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 language||English (US)|
|Title of host publication||IROS 2017 - IEEE/RSJ International Conference on Intelligent Robots and Systems|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||8|
|State||Published - Dec 13 2017|
|Event||2017 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2017 - Vancouver, Canada|
Duration: Sep 24 2017 → Sep 28 2017
|Name||IEEE International Conference on Intelligent Robots and Systems|
|Other||2017 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2017|
|Period||9/24/17 → 9/28/17|
Bibliographical noteFunding 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.
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.