{"id":"https://openalex.org/W3042534169","doi":"https://doi.org/10.5220/0009827900810090","title":"Simulation of near Infrared Sensor in Unity for Plant-weed Segmentation Classification","display_name":"Simulation of near Infrared Sensor in Unity for Plant-weed Segmentation Classification","publication_year":2020,"publication_date":"2020-01-01","ids":{"openalex":"https://openalex.org/W3042534169","doi":"https://doi.org/10.5220/0009827900810090","mag":"3042534169"},"language":"en","primary_location":{"id":"doi:10.5220/0009827900810090","is_oa":false,"landing_page_url":"https://doi.org/10.5220/0009827900810090","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 10th International Conference on Simulation and Modeling Methodologies, Technologies and Applications","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"http://hdl.handle.net/11573/1486949","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5087739715","display_name":"Carlos Carbone","orcid":"https://orcid.org/0000-0001-5615-0225"},"institutions":[{"id":"https://openalex.org/I861853513","display_name":"Sapienza University of Rome","ror":"https://ror.org/02be6w209","country_code":"IT","type":"education","lineage":["https://openalex.org/I861853513"]}],"countries":["IT"],"is_corresponding":true,"raw_author_name":"Carlos Carbone","raw_affiliation_strings":["Department of Computer, Control and Management Engineering, Sapienza University of Rome, Via Ariosto 25, Rome, Italy, --- Select a Country ---"],"affiliations":[{"raw_affiliation_string":"Department of Computer, Control and Management Engineering, Sapienza University of Rome, Via Ariosto 25, Rome, Italy, --- Select a Country ---","institution_ids":["https://openalex.org/I861853513"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5013703356","display_name":"Ciro Potena","orcid":"https://orcid.org/0000-0003-2395-2170"},"institutions":[{"id":"https://openalex.org/I861853513","display_name":"Sapienza University of Rome","ror":"https://ror.org/02be6w209","country_code":"IT","type":"education","lineage":["https://openalex.org/I861853513"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Ciro Potena","raw_affiliation_strings":["Department of Computer, Control and Management Engineering, Sapienza University of Rome, Via Ariosto 25, Rome, Italy, --- Select a Country ---"],"affiliations":[{"raw_affiliation_string":"Department of Computer, Control and Management Engineering, Sapienza University of Rome, Via Ariosto 25, Rome, Italy, --- Select a Country ---","institution_ids":["https://openalex.org/I861853513"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5075651762","display_name":"Daniele Nardi","orcid":"https://orcid.org/0000-0001-6606-200X"},"institutions":[{"id":"https://openalex.org/I861853513","display_name":"Sapienza University of Rome","ror":"https://ror.org/02be6w209","country_code":"IT","type":"education","lineage":["https://openalex.org/I861853513"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Daniele Nardi","raw_affiliation_strings":["Department of Computer, Control and Management Engineering, Sapienza University of Rome, Via Ariosto 25, Rome, Italy, --- Select a Country ---"],"affiliations":[{"raw_affiliation_string":"Department of Computer, Control and Management Engineering, Sapienza University of Rome, Via Ariosto 25, Rome, Italy, --- Select a Country ---","institution_ids":["https://openalex.org/I861853513"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5087739715"],"corresponding_institution_ids":["https://openalex.org/I861853513"],"apc_list":null,"apc_paid":null,"fwci":0.4598,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.74814338,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"81","last_page":"90"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10616","display_name":"Smart Agriculture and AI","score":0.9998000264167786,"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/T10616","display_name":"Smart Agriculture and AI","score":0.9998000264167786,"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/T10640","display_name":"Spectroscopy and Chemometric Analyses","score":0.9901999831199646,"subfield":{"id":"https://openalex.org/subfields/1602","display_name":"Analytical Chemistry"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10111","display_name":"Remote Sensing in Agriculture","score":0.9824000000953674,"subfield":{"id":"https://openalex.org/subfields/2303","display_name":"Ecology"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7371706962585449},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7343102693557739},{"id":"https://openalex.org/keywords/rgb-color-model","display_name":"RGB color model","score":0.6912580728530884},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6659532785415649},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6223113536834717},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.616059422492981},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.4285885989665985},{"id":"https://openalex.org/keywords/weed","display_name":"Weed","score":0.4279195964336395},{"id":"https://openalex.org/keywords/near-infrared-spectroscopy","display_name":"Near-infrared spectroscopy","score":0.4116588830947876},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.38949641585350037},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.21362346410751343}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7371706962585449},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7343102693557739},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.6912580728530884},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6659532785415649},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6223113536834717},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.616059422492981},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.4285885989665985},{"id":"https://openalex.org/C2775891814","wikidata":"https://www.wikidata.org/wiki/Q101879","display_name":"Weed","level":2,"score":0.4279195964336395},{"id":"https://openalex.org/C43571822","wikidata":"https://www.wikidata.org/wiki/Q599037","display_name":"Near-infrared spectroscopy","level":2,"score":0.4116588830947876},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.38949641585350037},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.21362346410751343},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C6557445","wikidata":"https://www.wikidata.org/wiki/Q173113","display_name":"Agronomy","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.5220/0009827900810090","is_oa":false,"landing_page_url":"https://doi.org/10.5220/0009827900810090","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 10th International Conference on Simulation and Modeling Methodologies, Technologies and Applications","raw_type":"proceedings-article"},{"id":"pmh:oai:iris.uniroma1.it:11573/1486949","is_oa":true,"landing_page_url":"http://hdl.handle.net/11573/1486949","pdf_url":null,"source":{"id":"https://openalex.org/S4377196107","display_name":"IRIS Research product catalog (Sapienza University of Rome)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"info:eu-repo/semantics/conferenceObject"}],"best_oa_location":{"id":"pmh:oai:iris.uniroma1.it:11573/1486949","is_oa":true,"landing_page_url":"http://hdl.handle.net/11573/1486949","pdf_url":null,"source":{"id":"https://openalex.org/S4377196107","display_name":"IRIS Research product catalog (Sapienza University of Rome)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"info:eu-repo/semantics/conferenceObject"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/2","score":0.4399999976158142,"display_name":"Zero hunger"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":33,"referenced_works":["https://openalex.org/W1910657905","https://openalex.org/W1945811542","https://openalex.org/W2030083859","https://openalex.org/W2092582191","https://openalex.org/W2487365028","https://openalex.org/W2539108539","https://openalex.org/W2564734427","https://openalex.org/W2585276140","https://openalex.org/W2586545389","https://openalex.org/W2588756761","https://openalex.org/W2615516218","https://openalex.org/W2616967369","https://openalex.org/W2618530766","https://openalex.org/W2739413041","https://openalex.org/W2755766995","https://openalex.org/W2766580384","https://openalex.org/W2773650303","https://openalex.org/W2789189366","https://openalex.org/W2805267014","https://openalex.org/W2886554959","https://openalex.org/W2887311010","https://openalex.org/W2889987506","https://openalex.org/W2898498213","https://openalex.org/W2903014168","https://openalex.org/W2903774596","https://openalex.org/W2911500720","https://openalex.org/W2926464840","https://openalex.org/W2962782553","https://openalex.org/W2963422987","https://openalex.org/W2966975099","https://openalex.org/W2970748783","https://openalex.org/W2973282505","https://openalex.org/W4378567336"],"related_works":["https://openalex.org/W2157457846","https://openalex.org/W1584821869","https://openalex.org/W2952813363","https://openalex.org/W4360783045","https://openalex.org/W2963346891","https://openalex.org/W3176438653","https://openalex.org/W2770149305","https://openalex.org/W3167930666","https://openalex.org/W3014952856","https://openalex.org/W3010730661"],"abstract_inverted_index":{"Weed":[0],"spotting":[1],"through":[2],"image":[3],"classification":[4,129],"is":[5,40,125],"one":[6],"of":[7,44,51,76,103,161,179],"the":[8,49,74,79,96,132,159,162,166,182,192,196],"methods":[9],"applied":[10],"in":[11,17,87,95,195],"precision":[12],"agriculture":[13],"to":[14,111,117,165],"increase":[15],"efficiency":[16],"crop":[18],"damage":[19],"reduction.":[20],"These":[21,175],"classifications":[22],"are":[23,109,115],"nowadays":[24],"typically":[25],"based":[26,63],"on":[27,64],"deep":[28],"machine":[29],"learning":[30],"with":[31,106,137,142,146],"convolutional":[32],"neural":[33],"networks":[34],"(CNN),":[35],"where":[36],"a":[37,93,177],"main":[38],"difficulty":[39],"gathering":[41],"large":[42],"amounts":[43],"labeled":[45],"data":[46,69,194],"required":[47],"for":[48,120,168,181],"training":[50],"these":[52,88],"networks.":[53],"Thus,":[54],"synthetic":[55,138],"dataset":[56,124],"sources":[57],"have":[58,83],"been":[59,85],"developed":[60],"including":[61,191],"simulations":[62],"graphic":[65],"engines;":[66],"however,":[67],"some":[68],"inputs":[70],"that":[71,100,114,158],"can":[72,171],"improve":[73],"performance":[75],"CNNs":[77,119],"like":[78],"near":[80],"infrared":[81],"(NIR)":[82],"not":[84],"considered":[86],"simulations.":[89],"This":[90],"paper":[91],"presents":[92],"simulation":[94,167],"Unity":[97],"game":[98],"engine":[99],"builds":[101],"fields":[102],"sugar":[104],"beets":[105],"weeds.":[107],"Images":[108],"generated":[110,198],"create":[112],"datasets":[113],"ready":[116],"train":[118],"semantic":[121],"segmentation.":[122],"The":[123,154],"tested":[126],"by":[127,190],"comparing":[128],"results":[130,156],"from":[131],"bonnet":[133],"CNN":[134],"network":[135],"trained":[136,141],"images":[139,189],"and":[140,148],"real":[143],"images,":[144],"both":[145],"RGB":[147],"RGBN":[149],"(RGB+near":[150],"infrared)":[151],"as":[152],"inputs.":[153],"preliminary":[155],"suggest":[157],"addition":[160],"NIR":[163,193],"channel":[164],"plant-weed":[169],"segmentation":[170],"be":[172],"effectively":[173],"exploited.":[174],"show":[176],"difference":[178],"5.75%":[180],"global":[183],"mean":[184],"IoU":[185],"over":[186],"820":[187],"classified":[188],"unity":[197],"dataset.":[199]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2020-07-23T00:00:00"}
