the representation of small-scale termite targets under complex backgrounds. Specifically, the Efficient Multi-scale Attention (EMA) mechanism is incorporated into the C3k2 module to enhance feature discriminability between termite individuals and the background. A Spatial Pyramid Pooling-Fast with Dual Global Pooling (SPPF-DGP) module is employed to supplement deep features with global contextual information and salient response information. In addition, the DySample dynamic upsampling module is introduced to improve spatial alignment during multi-scale feature fusion and enhance boundary representation for small targets. Experimental results show that ESD-YOLO achieves Precision, Recall, mAP@0.5, and mAP@0.5:0.95 values of 95.39%, 96.33%, 97.85%, and 65.92%, respectively, with 2.67 M parameters and 6.68 G FLOPs. Compared with Faster R-CNN, RetinaNet, RT-DETR, and several YOLO-series models, ESD-YOLO demonstrates strong small-target detection and localization performance under the controlled complex-background conditions established in this study, providing a methodological reference for automated termite detection in practical settings. Full article
(This article belongs to the Special Issue AI and Cloud Computing for Insect Ecology and Management)
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15 pages, 3255 KB  
Article
Explainable Classification of Different Coloration Stages in Cherry Fruits Using a Hybrid RF-ACO Model Based on Pomological and Cherry Fly Data
by Cebrail Barut, İnanç Özgen, Halil Bolu, Bilal Alataş, Hakan Yildirim and Ali Murat Tatar
Insects 2026, 17(8), 831; https://doi.org/10.3390/insects17080831 - 10 Aug 2026
Cited by 1 | Viewed by 464
Abstract
The coloring process in cherry fruit affects both fruit quality and the cherry fly (Rhagoletis cerasi). This is critically important for host preferences of major pests such as cherry fly. In this study, a randomized classification method was used to classify [...] Read more.
The coloring process in cherry fruit affects both fruit quality and the cherry fly (Rhagoletis cerasi). This is critically important for host preferences of major pests such as cherry fly. In this study, a randomized classification method was used to classify five distinct coloration stages of cherry fruit based on pomological characteristics and cherry fly density data. An explainable hybrid model combining Random Forest (RF) and Ant Colony Optimization (ACO) algorithms has been developed. In this study, fruit samples of the Ziraat 900 variety were collected from four different cherry orchards in Elazığ province during five different coloration stages. Pomological characteristics such as weight, width, length, height, stem length, firmness, seed weight, soluble solids content (SSC), NaOH, and acidity were determined, and adult cherry fly densities were also recorded. In the proposed method, candidate decision rules generated by the RF algorithm were optimized using the ACO algorithm, and the most distinctive rule sets were selected. The results showed that the proposed RF-ACO model achieved a 99.48% accuracy rate and exhibited higher performance than many common machine learning methods. Feature significance analysis revealed that SSC, cherry fly density, NaOH, and acidity were the most effective parameters in the classification process. The model not only provided high accuracy thanks to the explainable IF-THEN rules it generated, but also allowed for expert interpretation of the decision-making process. The findings offer significant contributions to the development of decision support systems for determining cherry ripening periods and controlling the cherry fruit fly. Full article
(This article belongs to the Special Issue AI and Cloud Computing for Insect Ecology and Management)
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