We present computer vision algorithms to collect yield related information in an apple orchard using images collected from a single camera. The goal of our system is to give farmers the capability to use their phones or digital cameras to record images and obtain yield related parameters. There are two challenges in this setup which necessitate novel methods: (i) It is very difficult to generate dense matches using standard image features. (ii) The constrained geometry of the setup causes existing structure from motion algorithms to fail. We present a novel piecewise incremental structure from motion technique to register and reconstruct the apples which is used for extracting count and diameter information. We validate our approach by presenting results from multiple field trials.
|Original language||English (US)|
|Title of host publication||2016 IEEE International Conference on Automation Science and Engineering, CASE 2016|
|Publisher||IEEE Computer Society|
|Number of pages||6|
|State||Published - Nov 14 2016|
|Event||2016 IEEE International Conference on Automation Science and Engineering, CASE 2016 - Fort Worth, United States|
Duration: Aug 21 2016 → Aug 24 2016
|Name||IEEE International Conference on Automation Science and Engineering|
|Other||2016 IEEE International Conference on Automation Science and Engineering, CASE 2016|
|Period||8/21/16 → 8/24/16|
Bibliographical noteFunding Information:
This material is based upon work supported by the National Science Foundation under Grant No. 1317788 and MnDrive RSAM initiative.
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