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Artificial Intelligence (AI) and Insect Pests Management: Securing Food Security,...
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Artificial Intelligence (AI) and Insect Pests Management: Securing Food Security, Human Health, and Natural Resources

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

Deadline for manuscript submissions: 31 October 2025 | Viewed by 10320

Special Issue Editor

Dr. Muhammad Haseeb

E-Mail Website
Guest Editor
Center for Biological Control, College of Agriculture and Food Sciences, Florida A&M University, Tallahassee, FL 32307, USA
Interests: integrated pest management of invasive insects pests; identification and diagnosis; biological control; insect pest modeling and predictions; insect identification; insect detection; insect monitoring and management in specialty crops
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Insect pests cause serious challenges to food production systems, human health, and natural resources. The economic, ecological, and social costs to control these pests in the production systems, forests, and urban areas are increasing every year. As a result, economical and ecological costs are increasing to manage them. In recent years, artificial intelligence (AI) development to support integrated pest management is gaining significant attention for pest identification, detection, monitoring, and management of invasive and established pests. Indeed, AI technology has the potential to revolutionize food production systems, human health, and natural resources by improving the speed and accuracy of insect pests’ surveillance, detection, and management. Certainly, it could help with the offshore mitigation of invasive pests and sustain trade and tourism. It is not surprising to see AI being incorporated into various IPM programs in agriculture, forests, and urban settings around the world. However, its regulations (public sector policies and laws) to promote safe and risk-free AI are in their infancy and need careful assessment and evaluation for its broader application. This Special Issue will include original research articles and reviews by leading research entomologists, plant pathologists, weed control specialists, and associated experts. Papers will focus on designing, developing, improving, and implementing AI-based technologies in sustaining food security, human health, and natural resources. Additionally, articles that outline the integration of effective IPM options for a given pest species using AI under climate change patterns in food crops, forestry, and urban areas are particularly welcome.

Dr. Muhammad Haseeb
Guest Editor

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

  • insect detection
  • identification
  • monitoring
  • AI
  • invasive pests
  • pest geographical modeling
  • food production systems
  • forests
  • landscape
  • training
  • IPM

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Published Papers (7 papers)

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Research

26 pages, 4009 KB  
Article
Lightweight Pest Object Detection Model for Complex Economic Forest Tree Scenarios
by Xiaohui Cheng, Xukun Wang, Yanping Kang, Yun Deng, Qiu Lu, Jian Tang, Yuanyuan Shi and Junyu Zhao
Insects 2025, 16(9), 959; https://doi.org/10.3390/insects16090959 - 12 Sep 2025
Viewed by 36
Abstract
Pest control in economic forests is a crucial aspect of sustainable forest resource management, yet it faces bottlenecks such as low efficiency and high miss rates for small objects. Based on the RT-DETR model, this paper proposes LightFAD-DETR, a lightweight architecture integrated with [...] Read more.
Pest control in economic forests is a crucial aspect of sustainable forest resource management, yet it faces bottlenecks such as low efficiency and high miss rates for small objects. Based on the RT-DETR model, this paper proposes LightFAD-DETR, a lightweight architecture integrated with feature aggregation diffusion, designed for complex economic forest scenarios. Firstly, by employing the YOLOv9 lightweight backbone network to compress the computational base load, we introduce the RepNCSPELAN4-CAA module, which integrates re-parameterization techniques and one-dimensional strip convolution. This enhances the model’s ability for cross-regional modeling of slender insect morphologies. Secondly, a feature aggregation diffusion network is designed, incorporating a dimension-aware selective integration mechanism. This dynamically fuses shallow detail features with deep semantic features, effectively mitigating information loss for small objects occluded by foliage. Finally, a re-parameterized batch normalization technique is introduced to reconstruct the AIFI module. Combined with a progressive training strategy, this eliminates redundant parameters, thereby enhancing inference efficiency on edge devices. Experimental validation demonstrates that compared to the baseline RT-DETR model, LightFAD-DETR achieves a 1.4% improvement in mAP0.5:0.95, while reducing parameters by 41.7% and computational load by 35.0%. With an inference speed reaching 106.3 FPS, the method achieves balanced improvements in both accuracy and lightweight design. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) and Insect Pests Management: Securing Food Security, Human Health, and Natural Resources)
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23 pages, 1657 KB  
Article
High-Precision Pest Management Based on Multimodal Fusion and Attention-Guided Lightweight Networks
by Ziye Liu, Siqi Li, Yingqiu Yang, Xinlu Jiang, Mingtian Wang, Dongjiao Chen, Tianming Jiang and Min Dong
Insects 2025, 16(8), 850; https://doi.org/10.3390/insects16080850 - 16 Aug 2025
Viewed by 891
Abstract
In the context of global food security and sustainable agricultural development, the efficient recognition and precise management of agricultural insect pests and their predators have become critical challenges in the domain of smart agriculture. To address the limitations of traditional models that overly [...] Read more.
In the context of global food security and sustainable agricultural development, the efficient recognition and precise management of agricultural insect pests and their predators have become critical challenges in the domain of smart agriculture. To address the limitations of traditional models that overly rely on single-modal inputs and suffer from poor recognition stability under complex field conditions, a multimodal recognition framework has been proposed. This framework integrates RGB imagery, thermal infrared imaging, and environmental sensor data. A cross-modal attention mechanism, environment-guided modality weighting strategy, and decoupled recognition heads are incorporated to enhance the model’s robustness against small targets, intermodal variations, and environmental disturbances. Evaluated on a high-complexity multimodal field dataset, the proposed model significantly outperforms mainstream methods across four key metrics, precision, recall, F1-score, and mAP@50, achieving 91.5% precision, 89.2% recall, 90.3% F1-score, and 88.0% mAP@50. These results represent an improvement of over 6% compared to representative models such as YOLOv8 and DETR. Additional ablation studies confirm the critical contributions of key modules, particularly under challenging scenarios such as low light, strong reflections, and sensor data noise. Moreover, deployment tests conducted on the Jetson Xavier edge device demonstrate the feasibility of real-world application, with the model achieving a 25.7 FPS inference speed and a compact size of 48.3 MB, thus balancing accuracy and lightweight design. This study provides an efficient, intelligent, and scalable AI solution for pest surveillance and biological control, contributing to precision pest management in agricultural ecosystems. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) and Insect Pests Management: Securing Food Security, Human Health, and Natural Resources)
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20 pages, 5993 KB  
Article
High-Precision Stored-Grain Insect Pest Detection Method Based on PDA-YOLO
by Fuyan Sun, Zhizhong Guan, Zongwang Lyu and Shanshan Liu
Insects 2025, 16(6), 610; https://doi.org/10.3390/insects16060610 - 10 Jun 2025
Viewed by 1071
Abstract
Effective stored-grain insect pest detection is crucial in grain storage management to prevent economic losses and ensure food security throughout production and supply chains. Existing detection methods suffer from issues such as high labor costs, environmental interference, high equipment costs, and inconsistent performance. [...] Read more.
Effective stored-grain insect pest detection is crucial in grain storage management to prevent economic losses and ensure food security throughout production and supply chains. Existing detection methods suffer from issues such as high labor costs, environmental interference, high equipment costs, and inconsistent performance. To address these limitations, we proposed PDA-YOLO, an improved stored-grain insect pest detection algorithm based on YOLO11n which integrates three key modules: PoolFormer_C3k2 (PF_C3k2) for efficient local feature extraction, Attention-based Intra-Scale Feature Interaction (AIFI) for enhanced global context awareness, and Dynamic Multi-scale Aware Edge (DMAE) for precise boundary detection of small targets. Trained and tested on 6200 images covering five common stored-grain insect pests (Lesser Grain Borer, Red Flour Beetle, Indian Meal Moth, Maize Weevil, and Angoumois Grain Moth), PDA-YOLO achieved an mAP@0.5 of 96.6%, mAP@0.5:0.95 of 60.4%, and F1 score of 93.5%, with a computational cost of only 6.9 G and mean detection time of 9.9 ms per image. These results demonstrate the advantages over mainstream detection algorithms, balancing accuracy, computational efficiency, and real-time performance. PDA-YOLO provides a reference for pest detection in intelligent grain storage management. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) and Insect Pests Management: Securing Food Security, Human Health, and Natural Resources)
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22 pages, 8365 KB  
Article
RDW-YOLO: A Deep Learning Framework for Scalable Agricultural Pest Monitoring and Control
by Jiaxin Song, Ke Cheng, Fei Chen and Xuecheng Hua
Insects 2025, 16(5), 545; https://doi.org/10.3390/insects16050545 - 21 May 2025
Cited by 1 | Viewed by 950
Abstract
Due to target diversity, life-cycle variations, and complex backgrounds, traditional pest detection methods often struggle with accuracy and efficiency. This study introduces RDW-YOLO, an improved pest detection algorithm based on YOLO11, featuring three key innovations. First, the Reparameterized Dilated Fusion Block (RDFBlock) enhances [...] Read more.
Due to target diversity, life-cycle variations, and complex backgrounds, traditional pest detection methods often struggle with accuracy and efficiency. This study introduces RDW-YOLO, an improved pest detection algorithm based on YOLO11, featuring three key innovations. First, the Reparameterized Dilated Fusion Block (RDFBlock) enhances feature extraction via multi-branch dilated convolutions for fine-grained pest characteristics. Second, the DualPathDown (DPDown) module integrates hybrid pooling and convolution for better multi-scale adaptability. Third, an enhanced Wise-Wasserstein IoU (WWIoU) loss function optimizes the matching mechanism and improves bounding-box regression. Experiments on the enhanced IP102 dataset show that RDW-YOLO achieves an mAP@0.5 of 71.3% and an mAP@0.5:0.95 of 50.0%, surpassing YOLO11 by 3.1% and 2.0%, respectively. The model also adopts a lightweight design and has a computational complexity of 5.6 G, ensuring efficient deployment without sacrificing accuracy. These results highlight RDW-YOLO’s potential for precise and efficient pest detection in sustainable agriculture. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) and Insect Pests Management: Securing Food Security, Human Health, and Natural Resources)
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17 pages, 6113 KB  
Article
On the Study of Joint YOLOv5-DeepSort Detection and Tracking Algorithm for Rhynchophorus ferrugineus
by Shuai Wu, Jianping Wang, Wei Wei, Xiangchuan Ji, Bin Yang, Danyang Chen, Huimin Lu and Li Liu
Insects 2025, 16(2), 219; https://doi.org/10.3390/insects16020219 - 17 Feb 2025
Cited by 2 | Viewed by 1415
Abstract
The Red Palm Weevil (RPW, Rhynchophorus ferrugineus) is a destructive pest of palm plants that can cause the death of the entire plant when infested. To enhance the efficiency of RPW control, a novel detection and tracking algorithm based on the joint [...] Read more.
The Red Palm Weevil (RPW, Rhynchophorus ferrugineus) is a destructive pest of palm plants that can cause the death of the entire plant when infested. To enhance the efficiency of RPW control, a novel detection and tracking algorithm based on the joint YOLOv5-DeepSort algorithm is proposed. Firstly, the original YOLOv5 is improved by adding a small object detection layer and an attention mechanism. At the same time, the detector of the original DeepSort is changed to the improved YOLOv5. Then, a historical frame data module is introduced into DeepSort to reduce the number of target identity (ID) switches while maintaining detection and tracking accuracy. Finally, an experiment is conducted to evaluate the joint YOLOv5-DeepSort detection and tracking algorithm. The experimental results show that, in terms of detectors, the improved YOLOv5 model achieves a mean average precision (mAP@.5) of 90.1% and a precision (P) of 93.8%. In terms of tracking performance, the joint YOLOv5-DeepSort algorithm achieves a Multiple Object Tracking Accuracy (MOTA) of 94.3%, a Multiple Object Tracking Precision (MOTP) of 90.14%, reduces ID switches by 33.3%, and realizes a count accuracy of 94.1%. These results demonstrate that the improved algorithm meets the practical requirements for RPW field detection and tracking. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) and Insect Pests Management: Securing Food Security, Human Health, and Natural Resources)
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17 pages, 8635 KB  
Article
PM-YOLO: A Powdery Mildew Automatic Grading Detection Model for Rubber Tree
by Yuheng Li, Qian Chen, Jiazheng Zhu, Zengping Li, Meng Wang and Yu Zhang
Insects 2024, 15(12), 937; https://doi.org/10.3390/insects15120937 - 28 Nov 2024
Cited by 4 | Viewed by 1149
Abstract
Powdery mildew has become a significant disease affecting the yield and quality of rubber trees in recent years. It typically manifests on the leaf surface at an early stage, rapidly infecting and spreading throughout the leaves. Therefore, early detection and intervention are essential [...] Read more.
Powdery mildew has become a significant disease affecting the yield and quality of rubber trees in recent years. It typically manifests on the leaf surface at an early stage, rapidly infecting and spreading throughout the leaves. Therefore, early detection and intervention are essential to reduce the resulting losses due to this disease. However, the conventional methods of disease detection are both time-consuming and labor-intensive. In this study, we proposed a novel deep-learning-based approach for detecting powdery mildew in rubber trees, even in complex backgrounds. First, to address the lack of existing datasets on rubber tree powdery mildew, we constructed a dataset comprising 6200 images and 38,000 annotations. Second, based on the YOLO framework, we integrated a multi-scale fusion module that combines a Feature Focus and Diffusion Mechanism (FFDM) into the neck of the detection architecture. We designed an overall focus diffusion architecture and introduced a Dimension-Aware Selective Integration (DASI) module to enhance the detection of small powdery mildew targets, naming the model PM-YOLO. Furthermore, we proposed an automatic grading detection algorithm to evaluate the severity of powdery mildew on rubber tree leaves. The experimental results demonstrated that the proposed method achieved 86.9% mean average precision (mAP) and 85.6% recall, which outperformed the standard YOLOv10 by 7.6% mAP and 8.2% recall. This approach offered accurate and real-time detection of powdery mildew rubber trees, providing an effective solution for early diagnosis through automated grading. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) and Insect Pests Management: Securing Food Security, Human Health, and Natural Resources)
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22 pages, 13240 KB  
Article
SpemNet: A Cotton Disease and Pest Identification Method Based on Efficient Multi-Scale Attention and Stacking Patch Embedding
by Keyuan Qiu, Yingjie Zhang, Zekai Ren, Meng Li, Qian Wang, Yiqiang Feng and Feng Chen
Insects 2024, 15(9), 667; https://doi.org/10.3390/insects15090667 - 2 Sep 2024
Cited by 10 | Viewed by 2380
Abstract
We propose a cotton pest and disease recognition method, SpemNet, based on efficient multi-scale attention and stacking patch embedding. By introducing the SPE module and the EMA module, we successfully solve the problems of local feature learning difficulty and insufficient multi-scale feature integration [...] Read more.
We propose a cotton pest and disease recognition method, SpemNet, based on efficient multi-scale attention and stacking patch embedding. By introducing the SPE module and the EMA module, we successfully solve the problems of local feature learning difficulty and insufficient multi-scale feature integration in the traditional Vision Transformer model, which significantly improve the performance and efficiency of the model. In our experiments, we comprehensively validate the SpemNet model on the CottonInsect dataset, and the results show that SpemNet performs well in the cotton pest recognition task, with significant effectiveness and superiority. The SpemNet model excels in key metrics such as precision and F1 score, demonstrating significant potential and superiority in the cotton pest and disease recognition task. This study provides an efficient and reliable solution in the field of cotton pest and disease identification, which is of great theoretical and applied significance. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) and Insect Pests Management: Securing Food Security, Human Health, and Natural Resources)
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