Development of a linear mixed model to predict the picking time in strawberry harvesting processes

In manual fruit and vegetable harvesting, picking time statistics can be used to improve labour management and optimise the design and operation of harvest-aiding machines, such as conventional cross-row conveyors or recently proposed robotic transport carts. In this study, a dataset of 161 picking...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Biosystems engineering 2018-02, Vol.166, p.76-89
Hauptverfasser: Khosro Anjom, Farangis, Vougioukas, Stavros G., Slaughter, David C.
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page 89
container_issue
container_start_page 76
container_title Biosystems engineering
container_volume 166
creator Khosro Anjom, Farangis
Vougioukas, Stavros G.
Slaughter, David C.
description In manual fruit and vegetable harvesting, picking time statistics can be used to improve labour management and optimise the design and operation of harvest-aiding machines, such as conventional cross-row conveyors or recently proposed robotic transport carts. In this study, a dataset of 161 picking times from 18 workers was collected in commercial strawberry fields in Salinas, California, and a set of conditional linear mixed models (LMMs) was formulated to model the amount of time (“picking time”) required by a picker to fill an empty tray with harvested crop. The LMMs were based on different combinations of the following influencing factors: picker speed, time of day, plant spacing, and picking cart style. The significance of effects of these factors was investigated and the LMMs were compared with each other using cross-validation (CV) techniques. The LMMs were also evaluated using a new dataset collected during the next year's harvest season. The best predictive LMM was found to be a heterogeneous model with “picker speed”, “time of day”, and “picking cart” factors. The model had a prediction error of 134.9 s based on 10-fold CV, and 136.8 s based on leave-one-out CV (LOOCV). The selected model predicts a priori mean and standard deviation of picking times for any given combination of factor levels. For instance, if picker speed is ‘fast’, the time of day is ‘morning’, and the picking cart is ‘standard’, the marginal predicted picking time is 477.1 ± 42.4 s. The proposed methodology and model structures offer a practical tool for strawberry picking time modelling, which could also be applied to other manually harvested specialty crops such as raspberries, cherry tomatoes, and table grapes. •A practical tool for modelling of picking time in strawberry harvesting is proposed.•The best model is selected among several conditional linear mixed models.•The model includes the factors of picker speed, time of day, and picking cart.•The prediction error of picking time using the selected model is 134.7 s.
doi_str_mv 10.1016/j.biosystemseng.2017.10.006
format Article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_2000546200</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><els_id>S1537511017302921</els_id><sourcerecordid>2000546200</sourcerecordid><originalsourceid>FETCH-LOGICAL-c413t-3cc228391107ebc78d99b02563b8ad7b8470e258c372347517a503e3ca37b5c13</originalsourceid><addsrcrecordid>eNqNkMtOwzAQRS0EEqXwD5bYsEnwI45TsUKlPCQkNrC2HGfauiRxsN1C_x5HRUjsWM1Id-bOnYPQJSU5JbS83uS1dWEfInQB-lXOCJVJyQkpj9CECi4zQdns-Len5BSdhbAhhApZlBNU38EOWjd00Efslljj1vagPe7sFzS4cw20ODo8eGisiTiuAQ_WvNt-haPtANseh-j1Zw3e7_Fa-x2EOKqDdwZCgHCOTpa6DXDxU6fo7X7xOn_Mnl8enua3z5kpKI8ZN4axis9SRgm1kVUzm9WEiZLXlW5kXRWSABOV4ZLxQgoqtSAcuNFc1sJQPkVXB990-WObUqjOBgNtq3tw26AYIUQUZSpp9OYwarwLwcNSDd522u8VJWokqzbqD1k1kh3FRDZtLw7bkL7ZWfAqGAu9SYQ8mKgaZ__l8w2Plori</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2000546200</pqid></control><display><type>article</type><title>Development of a linear mixed model to predict the picking time in strawberry harvesting processes</title><source>Elsevier ScienceDirect Journals</source><creator>Khosro Anjom, Farangis ; Vougioukas, Stavros G. ; Slaughter, David C.</creator><creatorcontrib>Khosro Anjom, Farangis ; Vougioukas, Stavros G. ; Slaughter, David C.</creatorcontrib><description>In manual fruit and vegetable harvesting, picking time statistics can be used to improve labour management and optimise the design and operation of harvest-aiding machines, such as conventional cross-row conveyors or recently proposed robotic transport carts. In this study, a dataset of 161 picking times from 18 workers was collected in commercial strawberry fields in Salinas, California, and a set of conditional linear mixed models (LMMs) was formulated to model the amount of time (“picking time”) required by a picker to fill an empty tray with harvested crop. The LMMs were based on different combinations of the following influencing factors: picker speed, time of day, plant spacing, and picking cart style. The significance of effects of these factors was investigated and the LMMs were compared with each other using cross-validation (CV) techniques. The LMMs were also evaluated using a new dataset collected during the next year's harvest season. The best predictive LMM was found to be a heterogeneous model with “picker speed”, “time of day”, and “picking cart” factors. The model had a prediction error of 134.9 s based on 10-fold CV, and 136.8 s based on leave-one-out CV (LOOCV). The selected model predicts a priori mean and standard deviation of picking times for any given combination of factor levels. For instance, if picker speed is ‘fast’, the time of day is ‘morning’, and the picking cart is ‘standard’, the marginal predicted picking time is 477.1 ± 42.4 s. The proposed methodology and model structures offer a practical tool for strawberry picking time modelling, which could also be applied to other manually harvested specialty crops such as raspberries, cherry tomatoes, and table grapes. •A practical tool for modelling of picking time in strawberry harvesting is proposed.•The best model is selected among several conditional linear mixed models.•The model includes the factors of picker speed, time of day, and picking cart.•The prediction error of picking time using the selected model is 134.7 s.</description><identifier>ISSN: 1537-5110</identifier><identifier>EISSN: 1537-5129</identifier><identifier>DOI: 10.1016/j.biosystemseng.2017.10.006</identifier><language>eng</language><publisher>Elsevier Ltd</publisher><subject>California ; carts ; cherry tomatoes ; conveyors ; data collection ; Harvest aids ; harvest date ; labor ; Manual harvesting ; Prediction ; raspberries ; robots ; Specialty crops ; statistical analysis ; statistical models ; Statistics ; strawberries ; table grapes ; Time studies</subject><ispartof>Biosystems engineering, 2018-02, Vol.166, p.76-89</ispartof><rights>2017 IAgrE</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c413t-3cc228391107ebc78d99b02563b8ad7b8470e258c372347517a503e3ca37b5c13</citedby><cites>FETCH-LOGICAL-c413t-3cc228391107ebc78d99b02563b8ad7b8470e258c372347517a503e3ca37b5c13</cites><orcidid>0000-0003-2758-8900</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S1537511017302921$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,776,780,3537,27901,27902,65306</link.rule.ids></links><search><creatorcontrib>Khosro Anjom, Farangis</creatorcontrib><creatorcontrib>Vougioukas, Stavros G.</creatorcontrib><creatorcontrib>Slaughter, David C.</creatorcontrib><title>Development of a linear mixed model to predict the picking time in strawberry harvesting processes</title><title>Biosystems engineering</title><description>In manual fruit and vegetable harvesting, picking time statistics can be used to improve labour management and optimise the design and operation of harvest-aiding machines, such as conventional cross-row conveyors or recently proposed robotic transport carts. In this study, a dataset of 161 picking times from 18 workers was collected in commercial strawberry fields in Salinas, California, and a set of conditional linear mixed models (LMMs) was formulated to model the amount of time (“picking time”) required by a picker to fill an empty tray with harvested crop. The LMMs were based on different combinations of the following influencing factors: picker speed, time of day, plant spacing, and picking cart style. The significance of effects of these factors was investigated and the LMMs were compared with each other using cross-validation (CV) techniques. The LMMs were also evaluated using a new dataset collected during the next year's harvest season. The best predictive LMM was found to be a heterogeneous model with “picker speed”, “time of day”, and “picking cart” factors. The model had a prediction error of 134.9 s based on 10-fold CV, and 136.8 s based on leave-one-out CV (LOOCV). The selected model predicts a priori mean and standard deviation of picking times for any given combination of factor levels. For instance, if picker speed is ‘fast’, the time of day is ‘morning’, and the picking cart is ‘standard’, the marginal predicted picking time is 477.1 ± 42.4 s. The proposed methodology and model structures offer a practical tool for strawberry picking time modelling, which could also be applied to other manually harvested specialty crops such as raspberries, cherry tomatoes, and table grapes. •A practical tool for modelling of picking time in strawberry harvesting is proposed.•The best model is selected among several conditional linear mixed models.•The model includes the factors of picker speed, time of day, and picking cart.•The prediction error of picking time using the selected model is 134.7 s.</description><subject>California</subject><subject>carts</subject><subject>cherry tomatoes</subject><subject>conveyors</subject><subject>data collection</subject><subject>Harvest aids</subject><subject>harvest date</subject><subject>labor</subject><subject>Manual harvesting</subject><subject>Prediction</subject><subject>raspberries</subject><subject>robots</subject><subject>Specialty crops</subject><subject>statistical analysis</subject><subject>statistical models</subject><subject>Statistics</subject><subject>strawberries</subject><subject>table grapes</subject><subject>Time studies</subject><issn>1537-5110</issn><issn>1537-5129</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><recordid>eNqNkMtOwzAQRS0EEqXwD5bYsEnwI45TsUKlPCQkNrC2HGfauiRxsN1C_x5HRUjsWM1Id-bOnYPQJSU5JbS83uS1dWEfInQB-lXOCJVJyQkpj9CECi4zQdns-Len5BSdhbAhhApZlBNU38EOWjd00Efslljj1vagPe7sFzS4cw20ODo8eGisiTiuAQ_WvNt-haPtANseh-j1Zw3e7_Fa-x2EOKqDdwZCgHCOTpa6DXDxU6fo7X7xOn_Mnl8enua3z5kpKI8ZN4axis9SRgm1kVUzm9WEiZLXlW5kXRWSABOV4ZLxQgoqtSAcuNFc1sJQPkVXB990-WObUqjOBgNtq3tw26AYIUQUZSpp9OYwarwLwcNSDd522u8VJWokqzbqD1k1kh3FRDZtLw7bkL7ZWfAqGAu9SYQ8mKgaZ__l8w2Plori</recordid><startdate>201802</startdate><enddate>201802</enddate><creator>Khosro Anjom, Farangis</creator><creator>Vougioukas, Stavros G.</creator><creator>Slaughter, David C.</creator><general>Elsevier Ltd</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7S9</scope><scope>L.6</scope><orcidid>https://orcid.org/0000-0003-2758-8900</orcidid></search><sort><creationdate>201802</creationdate><title>Development of a linear mixed model to predict the picking time in strawberry harvesting processes</title><author>Khosro Anjom, Farangis ; Vougioukas, Stavros G. ; Slaughter, David C.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c413t-3cc228391107ebc78d99b02563b8ad7b8470e258c372347517a503e3ca37b5c13</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>California</topic><topic>carts</topic><topic>cherry tomatoes</topic><topic>conveyors</topic><topic>data collection</topic><topic>Harvest aids</topic><topic>harvest date</topic><topic>labor</topic><topic>Manual harvesting</topic><topic>Prediction</topic><topic>raspberries</topic><topic>robots</topic><topic>Specialty crops</topic><topic>statistical analysis</topic><topic>statistical models</topic><topic>Statistics</topic><topic>strawberries</topic><topic>table grapes</topic><topic>Time studies</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Khosro Anjom, Farangis</creatorcontrib><creatorcontrib>Vougioukas, Stavros G.</creatorcontrib><creatorcontrib>Slaughter, David C.</creatorcontrib><collection>CrossRef</collection><collection>AGRICOLA</collection><collection>AGRICOLA - Academic</collection><jtitle>Biosystems engineering</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Khosro Anjom, Farangis</au><au>Vougioukas, Stavros G.</au><au>Slaughter, David C.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Development of a linear mixed model to predict the picking time in strawberry harvesting processes</atitle><jtitle>Biosystems engineering</jtitle><date>2018-02</date><risdate>2018</risdate><volume>166</volume><spage>76</spage><epage>89</epage><pages>76-89</pages><issn>1537-5110</issn><eissn>1537-5129</eissn><abstract>In manual fruit and vegetable harvesting, picking time statistics can be used to improve labour management and optimise the design and operation of harvest-aiding machines, such as conventional cross-row conveyors or recently proposed robotic transport carts. In this study, a dataset of 161 picking times from 18 workers was collected in commercial strawberry fields in Salinas, California, and a set of conditional linear mixed models (LMMs) was formulated to model the amount of time (“picking time”) required by a picker to fill an empty tray with harvested crop. The LMMs were based on different combinations of the following influencing factors: picker speed, time of day, plant spacing, and picking cart style. The significance of effects of these factors was investigated and the LMMs were compared with each other using cross-validation (CV) techniques. The LMMs were also evaluated using a new dataset collected during the next year's harvest season. The best predictive LMM was found to be a heterogeneous model with “picker speed”, “time of day”, and “picking cart” factors. The model had a prediction error of 134.9 s based on 10-fold CV, and 136.8 s based on leave-one-out CV (LOOCV). The selected model predicts a priori mean and standard deviation of picking times for any given combination of factor levels. For instance, if picker speed is ‘fast’, the time of day is ‘morning’, and the picking cart is ‘standard’, the marginal predicted picking time is 477.1 ± 42.4 s. The proposed methodology and model structures offer a practical tool for strawberry picking time modelling, which could also be applied to other manually harvested specialty crops such as raspberries, cherry tomatoes, and table grapes. •A practical tool for modelling of picking time in strawberry harvesting is proposed.•The best model is selected among several conditional linear mixed models.•The model includes the factors of picker speed, time of day, and picking cart.•The prediction error of picking time using the selected model is 134.7 s.</abstract><pub>Elsevier Ltd</pub><doi>10.1016/j.biosystemseng.2017.10.006</doi><tpages>14</tpages><orcidid>https://orcid.org/0000-0003-2758-8900</orcidid><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 1537-5110
ispartof Biosystems engineering, 2018-02, Vol.166, p.76-89
issn 1537-5110
1537-5129
language eng
recordid cdi_proquest_miscellaneous_2000546200
source Elsevier ScienceDirect Journals
subjects California
carts
cherry tomatoes
conveyors
data collection
Harvest aids
harvest date
labor
Manual harvesting
Prediction
raspberries
robots
Specialty crops
statistical analysis
statistical models
Statistics
strawberries
table grapes
Time studies
title Development of a linear mixed model to predict the picking time in strawberry harvesting processes
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-09T03%3A45%3A42IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Development%20of%20a%20linear%20mixed%20model%20to%20predict%20the%20picking%20time%20in%20strawberry%20harvesting%20processes&rft.jtitle=Biosystems%20engineering&rft.au=Khosro%20Anjom,%20Farangis&rft.date=2018-02&rft.volume=166&rft.spage=76&rft.epage=89&rft.pages=76-89&rft.issn=1537-5110&rft.eissn=1537-5129&rft_id=info:doi/10.1016/j.biosystemseng.2017.10.006&rft_dat=%3Cproquest_cross%3E2000546200%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2000546200&rft_id=info:pmid/&rft_els_id=S1537511017302921&rfr_iscdi=true