Automated Mobile System for Accurate Outdoor Tree Crop Enumeration Using an Uncalibrated Camera

This paper demonstrates an automated computer vision system for outdoor tree crop enumeration in a seedling nursery. The complete system incorporates both hardware components (including an embedded microcontroller, an odometry encoder, and an uncalibrated digital color camera) and software algorithm...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Vol. 15; no. 8; pp. 18427 - 18442
Main Authors: Nguyen, Thuy, Slaughter, David, Hanson, Bradley, Barber, Andrew, Freitas, Amy, Robles, Daniel, Whelan, Erin
Format: Journal Article
Language:English
Published: Switzerland MDPI 28.07.2015
MDPI AG
Subjects:
Algorithms
Automation
Calibration
California
Crops, Agricultural - physiology
Databases as Topic
Fertilizers
homography transform
Image Processing, Computer-Assisted
perspective transform
Photography - instrumentation
plant counting
projection histogram
tree crop enumeration
Trees - physiology
uncalibrated camera
California
uncalibrated camera
perspective transform
plant counting
homography transform
projection histogram
tree crop enumeration
ISSN:1424-8220, 1424-8220
Online Access:Get full text
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Summary:This paper demonstrates an automated computer vision system for outdoor tree crop enumeration in a seedling nursery. The complete system incorporates both hardware components (including an embedded microcontroller, an odometry encoder, and an uncalibrated digital color camera) and software algorithms (including microcontroller algorithms and the proposed algorithm for tree crop enumeration) required to obtain robust performance in a natural outdoor environment. The enumeration system uses a three-step image analysis process based upon: (1) an orthographic plant projection method integrating a perspective transform with automatic parameter estimation; (2) a plant counting method based on projection histograms; and (3) a double-counting avoidance method based on a homography transform. Experimental results demonstrate the ability to count large numbers of plants automatically with no human effort. Results show that, for tree seedlings having a height up to 40 cm and a within-row tree spacing of approximately 10 cm, the algorithms successfully estimated the number of plants with an average accuracy of 95.2% for trees within a single image and 98% for counting of the whole plant population in a large sequence of images.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s150818427