DocumentCode
3862972
Title
Cloud-Based Harvest Management System for Specialty Crops
Author
Li Tan;Ronald Haley;Riley Wortman
Author_Institution
Dept. of Eng. &
fYear
2015
fDate
6/15/2016 12:00:00 AM
Firstpage
91
Lastpage
98
Abstract
Harvesting labor is a major cost factor in the production of specialty crops. Today accruing harvest labors is still done by hands, which is error-prone and costly. By integrating cloud-based web application with purposely designed labor monitoring devices (LMDs), we developed a harvest management system for monitoring and accruing harvest labors. The system comprises of two major components: an in-orchard data collection network collecting harvest data and transmitting them to a cloud-based labor management software (LMS); and, LMS processing harvest data and delivering results to users via a tablet-friendly web interface. Using a patented technology, the system accurately accrues harvest labor activities for multiple orchards, even under complex many-to-many employment relations. The system provides multi-fold benefits to stakeholders of specialty crop harvesting: a picker can be compensated accurately by the actual weight of the fruits he picked; and an orchard manager may monitor labor activities in real time and improve his orchard operation based on the analytical reports generated by the system. The dynamic resource allocation provided by a cloud computing platform ensures that the system can handle the fluctuating demand for processing real-time harvest data during and off harvest seasons. The design of the system is optimized for cloud computing, improving the access to orchard data while preserving their privacy for growers. A prototype of the system has been validated in field tests in United States´ Pacific Northwest Region.
Keywords
"Least squares approximations","Cloud computing","Agriculture","Monitoring","Data collection","Radiofrequency identification"
Publisher
ieee
Conference_Titel
Network Cloud Computing and Applications (NCCA), 2015 IEEE Fourth Symposium on
Print_ISBN
978-1-4673-7741-6
Type
conf
DOI
10.1109/NCCA.2015.23
Filename
7340032
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