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

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

Deadline for manuscript submissions: closed (31 October 2025) | Viewed by 22853

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Special Issue Editor

Dr. Muhammad Haseeb

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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 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-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 (12 papers)

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25 pages, 2831 KB

Open AccessArticle

Lightweight Vision–Transformer Network for Early Insect Pest Identification in Greenhouse Agricultural Environments

by Wenjie Hong, Shaozu Ling, Pinrui Zhu, Zihao Wang, Ruixiang Zhao, Yunpeng Liu and Min Dong

Insects 2026, 17(1), 74; https://doi.org/10.3390/insects17010074 - 8 Jan 2026

Viewed by 645

Abstract

This study addresses the challenges of early recognition of fruit and vegetable diseases and pests in facility horticultural greenhouses and the difficulty of real-time deployment on edge devices, and proposes a lightweight cross-scale intelligent recognition network, Light-HortiNet, designed to achieve a balance between high accuracy and high efficiency for automated greenhouse pest and disease detection. The method is built upon a lightweight Mobile-Transformer backbone and integrates a cross-scale lightweight attention mechanism, a small-object enhancement branch, and an alternative block distillation strategy, thereby effectively improving robustness and stability under complex illumination, high-humidity environments, and small-scale target scenarios. Systematic experimental evaluations were conducted on a greenhouse pest and disease dataset covering crops such as tomato, cucumber, strawberry, and pepper. The results demonstrate significant advantages in detection performance, with mAP@50 reaching

0.872

, mAP@50:95 reaching

0.561

, classification accuracy reaching

0.894

, precision reaching

0.886

, recall reaching

0.879

, and F1-score reaching

0.882

, substantially outperforming mainstream lightweight models such as YOLOv8n, YOLOv11n, MobileNetV3, and Tiny-DETR. In terms of small-object recognition capability, the model achieved an mAP-small of

0.536

and a recall-small of

0.589

, markedly enhancing detection stability for micro pests such as whiteflies and thrips as well as early-stage disease lesions. In addition, real-time inference performance exceeding 20 FPS was achieved on edge platforms such as Jetson Nano, demonstrating favorable deployment adaptability. 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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25 pages, 3819 KB

Open AccessArticle

Cross-Modal and Contrastive Optimization for Explainable Multimodal Recognition of Predatory and Parasitic Insects

by Mingyu Liu, Liuxin Wang, Ruihao Jia, Shiyu Ji, Yalin Wu, Yuxin Wu, Luozehan Xie and Min Dong

Insects 2025, 16(12), 1187; https://doi.org/10.3390/insects16121187 - 22 Nov 2025

Viewed by 893

Abstract

Natural enemies play a vital role in pest suppression and ecological balance within agricultural ecosystems. However, conventional vision-based recognition methods are highly susceptible to illumination variation, occlusion, and background noise in complex field environments, making it difficult to accurately distinguish morphologically similar species. To address these challenges, a multimodal natural enemy recognition and ecological interpretation framework, termed MAVC-XAI, is proposed to enhance recognition accuracy and ecological interpretability in real-world agricultural scenarios. The framework employs a dual-branch spatiotemporal feature extraction network for deep modeling of both visual and acoustic signals, introduces a cross-modal sampling attention mechanism for dynamic inter-modality alignment, and incorporates cross-species contrastive learning to optimize inter-class feature boundaries. Additionally, an explainable generation module is designed to provide ecological visualizations of the model’s decision-making process in both visual and acoustic domains. Experiments conducted on multimodal datasets collected across multiple agricultural regions confirm the effectiveness of the proposed approach. The MAVC-XAI framework achieves an accuracy of 0.938, a precision of 0.932, a recall of 0.927, an F1-score of 0.929, an mAP@50 of 0.872, and a Top-5 recognition rate of 97.8%, all significantly surpassing unimodal models such as ResNet, Swin-T, and VGGish, as well as multimodal baselines including MMBT and ViLT. Ablation experiments further validate the critical contributions of the cross-modal sampling attention and contrastive learning modules to performance enhancement. The proposed framework not only enables high-precision natural enemy identification under complex ecological conditions but also provides an interpretable and intelligent foundation for AI-driven ecological pest management and food security monitoring. 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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27 pages, 4104 KB

Open AccessArticle

CropCLR-Wheat: A Label-Efficient Contrastive Learning Architecture for Lightweight Wheat Pest Detection

by Yan Wang, Chengze Li, Chenlu Jiang, Mingyu Liu, Shengzhe Xu, Binghua Yang and Min Dong

Insects 2025, 16(11), 1096; https://doi.org/10.3390/insects16111096 - 25 Oct 2025

Viewed by 1530

Abstract

To address prevalent challenges in field-based wheat pest recognition—namely, viewpoint perturbations, sample scarcity, and heterogeneous data distributions—a pest identification framework named CropCLR-Wheat is proposed, which integrates self-supervised contrastive learning with an attention-enhanced mechanism. By incorporating a viewpoint-invariant feature encoder and a diffusion-based feature filtering module, the model significantly enhances pest damage localization and feature consistency, enabling high-accuracy recognition under limited-sample conditions. In 5-shot classification tasks, CropCLR-Wheat achieves a precision of 89.4%, a recall of 87.1%, and an accuracy of 88.2%; these metrics further improve to 92.3%, 90.5%, and 91.2%, respectively, under the 10-shot setting. In the semantic segmentation of wheat pest damage regions, the model attains a mean intersection over union (mIoU) of 82.7%, with precision and recall reaching 85.2% and 82.4%, respectively, markedly outperforming advanced models such as SegFormer and Mask R-CNN. In robustness evaluation under viewpoint disturbances, a prediction consistency rate of 88.7%, a confidence variation of only 7.8%, and a prediction consistency score (PCS) of 0.914 are recorded, indicating strong stability and adaptability. Deployment results further demonstrate the framework’s practical viability: on the Jetson Nano device, an inference latency of 84 ms, a frame rate of 11.9 FPS, and an accuracy of 88.2% are achieved. These results confirm the efficiency of the proposed approach in edge computing environments. By balancing generalization performance with deployability, the proposed method provides robust support for intelligent agricultural terminal systems and holds substantial potential for wide-scale application. 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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26 pages, 4009 KB

Open AccessArticle

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

Cited by 2 | Viewed by 1389

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 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

{m A P}_{0.5 : 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

Open AccessArticle

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

Cited by 3 | Viewed by 2128

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 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

Open AccessArticle

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

Cited by 6 | Viewed by 2368

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. 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

Open AccessArticle

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 5 | Viewed by 1873

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 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

Open AccessArticle

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 4 | Viewed by 2055

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 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

Open AccessArticle

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 6 | Viewed by 1883

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 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

Open AccessArticle

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 17 | Viewed by 3117

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 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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Review

Jump to: Research

21 pages, 12413 KB

Open AccessReview

The Evolution of Modeling Approaches: From Statistical Models to Deep Learning for Locust and Grasshopper Forecasting

by Wei Sui, Jing Wang, Dan Miao, Yijie Jiang, Guojun Liu, Shujian Yang, Wei You, Zhi Li, Xiaojing Wu and Hu Meng

Insects 2026, 17(2), 182; https://doi.org/10.3390/insects17020182 - 8 Feb 2026

Viewed by 558

Abstract

Locust outbreaks cause a significant threat to global food security and ecosystem stability, with particularly severe consequences in grassland regions, where grasshoppers also exert considerable ecological pressure. In comparison to grasshoppers, locusts typically occur at much larger spatial scales, as their strong migratory ability and collective movement behavior lead to greater spatial connectivity and autocorrelation. The forecasting of both locust and grasshopper outbreaks remains a formidable scientific challenge, primarily due to the complex, nonlinear spatiotemporal interactions among environmental drivers such as weather, vegetation, and soil conditions. This review compares the evolution of prediction methodologies for locust and grasshopper outbreaks, focusing on the application of deep learning (DL) methods to ecological forecasting tasks. It traces the development from traditional statistical models to classical machine learning, and ultimately to DL, assessing the strengths and limitations of key DL architectures—including Deep Neural Networks (DNNs), Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, and Gated Recurrent Units (GRUs)—in modeling the intricate dynamics of locust populations. While most studies have concentrated on locust outbreaks, this review emphasizes the adaptation of these models to grassland ecosystems, such as those in Inner Mongolia, where grasshopper outbreaks exhibit similarities to locust plagues but have been largely overlooked in DL research. Despite the potential of DL, challenges such as data scarcity, limited model generalizability across regions, and the “black box” issue of low interpretability remain. To address these issues, we propose future research directions that integrate Explainable AI (XAI), transfer learning, and generative models like GANs to development more robust, transparent, and ecologically grounded forecasting tools. By promoting the use of efficient architectures like GRUs within customized frameworks, this review aims to guide the development of effective early warning systems for sustainable locust management in vulnerable grassland 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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41 pages, 1831 KB

Open AccessReview

Next-Generation Precision Breeding in Peanut (Arachis hypogaea L.) for Disease and Pest Resistance: From Multi-Omics to AI-Driven Innovations

by Xue Pei, Jinhui Xie, Chunhao Liang and Aleksandra O. Utkina

Insects 2026, 17(1), 63; https://doi.org/10.3390/insects17010063 - 4 Jan 2026

Viewed by 1356

Abstract

Peanut (Arachis hypogaea L.) is a globally important oilseed and food legume, yet its productivity is persistently constrained by devastating diseases and insect pests that thrive under changing climates. This review aims to provide a comprehensive synthesis of advances in precision breeding and molecular approaches for enhancing disease and pest resistance in peanut. Traditional control measures ranging from crop rotation and cultural practices to chemical protection have delivered only partial and often unsustainable relief. The narrow genetic base of cultivated peanut and its complex allotetraploid genome further hinder the introgression of durable resistance. Recent advances in precision breeding are redefining the possibilities for resilient peanut improvement. Multi-omics platforms genomics, transcriptomics, proteomics, and metabolomics have accelerated the identification of resistance loci, effector-triggered immune components, and molecular cross-talk between pathogen, pest, and host responses. Genome editing tools such as CRISPR-Cas systems now enable the precise modification of susceptibility genes and defense regulators, overcoming barriers of conventional breeding. Integration of these molecular innovations with phenomics, machine learning, and remote sensing has transformed resistance screening from manual assessment to real-time, data-driven prediction. Such AI-assisted breeding pipelines promise enhanced selection accuracy and faster deployment of multi-stress-tolerant cultivars. This review outlines current progress, technological frontiers, and persisting gaps in leveraging precision breeding for disease and pest resistance in peanut, outlining a roadmap toward climate-resilient, sustainable production systems. 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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