ReviewFrontiers in plant science2024
Revolutionizing agriculture with artificial intelligence: plant disease detection methods, applications, and their limitations.
Review in Frontiers in plant science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
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Who cites it
38 citing papers in PubMed, 216 citations in OpenAlex.
- Artificial intelligence in plant salt stress research: from predictive models to multi-omics integration.Journal of experimental botany · 2026Review
- GreenAid: a confidence-weighted ensemble deep learning system for real-time plant disease detection and management.Scientific reports · 2026Article
- HAMNet: Hierarchical Multi-scale Attention Network for precise disease detection in pearl millet using spatial fusion.Scientific reports · 2026Article
- RustMAE: A Spatiotemporal Transformer for Short- to Medium-Term Warning of Wheat Stripe Rust Spring Spread.iScience · 2026Article
- A hybrid convolution and attention-based framework with visual explanation for fruit disease identification.Scientific reports · 2026Article
- Artificial Intelligence (AI) in Detection of Abiotic Stress in Plants: A Review.Sensors (Basel, Switzerland) · 2026Review
- A Large-Scale In-the-wild Dataset for Plant Disease Segmentation.Scientific data · 2026Article
- A Survey of Crop Disease Recognition Methods Based on Spectral and RGB Images.Journal of imaging · 2026Review
- IoT-RiceMobileNet: An improved lightweight MobileNetV2 Model for real-time multi-class rice disease detection using IoT.PloS one · 2026Article
- A resource-efficient framework for plant disease classification: integrating reduced-order modeling with treatment-based label engineering.Frontiers in plant science · 2026Article
- A multi-scale parallel weighted fusion dynamic attention method for citrus leaf disease recognitions.Frontiers in plant science · 2026Article
- A curated dataset and lightweight deep learning framework for tea leaf disease classification.PloS one · 2026Article
- From detection accuracy to safety assurance in intelligent plant health early warning systems.Frontiers in plant science · 2026Review
- AI-driven drone technology and computer vision for early detection of crop disease in large agricultural areas.Scientific reports · 2025Article
- Classification of cotton leaf disease using YOLOv8 based k-fold cross validation deep learning method for precision agriculture.Scientific reports · 2025Article
- Climate-Resilient Crops: Integrating AI, Multi-Omics, and Advanced Phenotyping to Address Global Agricultural and Societal Challenges.Plants (Basel, Switzerland) · 2025Review
- A Method for Automated Detection of Chicken Coccidia in Vaccine Environments.Veterinary sciences · 2025Article
- 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
- Wheat Soil-Borne Mosaic Virus Disease Detection: A Perspective of Agricultural Decision-Making via Spectral Clustering and Multi-Indicator Feedback.Plants (Basel, Switzerland) · 2025Article
- Viral tropism in plants, reproductive tissues, and seeds.Archives of microbiology · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Accurate and rapid plant disease detection is critical for enhancing long-term agricultural yield. Disease infection poses the most significant challenge in crop production, potentially leading to economic losses. Viruses, fungi, bacteria, and other infectious organisms can affect numerous plant parts, including roots, stems, and leaves. Traditional techniques for plant disease detection are time-consuming, require expertise, and are resource-intensive. Therefore, automated leaf disease diagnosis using artificial intelligence (AI) with Internet of Things (IoT) sensors methodologies are considered for the analysis and detection. This research examines four crop diseases: tomato, chilli, potato, and cucumber. It also highlights the most prevalent diseases and infections in these four types of vegetables, along with their symptoms. This review provides detailed predetermined steps to predict plant diseases using AI. Predetermined steps include image acquisition, preprocessing, segmentation, feature selection, and classification. Machine learning (ML) and deep understanding (DL) detection models are discussed. A comprehensive examination of various existing ML and DL-based studies to detect the disease of the following four crops is discussed, including the datasets used to evaluate these studies. We also provided the list of plant disease detection datasets. Finally, different ML and DL application problems are identified and discussed, along with future research prospects, by combining AI with IoT platforms like smart drones for field-based disease detection and monitoring. This work will help other practitioners in surveying different plant disease detection strategies and the limits of present systems.
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Registered trials
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 Socratic graph.