Estimating the Leaf Area Index of crops through the evaluation of 3D models

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Scopus citations

Abstract

Financial and social elements of modern societies are closely connected to the cultivation of corn. Due to the massive production of corn, deficiencies during the cultivation process directly translate to major financial losses. The early detection and treatment of crops deficiencies is thus a task of great significance. Towards an automated health condition assessment, this study introduces a scheme for the computation of plant health indices. Based on the 3D reconstruction of small batches of corn plants, an alternative to existing cumbersome Leaf Area Index (LAI) estimation methodologies is presented. The use of 3D models provides an elevated information content, when compared to planar methods, mainly due to the reduced loss attributed to leaf occlusions. High resolution images of corn stalks are collected and used to obtain 3D models of plants of interest. Based on the extracted 3D point clouds, an accurate calculation of the Leaf Area Index (LAI) of the plants is performed. An experimental validation (using artificially made corn plants used as ground truth of the LAI estimation), emulating real world scenarios, supports the efficacy of the proposed methodology. The conclusions of this work, suggest a fully automated scheme for information gathering in modern farms capable of replacing current labor intensive procedures, thus greatly impacting the timely detection of crop deficiencies.

Original languageEnglish (US)
Title of host publicationIROS 2017 - IEEE/RSJ International Conference on Intelligent Robots and Systems
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages6155-6162
Number of pages8
ISBN (Electronic)9781538626825
DOIs
StatePublished - Dec 13 2017
Event2017 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2017 - Vancouver, Canada
Duration: Sep 24 2017Sep 28 2017

Publication series

NameIEEE International Conference on Intelligent Robots and Systems
Volume2017-September
ISSN (Print)2153-0858
ISSN (Electronic)2153-0866

Other

Other2017 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2017
Country/TerritoryCanada
CityVancouver
Period9/24/179/28/17

Bibliographical note

Funding Information:
This material is based upon work supported by the Corn Growers Association of MN and the National Science Foundation through grants #IIP-0934413, #IIS-1017344, #CNS-1061489, #IIS-1427014, #IIP-1432957, and #CNS-1531330.

Funding Information:
ACKNOWLEDGEMENTS Dimitris Zermas has been supported in part by a UMII Fellowship. This material is based upon work supported by the Corn Growers Association of MN and the National Science Foundation through grants #IIP-0934413, #IIS-1017344, #CNS-1061489, #IIS-1427014, #IIP-1432957, and #CNS-1531330.

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