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Drones 2018, 2(4), 39; https://doi.org/10.3390/drones2040039

Identification of Citrus Trees from Unmanned Aerial Vehicle Imagery Using Convolutional Neural Networks

1
Department of Geoinformatics, Z_GIS, University of Salzburg, 5020 Salzburg, Austria
2
UAV/Remote Sensing Subject Matter Expert, Berkeley, CA 94705, USA
3
University of California Division of Agriculture and Natural Resources, Davis, CA 95618, USA
4
Department of Environmental Sciences, Policy and Management, University of California, Berkeley, CA 94720, USA
*
Author to whom correspondence should be addressed.
Received: 24 October 2018 / Revised: 7 November 2018 / Accepted: 14 November 2018 / Published: 20 November 2018
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Abstract

Remote sensing is important to precision agriculture and the spatial resolution provided by Unmanned Aerial Vehicles (UAVs) is revolutionizing precision agriculture workflows for measurement crop condition and yields over the growing season, for identifying and monitoring weeds and other applications. Monitoring of individual trees for growth, fruit production and pest and disease occurrence remains a high research priority and the delineation of each tree using automated means as an alternative to manual delineation would be useful for long-term farm management. In this paper, we detected citrus and other crop trees from UAV images using a simple convolutional neural network (CNN) algorithm, followed by a classification refinement using superpixels derived from a Simple Linear Iterative Clustering (SLIC) algorithm. The workflow performed well in a relatively complex agricultural environment (multiple targets, multiple size trees and ages, etc.) achieving high accuracy (overall accuracy = 96.24%, Precision (positive predictive value) = 94.59%, Recall (sensitivity) = 97.94%). To our knowledge, this is the first time a CNN has been used with UAV multi-spectral imagery to focus on citrus trees. More of these individual cases are needed to develop standard automated workflows to help agricultural managers better incorporate large volumes of high resolution UAV imagery into agricultural management operations. View Full-Text
Keywords: CNN; deep learning; superpixels; precision agriculture; UAS; feature extraction; citrus; tree identification CNN; deep learning; superpixels; precision agriculture; UAS; feature extraction; citrus; tree identification
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Csillik, O.; Cherbini, J.; Johnson, R.; Lyons, A.; Kelly, M. Identification of Citrus Trees from Unmanned Aerial Vehicle Imagery Using Convolutional Neural Networks. Drones 2018, 2, 39.

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