ReviewFrontiers in plant science2019
Convolutional Neural Networks for the Automatic Identification of Plant Diseases.
Review in Frontiers in plant science, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 59 papers.
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Who cites it
59 citing papers in PubMed.
- StyleGAN3-T: an alias-free generative framework for synthetic plant disease image augmentation and recognition.Scientific reports · 2026Article
- SLR-YOLO: An Improved YOLO-Based Method for Accurate Detection of Potato Leaf Diseases in Complex Field Images.Plants (Basel, Switzerland) · 2026Article
- Artificial Intelligence-Driven Plant Disease Detection and Diagnosis: A Comprehensive Review of Deep Learning Approaches, Multimodal Sensing Technologies, and Future Perspectives in Precision Agriculture.The plant pathology journal · 2026Review
- A resource-efficient framework for plant disease classification: integrating reduced-order modeling with treatment-based label engineering.Frontiers in plant science · 2026Article
- Classification of tobacco leaf diseases based on multi-source remote sensing data.Frontiers in plant science · 2026Article
- Design and implementation of an intelligent system for smart garden watering using fuzzy logic.Frontiers in artificial intelligence · 2026Article
- Quantifying the reliability gap in cross-domain plant disease classification: benchmarking the limited efficacy of standard mitigation techniques under controlled-to-field shift.Frontiers in plant science · 2026Article
- TB-YOLOv8n: an improved YOLOv8n-based model for tip-burn detection in plant factory-grown pakchoi under LED lighting.Frontiers in plant science · 2026Article
- A curated dataset and lightweight deep learning framework for tea leaf disease classification.PloS one · 2026Article
- CATR: CNN augmented transformer for object detection in remote sensing imagery.Scientific reports · 2025Article
- Hyperspectral image analysis for classification of multiple infections in wheat.Plant methods · 2025Article
- Machine learning in biological research: key algorithms, applications, and future directions.BMC biology · 2025Review
- A fine tuned EfficientNet-B0 convolutional neural network for accurate and efficient classification of apple leaf diseases.Scientific reports · 2025Article
- Cutting-edge computational approaches to plant phenotyping.Plant molecular biology · 2025Review
- A high-throughput ResNet CNN approach for automated grapevine leaf hair quantification.Scientific reports · 2025Article
- A rapid approach for discriminatingFrontiers in chemistry · 2025Article
- Soil Moisture and Heat Level Prediction for Plant Health Monitoring Using Deep Learning with Gannet Namib Beetle Optimization in IoT.Applied biochemistry and biotechnology · 2024Article
- Article
- ScabyNet, a user-friendly application for detecting common scab in potato tubers using deep learning and morphological traits.Scientific reports · 2024Article
- Leveraging three-tier deep learning model for environmental cleaner plants production.Scientific reports · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Deep learning techniques, and in particular Convolutional Neural Networks (CNNs), have led to significant progress in image processing. Since 2016, many applications for the automatic identification of crop diseases have been developed. These applications could serve as a basis for the development of expertise assistance or automatic screening tools. Such tools could contribute to more sustainable agricultural practices and greater food production security. To assess the potential of these networks for such applications, we survey 19 studies that relied on CNNs to automatically identify crop diseases. We describe their profiles, their main implementation aspects and their performance. Our survey allows us to identify the major issues and shortcomings of works in this research area. We also provide guidelines to improve the use of CNNs in operational contexts as well as some directions for future research.
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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 OpenQuestion graph.