Active Optical Sensors for Tree Stem Detection and Classification in Nurseries

Active optical sensing (LIDAR and light curtain transmission) devices mounted on a mobile platform can correctly detect, localize, and classify trees. To conduct an evaluation and comparison of the different sensors, an optical encoder wheel was used for vehicle odometry and provided a measurement o...

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Main Authors: Miguel Garrido, Manuel Perez-Ruiz, Constantino Valero, Chris J. Gliever, Bradley D. Hanson, David C. Slaughter
Format: Article
Language:English
Published: MDPI AG 2014-06-01
Series:Sensors
Subjects:
Online Access:http://www.mdpi.com/1424-8220/14/6/10783
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author Miguel Garrido
Manuel Perez-Ruiz
Constantino Valero
Chris J. Gliever
Bradley D. Hanson
David C. Slaughter
author_facet Miguel Garrido
Manuel Perez-Ruiz
Constantino Valero
Chris J. Gliever
Bradley D. Hanson
David C. Slaughter
author_sort Miguel Garrido
collection DOAJ
description Active optical sensing (LIDAR and light curtain transmission) devices mounted on a mobile platform can correctly detect, localize, and classify trees. To conduct an evaluation and comparison of the different sensors, an optical encoder wheel was used for vehicle odometry and provided a measurement of the linear displacement of the prototype vehicle along a row of tree seedlings as a reference for each recorded sensor measurement. The field trials were conducted in a juvenile tree nursery with one-year-old grafted almond trees at Sierra Gold Nurseries, Yuba City, CA, United States. Through these tests and subsequent data processing, each sensor was individually evaluated to characterize their reliability, as well as their advantages and disadvantages for the proposed task. Test results indicated that 95.7% and 99.48% of the trees were successfully detected with the LIDAR and light curtain sensors, respectively. LIDAR correctly classified, between alive or dead tree states at a 93.75% success rate compared to 94.16% for the light curtain sensor. These results can help system designers select the most reliable sensor for the accurate detection and localization of each tree in a nursery, which might allow labor-intensive tasks, such as weeding, to be automated without damaging crops.
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spelling doaj.art-87f6702a6f9e4ab4a6fc303a9143c02f2022-12-22T04:01:19ZengMDPI AGSensors1424-82202014-06-01146107831080310.3390/s140610783s140610783Active Optical Sensors for Tree Stem Detection and Classification in NurseriesMiguel Garrido0Manuel Perez-Ruiz1Constantino Valero2Chris J. Gliever3Bradley D. Hanson4David C. Slaughter5Laboratorio de Propiedades Físicas (LPF)-TAGRALIA, Technical University of Madrid, Madrid 28040, SpainAerospace Engineering and Fluids Mechanics Department, University of Seville, Ctra. Sevilla-Utrera km 1, 41013 Seville, SpainLaboratorio de Propiedades Físicas (LPF)-TAGRALIA, Technical University of Madrid, Madrid 28040, SpainDepartment of Plant Sciences and Biological and Agricultural Engineering, Sensor and Instrumentation Lab, University of California, Davis, One Shields Ave, Davis, CA 95616, USADepartment of Plant Sciences and Biological and Agricultural Engineering, Sensor and Instrumentation Lab, University of California, Davis, One Shields Ave, Davis, CA 95616, USADepartment of Plant Sciences and Biological and Agricultural Engineering, Sensor and Instrumentation Lab, University of California, Davis, One Shields Ave, Davis, CA 95616, USAActive optical sensing (LIDAR and light curtain transmission) devices mounted on a mobile platform can correctly detect, localize, and classify trees. To conduct an evaluation and comparison of the different sensors, an optical encoder wheel was used for vehicle odometry and provided a measurement of the linear displacement of the prototype vehicle along a row of tree seedlings as a reference for each recorded sensor measurement. The field trials were conducted in a juvenile tree nursery with one-year-old grafted almond trees at Sierra Gold Nurseries, Yuba City, CA, United States. Through these tests and subsequent data processing, each sensor was individually evaluated to characterize their reliability, as well as their advantages and disadvantages for the proposed task. Test results indicated that 95.7% and 99.48% of the trees were successfully detected with the LIDAR and light curtain sensors, respectively. LIDAR correctly classified, between alive or dead tree states at a 93.75% success rate compared to 94.16% for the light curtain sensor. These results can help system designers select the most reliable sensor for the accurate detection and localization of each tree in a nursery, which might allow labor-intensive tasks, such as weeding, to be automated without damaging crops.http://www.mdpi.com/1424-8220/14/6/10783optical sensorstree stem detectionstate tree classificationLIDARlight curtain transmission
spellingShingle Miguel Garrido
Manuel Perez-Ruiz
Constantino Valero
Chris J. Gliever
Bradley D. Hanson
David C. Slaughter
Active Optical Sensors for Tree Stem Detection and Classification in Nurseries
Sensors
optical sensors
tree stem detection
state tree classification
LIDAR
light curtain transmission
title Active Optical Sensors for Tree Stem Detection and Classification in Nurseries
title_full Active Optical Sensors for Tree Stem Detection and Classification in Nurseries
title_fullStr Active Optical Sensors for Tree Stem Detection and Classification in Nurseries
title_full_unstemmed Active Optical Sensors for Tree Stem Detection and Classification in Nurseries
title_short Active Optical Sensors for Tree Stem Detection and Classification in Nurseries
title_sort active optical sensors for tree stem detection and classification in nurseries
topic optical sensors
tree stem detection
state tree classification
LIDAR
light curtain transmission
url http://www.mdpi.com/1424-8220/14/6/10783
work_keys_str_mv AT miguelgarrido activeopticalsensorsfortreestemdetectionandclassificationinnurseries
AT manuelperezruiz activeopticalsensorsfortreestemdetectionandclassificationinnurseries
AT constantinovalero activeopticalsensorsfortreestemdetectionandclassificationinnurseries
AT chrisjgliever activeopticalsensorsfortreestemdetectionandclassificationinnurseries
AT bradleydhanson activeopticalsensorsfortreestemdetectionandclassificationinnurseries
AT davidcslaughter activeopticalsensorsfortreestemdetectionandclassificationinnurseries