Cheryl McCarthy

Catalog of Nearby Exoplanets

We present a catalog of nearby exoplanets. It contains the 172 known low-mass companions with orbits established through radial velocity and transit measurements around stars within 200 pc. We include five previously unpublished exoplanets orbiting the stars HD 11964, HD 66428, HD 99109, HD 107148, and HD 164922. We update orbits for 83 additional exoplanets, including many whose orbits have not been revised since their announcement, and include radial velocity time series from the Lick, Keck, and Anglo-Australian Observatory planet searches. Both these new and previously published velocities are more precise here due to improvements in our data reduction pipeline, which we applied to archival spectra. We present a brief summary of the global properties of the known exoplanets, including their distributions of orbital semimajor axis, minimum mass, and orbital eccentricity.

Four New Exoplanets and Hints of Additional Substellar Companions to Exoplanet Host Stars

We present four new exoplanets: HIP 14810b and HIP 14810c, HD 154345b, and HD 187123c. The two planets orbiting HIP 14810, from the N2K project, have masses of 3.9 and 0.76 M_J. We have searched the radial velocity time series of 90 known exoplanet systems and found new residual trends due to additional, long period companions. Two stars known to host one exoplanet have sufficient curvature in the residuals to a one planet fit to constrain the minimum mass of the outer companion to be substellar: HD 68988c with 8 M_J 8 yr. We have also searched the velocity residuals of known exoplanet systems for prospective low-amplitude exoplanets and present some candidates. We discuss techniques for constraining the mass and period of exoplanets in such cases, and for quantifying the significance of weak RV signals. We also present two substellar companions with incomplete orbits and periods longer than 8 yr: HD 24040b and HD 154345b with m sin i < 20 M_J and m sin i < 10 M_J, respectively.

An activity catalogue of southern stars

We have acquired high-resolution echelle spectra of 225 F6-M5 type stars in the southern hemisphere. The stars are targets or candidates to be targets for the AngloAustralian Planet Search. CaII HK line cores were used to derive activity indices for all of these objects. The indices were converted to the Mt. Wilson system of measurements . .

Applied machine vision of plants: a review with implications for field deployment in automated farming operations

Automated visual assessment of plant condition, specifically foliage wilting, reflectance and growth parameters, using machine vision has potential use as input for real-time variable-rate irrigation and fertigation systems in precision agriculture. This paper reviews the research literature for both outdoor and indoor applications of machine vision of plants, which reveals that different environments necessitate varying levels of complexity in both apparatus and nature of plant measurement which can be achieved. Deployment of systems to the field environment in precision agriculture applications presents the challenge of overcoming image variation caused by the diurnal and seasonal variation of sunlight. From the literature reviewed, it is argued that augmenting a monocular RGB vision system with additional sensing techniques potentially reduces image analysis complexity while enhancing system robustness to environmental variables. Therefore, machine vision systems with a foundation in optical and lighting design may potentially expedite the transition from laboratory and research prototype to robust field tool.

Orchard mapping and mobile robot localisation using on-board camera and laser scanner data fusion - Part A

A novel tree trunk detection algorithm uses camera and laser scanner data fusion.Discriminates between trees and non-tree objects in the orchard.Automatic adjustment of colour parameters increased the detection accuracy.Evaluation under different illumination conditions (sunny, cloudy).Using a small robot platform, a detection accuracy of 96.64% was achieved in a real orchard. Trees in orchards are natural landmarks providing suitable cues for mobile robot localisation as they are nominally planted in straight and parallel rows. This paper presents a novel tree trunk detection algorithm using a camera and laser scanner data fusion to enhance the detection capability. The algorithm detects the trees in the orchard and discriminates between trees and non-tree objects (e.g. posts and tree supports). The laser scanner is used to detect the edge points and determine the width of the tree trunks and non-tree objects, while the camera images are used to verify the colour and the parallel edges of the tree trunks and non-tree objects. The algorithm automatically adjusts the colour detection parameters after each test which shown to increase the detection accuracy. Experimental tests were conducted with a small robot platform in a real orchard environment to evaluate the performance of the tree trunk detection algorithm under two broad illumination conditions (sunny and cloudy). The algorithm was able to detect the tree trunks and discriminate between trees and non-tree objects with detection accuracy of 96.64% showing that the fusion of both vision and laser scanner technologies produced robust tree trunk detection.

Automated Internode Length Measurement of Cotton Plants under Field Conditions

An in-field vision system has been developed that automatically and non-destructively measures internode length (i.e., the distance between successive main stem branches) of plants in a growing cotton crop for the purpose of inferring plant growth performance and informing crop irrigation management. The system uses monocular video acquisition behind a plant-contacting transparent panel that moves through the crop. Line features are extracted from acquired imagery to estimate candidate nodes on the plants main stem via a two-stage process that may be implemented in real-time. Firstly, candidate nodes are identified in each individual image; secondly, the comparison of sequential images permits the removal of erroneously identified nodes (false positives) and compensation for missed nodes (due to occlusion by a leaf, commonly). Ninety-five internode length measurements were automatically detected from 168 video sequences of 14 plants. Algorithm run-time calculations and the rate of internode measurement acquisition were shown to be adequate for real-time input to spatially varied irrigation control. For this data set, the median absolute error was 5.3 mm in comparison with physical plant measurements, and the standard error in measurement ranged from 1.1 to 5.7 mm, with an average of 3.0 mm.

Orchard mapping and mobile robot localisation using on-board camera and laser scanner data fusion – Part B: Mapping and localisation

Accurate mobile robot localisation in orchards relies on precise orchard maps which help the mobile robot to efficiently estimate its position and orientation while moving between tree rows. This paper presents a new method for constructing a local orchard map based on tree trunk detection using camera and laser scanner data fusion. The final orchard map consists of the positions of the trees and non-tree objects (e.g. posts and tree supports) in the tree rows. The map of the individual trees is used as an a priori map to localise the mobile robot in the orchard. A data fusion algorithm based on an Extended Kalman Filter is used for position estimation. Experimental tests were conducted with a small robot platform in a real orchard environment to evaluate the performance of orchard mapping and mobile robot localisation. The mapping method successfully localised all the trees and non-tree objects of the tested tree rows in the orchard. The mapping results indicate that the constructed orchard map can be reliably used for mobile robot localisation and navigation. The localisation algorithm was evaluated against the logged RTK-GPS positions for different paths and headland turns. The average of the root mean square of the Euclidean distance between the ground truth and the estimated position for different paths was 0.103 m, whilst the average of the root mean square of the heading error was 3.32°.

On-the-go machine vision sensing of cotton plant geometric parameters: first results

Plant geometrical parameters such as internode length (i.e. the distance between successive branches on the main stem) indicate water stress in cotton. This paper describes a machine vision system that has been designed to measure internode length for the purpose of determining real-time cotton plant irrigation requirement. The imaging system features an enclosure which continuously traverses the crop canopy and forces the flexible upper main stem of individual plants against a glass panel at the front of the enclosure, hence allowing images of the plant to be captured in a fixed object plane. Subsequent image processing of selected video sequences enabled detection of the main stem in 88% of frames. However, node detection was subject to a high false detection rate due to leaf edges present in the images. Manual identification of nodes in the acquired imagery enabled measurement of internode lengths with 3% standard error.

Development and evaluation of a prototype precision spot spray system using image analysis to target guinea grass in sugarcane

Abstract Herbicide usage in weed control represents a significant economic cost and environmental risk in Australian sugarcane production. Weed spot spraying has potential to increase sugarcane production while reducing chemical usage and environmentally damaging runoff. However, weed spot spraying is traditionally a laborious manual task. This paper reports on a precision machine vision system that was developed to automatically identify and target the difficult to control weed Panicum spp. (Guinea Grass) in sugarcane crops. The infield machine vision system comprised a camera and artificial illumination to enable day and night trials. Image analysis algorithms were developed to discriminate Guinea Grass and sugarcane based on colour and textural differences between the species. A positive weed identification from the image analysis activated solenoid-controlled spray nozzles. Evaluations of the system in a sugarcane crop established that the image analysis algorithm parameters required frequent recalibration during the day but that the requirement for recalibration was reduced at night with constant artificial illumination. The algorithm was only effective at detecting mature Guinea Grass. The developed technology is considered a viable alternative to manual spot spraying of mature Guinea Grass in sugarcane at night. A cost benefit analysis of the new weed control system indicated potential grower savings of

Row and water front detection from UAV thermal-infrared imagery for furrow irrigation monitoring

170/ha by adopting the technology.