Millimeter-Level Plant Disease Detection From Aerial Photographs via Deep Learning and Crowdsourced Data
Published 2019 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Millimeter-Level Plant Disease Detection From Aerial Photographs via Deep Learning and Crowdsourced Data
Authors
Keywords
-
Journal
Frontiers in Plant Science
Volume 10, Issue -, Pages -
Publisher
Frontiers Media SA
Online
2019-12-12
DOI
10.3389/fpls.2019.01550
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Applications of Computer Vision in Plant Pathology: A Survey
- (2019) Siddharth Singh Chouhan et al. ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING
- A low shot learning method for tea leaf’s disease identification
- (2019) Gensheng Hu et al. COMPUTERS AND ELECTRONICS IN AGRICULTURE
- Quantitative Phenotyping of Northern Leaf Blight in UAV Images Using Deep Learning
- (2019) Ethan L. Stewart et al. Remote Sensing
- Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists
- (2018) H A Haenssle et al. ANNALS OF ONCOLOGY
- Deep convolutional neural networks for mobile capture device-based crop disease classification in the wild
- (2018) Artzai Picon et al. COMPUTERS AND ELECTRONICS IN AGRICULTURE
- An explainable deep machine vision framework for plant stress phenotyping
- (2018) Sambuddha Ghosal et al. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
- Crowdsourcing image analysis for plant phenomics to generate ground truth data for machine learning
- (2018) Naihui Zhou et al. PLoS Computational Biology
- Deep leaning approach with colorimetric spaces and vegetation indices for vine diseases detection in UAV images
- (2018) Mohamed Kerkech et al. COMPUTERS AND ELECTRONICS IN AGRICULTURE
- Perspectives for Remote Sensing with Unmanned Aerial Vehicles in Precision Agriculture
- (2018) Wouter H. Maes et al. TRENDS IN PLANT SCIENCE
- Identification of Soybean Foliar Diseases Using Unmanned Aerial Vehicle Images
- (2017) Everton Castelao Tetila et al. IEEE Geoscience and Remote Sensing Letters
- Deep convolutional neural network for classifying Fusarium wilt of radish from unmanned aerial vehicles
- (2017) Jin Gwan Ha et al. Journal of Applied Remote Sensing
- Automated Identification of Northern Leaf Blight-Infected Maize Plants from Field Imagery Using Deep Learning
- (2017) Chad DeChant et al. PHYTOPATHOLOGY
- Unmanned Aerial Vehicle Remote Sensing for Field-Based Crop Phenotyping: Current Status and Perspectives
- (2017) Guijun Yang et al. Frontiers in Plant Science
- Deep Plant Phenomics: A Deep Learning Platform for Complex Plant Phenotyping Tasks
- (2017) Jordan R. Ubbens et al. Frontiers in Plant Science
- Machine Learning for Plant Phenotyping Needs Image Processing
- (2016) Sotirios A. Tsaftaris et al. TRENDS IN PLANT SCIENCE
- Machine Learning for High-Throughput Stress Phenotyping in Plants
- (2016) Arti Singh et al. TRENDS IN PLANT SCIENCE
- Using Deep Learning for Image-Based Plant Disease Detection
- (2016) Sharada P. Mohanty et al. Frontiers in Plant Science
- ImageNet Large Scale Visual Recognition Challenge
- (2015) Olga Russakovsky et al. INTERNATIONAL JOURNAL OF COMPUTER VISION
Find Funding. Review Successful Grants.
Explore over 25,000 new funding opportunities and over 6,000,000 successful grants.
ExploreAdd your recorded webinar
Do you already have a recorded webinar? Grow your audience and get more views by easily listing your recording on Peeref.
Upload Now