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

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Abstract

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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Scalable Nearest Neighbor Algorithms for High Dimensional Data.

Marius Muja, David Lowe (2014)

For many computer vision and machine learning problems, large training sets are key for good performance. However, the most computationally expensive part of many computer vision and machine learning algorithms consists of finding nearest neighbor matches to high dimensional vectors that represent the training data. We propose new algorithms for approximate nearest neighbor matching and evaluate and compare them with previous algorithms. For matching high dimensional features, we find two algorithms to be the most efficient: the randomized k-d forest and a new algorithm proposed in this paper, the priority search k-means tree. We also propose a new algorithm for matching binary features by searching multiple hierarchical clustering trees and show it outperforms methods typically used in the literature. We show that the optimal nearest neighbor algorithm and its parameters depend on the data set characteristics and describe an automated configuration procedure for finding the best algorithm to search a particular data set. In order to scale to very large data sets that would otherwise not fit in the memory of a single machine, we propose a distributed nearest neighbor matching framework that can be used with any of the algorithms described in the paper. All this research has been released as an open source library called fast library for approximate nearest neighbors (FLANN), which has been incorporated into OpenCV and is now one of the most popular libraries for nearest neighbor matching.

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Towards Autonomous Agriculture: Automatic Ground Detection Using Trinocular Stereovision

Giulio Reina, Annalisa Milella (2012)

Autonomous driving is a challenging problem, particularly when the domain is unstructured, as in an outdoor agricultural setting. Thus, advanced perception systems are primarily required to sense and understand the surrounding environment recognizing artificial and natural structures, topology, vegetation and paths. In this paper, a self-learning framework is proposed to automatically train a ground classifier for scene interpretation and autonomous navigation based on multi-baseline stereovision. The use of rich 3D data is emphasized where the sensor output includes range and color information of the surrounding environment. Two distinct classifiers are presented, one based on geometric data that can detect the broad class of ground and one based on color data that can further segment ground into subclasses. The geometry-based classifier features two main stages: an adaptive training stage and a classification stage. During the training stage, the system automatically learns to associate geometric appearance of 3D stereo-generated data with class labels. Then, it makes predictions based on past observations. It serves as well to provide training labels to the color-based classifier. Once trained, the color-based classifier is able to recognize similar terrain classes in stereo imagery. The system is continuously updated online using the latest stereo readings, thus making it feasible for long range and long duration navigation, over changing environments. Experimental results, obtained with a tractor test platform operating in a rural environment, are presented to validate this approach, showing an average classification precision and recall of 91.0% and 77.3%, respectively.

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Multisensory System for Fruit Harvesting Robots. Experimental Testing in Natural Scenarios and with Different Kinds of Crops

Roemi Fernández, Carlota Salinas, Héctor Montes … (2014)

The motivation of this research was to explore the feasibility of detecting and locating fruits from different kinds of crops in natural scenarios. To this end, a unique, modular and easily adaptable multisensory system and a set of associated pre-processing algorithms are proposed. The offered multisensory rig combines a high resolution colour camera and a multispectral system for the detection of fruits, as well as for the discrimination of the different elements of the plants, and a Time-Of-Flight (TOF) camera that provides fast acquisition of distances enabling the localisation of the targets in the coordinate space. A controlled lighting system completes the set-up, increasing its flexibility for being used in different working conditions. The pre-processing algorithms designed for the proposed multisensory system include a pixel-based classification algorithm that labels areas of interest that belong to fruits and a registration algorithm that combines the results of the aforementioned classification algorithm with the data provided by the TOF camera for the 3D reconstruction of the desired regions. Several experimental tests have been carried out in outdoors conditions in order to validate the capabilities of the proposed system.

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Author and article information

Contributors

Gonzalo Pajares Martinsanz: Role: Academic Editor

Journal

Journal ID (nlm-ta): Sensors (Basel)

Journal ID (iso-abbrev): Sensors (Basel)

Journal ID (publisher-id): sensors

Title: Sensors (Basel, Switzerland)

Publisher: MDPI

ISSN (Electronic): 1424-8220

Publication date (Electronic): 28 July 2015

Publication date Collection: August 2015

Volume: 15

Issue: 8

Pages: 18427-18442

Affiliations

[1 ]Department of Computer Science, University of California, Davis, CA 95616, USA

[2 ]Department of Biological and Agricultural Engineering, University of California, Davis, CA 95616, USA; E-Mails: abarber@ 123456ucdavis.edu (A.B.); asfreitas@ 123456ucdavis.edu (A.F.); drrobles@ 123456ucdavis.edu (D.R.); emwhelan@ 123456ucdavis.edu (E.W.)

[3 ]Department of Plant Sciences, University of California, Davis, CA 95616, USA; E-Mail: bhanson@ 123456ucdavis.edu

Author notes

[* ]Authors to whom correspondence should be addressed; E-Mails: thnguyen@ 123456ucdavis.edu (T.T.N.); dcslaughter@ 123456ucdavis.edu (D.C.S.); Tel.: +1-530-752-5553 (T.T.N.); +1-530-752-5553 (D.C.S.); Fax: +1-530-752-2640 (D.C.S.).

Article

Publisher ID: sensors-15-18427

DOI: 10.3390/s150818427

PMC ID: 4570329

PubMed ID: 26225982

SO-VID: 22c927bc-bf60-4a98-b19f-9b8e8d833854

License:

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license ( http://creativecommons.org/licenses/by/4.0/).

History

Date received : 31 May 2015

Date accepted : 20 July 2015

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