{"id":"https://openalex.org/W3090011912","doi":"https://doi.org/10.1109/ijcnn48605.2020.9206998","title":"Prediction of Strawberry Yield and Farm Price Utilizing Deep Learning","display_name":"Prediction of Strawberry Yield and Farm Price Utilizing Deep Learning","publication_year":2020,"publication_date":"2020-07-01","ids":{"openalex":"https://openalex.org/W3090011912","doi":"https://doi.org/10.1109/ijcnn48605.2020.9206998","mag":"3090011912"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn48605.2020.9206998","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn48605.2020.9206998","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5035038876","display_name":"Lobna Nassar","orcid":null},"institutions":[{"id":"https://openalex.org/I151746483","display_name":"University of Waterloo","ror":"https://ror.org/01aff2v68","country_code":"CA","type":"education","lineage":["https://openalex.org/I151746483"]}],"countries":["CA"],"is_corresponding":true,"raw_author_name":"Lobna Nassar","raw_affiliation_strings":["Electrical and Computer Engineering Department, University of Waterloo, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Electrical and Computer Engineering Department, University of Waterloo, Canada","institution_ids":["https://openalex.org/I151746483"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081150960","display_name":"Ifeanyi Emmanuel Okwuchi","orcid":null},"institutions":[{"id":"https://openalex.org/I151746483","display_name":"University of Waterloo","ror":"https://ror.org/01aff2v68","country_code":"CA","type":"education","lineage":["https://openalex.org/I151746483"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Ifeanyi Emmanuel Okwuchi","raw_affiliation_strings":["Systems Design Engineering Department, University of Waterloo, Ontario, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Systems Design Engineering Department, University of Waterloo, Ontario, Canada","institution_ids":["https://openalex.org/I151746483"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076891624","display_name":"Muhammad Saad","orcid":"https://orcid.org/0000-0002-8762-4566"},"institutions":[{"id":"https://openalex.org/I151746483","display_name":"University of Waterloo","ror":"https://ror.org/01aff2v68","country_code":"CA","type":"education","lineage":["https://openalex.org/I151746483"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Muhammad Saad","raw_affiliation_strings":["Electrical and Computer Engineering Department, University of Waterloo, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Electrical and Computer Engineering Department, University of Waterloo, Canada","institution_ids":["https://openalex.org/I151746483"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5070046659","display_name":"Fakhri Karray","orcid":null},"institutions":[{"id":"https://openalex.org/I151746483","display_name":"University of Waterloo","ror":"https://ror.org/01aff2v68","country_code":"CA","type":"education","lineage":["https://openalex.org/I151746483"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Fakhri Karray","raw_affiliation_strings":["Electrical and Computer Engineering Department, University of Waterloo, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Electrical and Computer Engineering Department, University of Waterloo, Canada","institution_ids":["https://openalex.org/I151746483"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086032740","display_name":"K. Ponnambalam","orcid":"https://orcid.org/0000-0002-4107-4829"},"institutions":[{"id":"https://openalex.org/I151746483","display_name":"University of Waterloo","ror":"https://ror.org/01aff2v68","country_code":"CA","type":"education","lineage":["https://openalex.org/I151746483"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Kumaraswamy Ponnambalam","raw_affiliation_strings":["Systems Design Engineering Department, University of Waterloo, Ontario, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Systems Design Engineering Department, University of Waterloo, Ontario, Canada","institution_ids":["https://openalex.org/I151746483"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5052924817","display_name":"Prarabdha Agrawal","orcid":null},"institutions":[{"id":"https://openalex.org/I151746483","display_name":"University of Waterloo","ror":"https://ror.org/01aff2v68","country_code":"CA","type":"education","lineage":["https://openalex.org/I151746483"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Prarabdha Agrawal","raw_affiliation_strings":["Electrical and Computer Engineering Department, University of Waterloo, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Electrical and Computer Engineering Department, University of Waterloo, Canada","institution_ids":["https://openalex.org/I151746483"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5035038876"],"corresponding_institution_ids":["https://openalex.org/I151746483"],"apc_list":null,"apc_paid":null,"fwci":5.0868,"has_fulltext":false,"cited_by_count":37,"citation_normalized_percentile":{"value":0.94832984,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"7"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12093","display_name":"Greenhouse Technology and Climate Control","score":0.9957000017166138,"subfield":{"id":"https://openalex.org/subfields/1110","display_name":"Plant Science"},"field":{"id":"https://openalex.org/fields/11","display_name":"Agricultural and Biological Sciences"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T12093","display_name":"Greenhouse Technology and Climate Control","score":0.9957000017166138,"subfield":{"id":"https://openalex.org/subfields/1110","display_name":"Plant Science"},"field":{"id":"https://openalex.org/fields/11","display_name":"Agricultural and Biological Sciences"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T11796","display_name":"Horticultural and Viticultural Research","score":0.9914000034332275,"subfield":{"id":"https://openalex.org/subfields/1110","display_name":"Plant Science"},"field":{"id":"https://openalex.org/fields/11","display_name":"Agricultural and Biological Sciences"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10616","display_name":"Smart Agriculture and AI","score":0.9876000285148621,"subfield":{"id":"https://openalex.org/subfields/1110","display_name":"Plant Science"},"field":{"id":"https://openalex.org/fields/11","display_name":"Agricultural and Biological Sciences"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7079334259033203},{"id":"https://openalex.org/keywords/yield","display_name":"Yield (engineering)","score":0.6892455816268921},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6537115573883057},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.635143518447876},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6330150961875916},{"id":"https://openalex.org/keywords/mean-squared-prediction-error","display_name":"Mean squared prediction error","score":0.5972583293914795},{"id":"https://openalex.org/keywords/measure","display_name":"Measure (data warehouse)","score":0.5880722403526306},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5800238251686096},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5629000663757324},{"id":"https://openalex.org/keywords/simple","display_name":"Simple (philosophy)","score":0.5120158791542053},{"id":"https://openalex.org/keywords/predictive-modelling","display_name":"Predictive modelling","score":0.45569270849227905},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.2106969952583313}],"concepts":[{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7079334259033203},{"id":"https://openalex.org/C134121241","wikidata":"https://www.wikidata.org/wiki/Q899301","display_name":"Yield (engineering)","level":2,"score":0.6892455816268921},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6537115573883057},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.635143518447876},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6330150961875916},{"id":"https://openalex.org/C167085575","wikidata":"https://www.wikidata.org/wiki/Q6803654","display_name":"Mean squared prediction error","level":2,"score":0.5972583293914795},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.5880722403526306},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5800238251686096},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5629000663757324},{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.5120158791542053},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.45569270849227905},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2106969952583313},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C191897082","wikidata":"https://www.wikidata.org/wiki/Q11467","display_name":"Metallurgy","level":1,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn48605.2020.9206998","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn48605.2020.9206998","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":42,"referenced_works":["https://openalex.org/W1485009520","https://openalex.org/W1678356000","https://openalex.org/W2016210396","https://openalex.org/W2040395995","https://openalex.org/W2064675550","https://openalex.org/W2068253265","https://openalex.org/W2070240570","https://openalex.org/W2143545157","https://openalex.org/W2170391700","https://openalex.org/W2290653249","https://openalex.org/W2294467502","https://openalex.org/W2295598076","https://openalex.org/W2409878965","https://openalex.org/W2481916066","https://openalex.org/W2532939453","https://openalex.org/W2585888707","https://openalex.org/W2768975186","https://openalex.org/W2774470364","https://openalex.org/W2792764867","https://openalex.org/W2795340004","https://openalex.org/W2805173585","https://openalex.org/W2805388006","https://openalex.org/W2900868617","https://openalex.org/W2912278761","https://openalex.org/W2945692492","https://openalex.org/W2962736999","https://openalex.org/W2963059228","https://openalex.org/W2963403868","https://openalex.org/W2966938419","https://openalex.org/W2969538577","https://openalex.org/W2981412311","https://openalex.org/W3102476541","https://openalex.org/W4385245566","https://openalex.org/W6628877408","https://openalex.org/W6637404493","https://openalex.org/W6733268759","https://openalex.org/W6739901393","https://openalex.org/W6745829810","https://openalex.org/W6749825310","https://openalex.org/W6752125693","https://openalex.org/W6756052569","https://openalex.org/W7033551188"],"related_works":["https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3193565141","https://openalex.org/W3133861977","https://openalex.org/W2951211570","https://openalex.org/W3167935049","https://openalex.org/W3103566983","https://openalex.org/W3029198973","https://openalex.org/W2811187992","https://openalex.org/W3042921537"],"abstract_inverted_index":{"The":[0,52,66,139],"currently":[1],"deployed":[2,126],"prediction":[3,69,160],"models":[4,24,40,75,98,129],"for":[5,29,44],"strawberry":[6,50,53,67,151],"fresh":[7],"produce":[8],"(FP)":[9],"are":[10,26,42],"based":[11],"on":[12,19],"either":[13],"conventional":[14,127],"machine":[15],"learning":[16,22],"(ML)":[17],"or":[18],"simple":[20,136],"deep":[21],"(DL)":[23],"that":[25,41,89,143],"mostly":[27],"applied":[28,43],"yield":[30],"prediction.":[31],"In":[32],"this":[33,144],"paper,":[34],"we":[35],"propose":[36],"more":[37],"comprehensive":[38],"DL":[39,74,137],"the":[45,93,96,109,112,118,122,125,133,158,163],"first":[46],"time":[47],"to":[48,107,116,121],"predict":[49],"yield.":[51,65],"price":[54,68],"is":[55,70,87,146],"predicted":[56],"as":[57,77,130,132],"well":[58,131],"directly":[59],"from":[60],"weather":[61],"input":[62],"parameters":[63],"and":[64,135,162],"achieved":[71],"using":[72],"compound":[73,97,134],"such":[76],"Convolutional":[78],"Long":[79],"Short-Term":[80],"Memory":[81],"Recurrent":[82],"Neural":[83],"Network":[84],"(CNN-LSTM).":[85],"It":[86],"found":[88],"by":[90],"adding":[91],"attention,":[92],"performance":[94,105],"of":[95,124,148],"usually":[99],"improves.":[100],"After":[101],"utilizing":[102],"an":[103],"aggregated":[104,140],"measure":[106,141],"find":[108],"best":[110,119],"model,":[111],"Attention-CNN-LSTM":[113],"model":[114,145,165],"proved":[115],"be":[117],"compared":[120],"rest":[123],"ML":[128],"models.":[138],"shows":[142],"capable":[147],"precisely":[149],"predicting":[150],"prices":[152],"five":[153],"weeks":[154],"ahead":[155],"while":[156],"maintaining":[157],"lowest":[159],"error":[161],"highest":[164],"correlation.":[166]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":9},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":9},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":1}],"updated_date":"2026-05-21T09:19:25.381259","created_date":"2025-10-10T00:00:00"}