GeoDLS: A Deep Learning-Based Corn Disease Tracking and Location System Using RTK Geolocated UAS Imagery
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Title
GeoDLS: A Deep Learning-Based Corn Disease Tracking and Location System Using RTK Geolocated UAS Imagery
Authors
Keywords
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Journal
Remote Sensing
Volume 14, Issue 17, Pages 4140
Publisher
MDPI AG
Online
2022-08-24
DOI
10.3390/rs14174140
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- (2019) Amin Alqudah et al. IETE JOURNAL OF RESEARCH
- Advanced Spectroscopic Techniques for Plant Disease Diagnostics. A Review
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- A Deep Learning-Based Approach for Automated Yellow Rust Disease Detection from High-Resolution Hyperspectral UAV Images
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- Detection of target spot and bacterial spot diseases in tomato using UAV-based and benchtop-based hyperspectral imaging techniques
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- Automatic Recognition of Soybean Leaf Diseases Using UAV Images and Deep Convolutional Neural Networks
- (2019) Everton Castelao Tetila et al. IEEE Geoscience and Remote Sensing Letters
- Factors influencing the use of deep learning for plant disease recognition
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- The recognition of rice images by UAV based on capsule network
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- Hyperspectral Imaging for Presymptomatic Detection of Tobacco Disease with Successive Projections Algorithm and Machine-learning Classifiers
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- Leaf Doctor: A New Portable Application for Quantifying Plant Disease Severity
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- Plant Disease Severity Estimated Visually, by Digital Photography and Image Analysis, and by Hyperspectral Imaging
- (2010) C. H. Bock et al. CRITICAL REVIEWS IN PLANT SCIENCES
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