ReviewFrontiers in plant science2025
Leveraging deep learning for plant disease and pest detection: a comprehensive review and future directions.
Review in Frontiers in plant science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- Controlled-release gel encapsulation: an emerging technology for delivering beneficial microorganisms in sustainable plant disease management.Crop health · 2026Review
- Sunn Pest Optimization (SPO): A Phase-Differentiated Bio-Inspired Metaheuristic for Global Optimization with Application to Precision Agriculture Monitoring Networks.Biomimetics (Basel, Switzerland) · 2026Article
- MoCoProto: Enhancing few-shot pest image classification with self-supervised representation learning.Plant phenomics (Washington, D.C.) · 2026Article
- Machine learning-enabled chemical ecology for integrated pest management: from volatiles to field applications.Journal of insect science (Online) · 2026Review
- Review
- EP-YOLO: An Enhanced Lightweight Model for Micro-Pest Detection in Agricultural Light-Trap Environments.Sensors (Basel, Switzerland) · 2026Article
- Lightweight attention enhanced YOLOv11 for accurate multi class detection of brinjal diseases.Scientific reports · 2026Article
- Proposal for Computationally Efficient Fog Computing System for Coffee Berry Borer Detection via Optimized YOLOv26.Sensors (Basel, Switzerland) · 2026Article
- Enhancing plant disease detection through multi-modal integration of visual and textual data.Plant methods · 2026Article
- Detection of Soil-Borne Pathogens Using Fine-Tuned Deep Learning Models: A Case Study on the Soybean Cyst Nematode (Heterodera glycines Ichinohe).The plant pathology journal · 2026Article
- Article
- From detection accuracy to safety assurance in intelligent plant health early warning systems.Frontiers in plant science · 2026Review
- FD-DEIM: efficient and robust detection of small, irregular foliar lesions in the field.Frontiers in plant science · 2026Article
- Artificial intelligence-Driven detection and decision support system for precision management of maize downy mildew.PloS one · 2026Article
- TDS-YOLO: a lightweight detection model for fine-grained segmentation of tea leaf diseases.Frontiers in plant science · 2026Article
- A paradigm-level evaluation of deep learning architectures for plant pest and disease recognition under data-scarce field conditions.Frontiers in plant science · 2026Article
- Explainable deep learning-based comparative study for guava fruit and leaf disease classification: advancing agricultural diagnostics through AI.Frontiers in plant science · 2026Article
- DLCPD-25: A Large-Scale and Diverse Dataset for Crop Disease and Pest Recognition.Sensors (Basel, Switzerland) · 2025Article
- A bibliometric review of triazine hybrids: synthesis, reactions, and applications spanning the last quarter-century (2000-2025).RSC advances · 2025Review
- Sparse-MoE-SAM: A Lightweight Framework Integrating MoE and SAM with a Sparse Attention Mechanism for Plant Disease Segmentation in Resource-Constrained Environments.Plants (Basel, Switzerland) · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
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Abstract
Plant diseases and pests pose significant threats to crop yield and quality, prompting the exploration of digital image processing techniques for their detection. Recent advancements in deep learning models have shown remarkable progress in this domain, outperforming traditional methods across various fronts including classification, detection, and segmentation networks. This review delves into recent research endeavors focused on leveraging deep learning for detecting plant and pest diseases, reflecting a burgeoning interest among researchers in artificial intelligence-driven approaches for agricultural analysis. The study begins by elucidating the limitations of conventional detection methods, setting the stage for exploring the challenges and opportunities inherent in deploying deep learning in real-world applications for plant disease and pest infestation detection. Moreover, the review offers insights into potential solutions while critically analyzing the obstacles encountered. Furthermore, it conducts a meticulous examination and prognostication of the trajectory of deep learning models in plant disease and pest infestation detection. Through this comprehensive analysis, the review seeks to provide a nuanced understanding of the evolving landscape and prospects in this vital area of agricultural research. The review highlights that state-of-the-art deep learning models have achieved impressive accuracies, with classification tasks often exceeding 95% and detection and segmentation networks demonstrating precision rates above 90% in identifying plant diseases and pest infestations. These findings underscore the transformative potential of deep learning in revolutionizing agricultural diagnostics.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.