ArticleFrontiers in plant science2019
Millimeter-Level Plant Disease Detection From Aerial Photographs
Article 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 17 papers.
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Who cites it
17 citing papers in PubMed.
- Ground Mobile Robots for High-Throughput Plant Phenotyping: A Review from the Closed-Loop Perspective of Perception, Decision, and Action.Plants (Basel, Switzerland) · 2026Review
- Article
- A review of deep learning architectures for plant disease detection.Turkish journal of biology = Turk biyoloji dergisi · 2025Review
- Global Genotype by Environment Prediction Competition Reveals That Diverse Modeling Strategies Can Deliver Satisfactory Maize Yield Estimates.bioRxiv : the preprint server for biology · 2024Article
- An efficient deep learning model for tomato disease detection.Plant methods · 2024Article
- Spatio-temporal modeling of high-throughput multispectral aerial images improves agronomic trait genomic prediction in hybrid maize.Genetics · 2024Article
- TomatoGuard-YOLO: a novel efficient tomato disease detection method.Frontiers in plant science · 2024Article
- Leveraging three-tier deep learning model for environmental cleaner plants production.Scientific reports · 2023Article
- An advanced deep learning models-based plant disease detection: A review of recent research.Frontiers in plant science · 2023Review
- A survey on deep learning-based identification of plant and crop diseases from UAV-based aerial images.Cluster computing · 2023Article
- Importance of Meteorological Parameters and Airborne Conidia to Predict Risk ofSensors (Basel, Switzerland) · 2022Article
- Sensor-based phenotyping of above-ground plant-pathogen interactions.Plant methods · 2022Review
- High-throughput field crop phenotyping: current status and challenges.Breeding science · 2022Article
- Enhancing Wheat Disease Diagnosis in a Greenhouse Using Image Deep Features and Parallel Feature Fusion.Frontiers in plant science · 2022Article
- Machine Learning in Agriculture: A Comprehensive Updated Review.Sensors (Basel, Switzerland) · 2021Review
- Plant diseases and pests detection based on deep learning: a review.Plant methods · 2021Review
- Convolutional Neural Networks for Image-Based High-Throughput Plant Phenotyping: A Review.Plant phenomics (Washington, D.C.) · 2020Review
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Computer vision models that can recognize plant diseases in the field would be valuable tools for disease management and resistance breeding. Generating enough data to train these models is difficult, however, since only trained experts can accurately identify symptoms. In this study, we describe and implement a two-step method for generating a large amount of high-quality training data with minimal expert input. First, experts located symptoms of northern leaf blight (NLB) in field images taken by unmanned aerial vehicles (UAVs), annotating them quickly at low resolution. Second, non-experts were asked to draw polygons around the identified diseased areas, producing high-resolution ground truths that were automatically screened based on agreement between multiple workers. We then used these crowdsourced data to train a convolutional neural network (CNN), feeding the output into a conditional random field (CRF) to segment images into lesion and non-lesion regions with accuracy of 0.9979 and F1 score of 0.7153. The CNN trained on crowdsourced data showed greatly improved spatial resolution compared to one trained on expert-generated data, despite using only one fifth as many expert annotations. The final model was able to accurately delineate lesions down to the millimeter level from UAV-collected images, the finest scale of aerial plant disease detection achieved to date. The two-step approach to generating training data is a promising method to streamline deep learning approaches for plant disease detection, and for complex plant phenotyping tasks in general.
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