Applying the machine repair model to improve efficiency of harvesting fruit

Ampatzidis, Yiannis; Vougioukas, Stavros; Whiting, Matthew; Zhang, Qin

doi:10.1016/j.biosystemseng.2013.07.011

Cited by 33 publications

(22 citation statements)

References 18 publications

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“…It is also a highly perishable product and harvesting inefficiency could have damaging effects on product quality and storability (Ampatzidis et al, 2014). Recently, various planning and optimization algorithms have been introduced to improve the harvesting efficiency in agronomic crops (i.e.…”

Section: Literature Reviewmentioning

confidence: 99%

Hybrid modeling for vineyard harvesting operations

Mesabbah¹,

Mahfouz²,

Af³

et al. 2016

2016 Winter Simulation Conference (WSC)

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Section: Literature Reviewmentioning

confidence: 99%

Hybrid modeling for vineyard harvesting operations

Mesabbah¹,

Mahfouz²,

Af³

et al. 2016

2016 Winter Simulation Conference (WSC)

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“…Briefly speaking, Cherry trees with the UFO architecture are trained to grow as 2-dimension wall, instead of a traditional cone-shaped canopy. The UFO architecture has been shown to improve the yield as well as harvest labor efficiency [11]. A cherry tree with the UFO architecture is pruned to have a maximal height within arm's reach.…”

Section: Defining Yield Distribution Functionsmentioning

confidence: 99%

Cloud-based monitoring and analysis of yield efficiency in precision farming

Tan

Wortman

2014

Proceedings of the 2014 IEEE 15th International Conference on Information Reuse and Integration (IEEE IRI 2014)

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Yield mapping visualizes yield rate per geological distribution. It is frequently used as a baseline metric to measure yield efficiency in precision farming. A major challenge in mapping yield for specialty crops is how to collect accurate yield data without incurring substantial overhead to a farming operation. We design a yield efficiency analysis system that uses a cloud-based computing platform to acquire and analyze yield data. By reusing labor data collected by a cloud-based labor monitoring system that we developed earlier, our system calculates yield data from labor data, and computes yield map in real time and without the overhead for data acquisition. A distinctive feature of our approach is the introduction of a customizable yield distribution function that quantifies the probability of geographic distribution of fruits weighted at a Labor Monitoring Device. Practitioners may define yield distribution functions based on operational characteristics of an orchard, enabling our system adaptive for a variety of orchards with different harvesting operations and canopy architecture. Using a multi-tenancy software architecture, our system can support multiple orchards concurrently with improved scalability and data privacy. Our system has been deployed and tested on Amazon Web Services (AWS).

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“…In agriculture they can be employed to analyse: performance of agricultural machines (Ampatzidis et al, 2014;Coen et al, 2008 andEbrahimi et al, 2013), agronomic operations ( Van't Ooster et al, 2014 andBochtis et al, 2014), production management (Tedeschi et al, 2011 andReynoso-Campos et al, 2004), production economy (Valdivia et al, 2012 andLi, 2011) possibility of greenhouse emission reduction (Crosson et al, 2011) and other areas of agricultural production and systems (Wallach et al, 2014;Rosenzweig et al, 2013;Rossing et al, 2007). One of the tools used in numerical modelling and analysis of various systems is the Matlab environment, widely used in the area of agriculture (Giusti and Marsili-Libelli, 2015;Menesatti et al, 2014;Zhang et al, 2011;Papadopoulos et al, 2011;Cool et al, 2014 andOdegard andvan der Voet, 2014), including modelling of the renewable energy systems (Kıyan et al, 2013;Woinaroschy, 2014;Da Silva and Fernandes, 2010;Nakoul et al, 2014;Taghavifar andMardani, 2014 andSefeedpari et al, 2014).…”

Section: Introductionmentioning

confidence: 99%