Financial and social elements of modern societies are closely connected to the cultivation of corn. Due to its massive production, deficiencies during the cultivation process directly translate to major financial losses. Since proper surveillance in a large scale is still very challenging, the companies that specialize in optimizing crop yield are trying to address the problem at its root by developing hybrid plants able to resist the harsh conditions of the field. The selection of the best hybrid is not easy and every year hundreds of test plants with different phenotypic characteristics are planted while their performance is quantified by inconsistent and rough measurements gathered by humans. We propose a pipeline that takes advantage of the structure from motion technology to create a detailed 3D point cloud of a few plants and segment it into the basic elements of the scene; the ground, the plants, the plant stems, and the plant leaves. The focus is on the segmentation process through which several phenotypic characteristics of individual plants can be extracted. As an example, we show the results for the plant counting and plant height estimation processes where we achieve an accuracy of 88.1 % and 89.2%.
|Original language||English (US)|
|Title of host publication||2018 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2018|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||8|
|State||Published - Dec 27 2018|
|Event||2018 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2018 - Madrid, Spain|
Duration: Oct 1 2018 → Oct 5 2018
|Name||IEEE International Conference on Intelligent Robots and Systems|
|Conference||2018 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2018|
|Period||10/1/18 → 10/5/18|
Bibliographical noteFunding Information:
This material is based upon work supported by the Corn Growers Association of MN and the National Science Foundation through grants #CNS-1439728, #IIS-1427014, and #CNS-1531330.