ArticleFrontiers in plant science2017
X-FIDO: An Effective Application for Detecting Olive Quick Decline Syndrome with Deep Learning and Data Fusion.
Article in Frontiers in plant science, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Dataset of Ash gourd plant leaf images for detection and classification.Data in brief · 2025Article
- A survey: to identify plant leaf diseases by feature extraction methods and classification techniques.Planta · 2025Review
- Multi-Omic Advances in Olive Tree (Biology · 2025Review
- Recent Methods for Evaluating Crop Water Stress Using AI Techniques: A Review.Sensors (Basel, Switzerland) · 2024Review
- A novel ensemble learning method for crop leaf disease recognition.Frontiers in plant science · 2023Article
- Rice pest identification based on multi-scale double-branch GAN-ResNet.Frontiers in plant science · 2023Article
- OSC-COFrontiers in plant science · 2023Article
- Integration of Innovative Technologies in the Agri-Food Sector: The Fundamentals and Practical Case of DNA-Based Traceability of Olives from Fruit to Oil.Plants (Basel, Switzerland) · 2022Review
- Data Fusion in Agriculture: Resolving Ambiguities and Closing Data Gaps.Sensors (Basel, Switzerland) · 2022Review
- CASM-AMFMNet: A Network Based on Coordinate Attention Shuffle Mechanism and Asymmetric Multi-Scale Fusion Module for Classification of Grape Leaf Diseases.Frontiers in plant science · 2022Article
- A Survey of Deep Convolutional Neural Networks Applied for Prediction of Plant Leaf Diseases.Sensors (Basel, Switzerland) · 2021Review
- AI-Driven Framework for Recognition of Guava Plant Diseases through Machine Learning from DSLR Camera Sensor Based High Resolution Imagery.Sensors (Basel, Switzerland) · 2021Article
- A Novel Computational Framework for Precision Diagnosis and Subtype Discovery of Plant With Lesion.Frontiers in plant science · 2021Article
- Review of the State of the Art of Deep Learning for Plant Diseases: A Broad Analysis and Discussion.Plants (Basel, Switzerland) · 2020Review
- Convolutional Neural Networks for Image-Based High-Throughput Plant Phenotyping: A Review.Plant phenomics (Washington, D.C.) · 2020Review
- Improved Vision-Based Detection of Strawberry Diseases Using a Deep Neural Network.Frontiers in plant science · 2020Article
- ThePathogens (Basel, Switzerland) · 2019Article
- Deep Learning-Based Phenotyping System With Glocal Description of Plant Anomalies and Symptoms.Frontiers in plant science · 2019Article
- Convolutional Neural Networks for the Automatic Identification of Plant Diseases.Frontiers in plant science · 2019Review
- Computer vision-based phenotyping for improvement of plant productivity: a machine learning perspective.GigaScience · 2019Review
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We have developed a vision-based program to detect symptoms of Olive Quick Decline Syndrome (OQDS) on leaves of
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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.