{"id":"https://openalex.org/W4402260285","doi":"https://doi.org/10.1109/igarss53475.2024.10641426","title":"Comparative Evaluation of XAI Methods for Transparent Crop Yield Estimation Using CNN","display_name":"Comparative Evaluation of XAI Methods for Transparent Crop Yield Estimation Using CNN","publication_year":2024,"publication_date":"2024-07-07","ids":{"openalex":"https://openalex.org/W4402260285","doi":"https://doi.org/10.1109/igarss53475.2024.10641426"},"language":"en","primary_location":{"id":"doi:10.1109/igarss53475.2024.10641426","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss53475.2024.10641426","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://research.utwente.nl/en/publications/dddf3aa2-8b2c-411c-ab41-5284ea8d39fc","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5109776745","display_name":"Arun Vishwanath Venugopal","orcid":null},"institutions":[{"id":"https://openalex.org/I94624287","display_name":"University of Twente","ror":"https://ror.org/006hf6230","country_code":"NL","type":"education","lineage":["https://openalex.org/I94624287"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Arun Venugopal","raw_affiliation_strings":["University of Twente,Faculty of Geoinformation Science and Earth Observation (ITC),Enschede,The Netherlands,7500 AE"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Twente,Faculty of Geoinformation Science and Earth Observation (ITC),Enschede,The Netherlands,7500 AE","institution_ids":["https://openalex.org/I94624287"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011461918","display_name":"Mahdi Farnaghi","orcid":"https://orcid.org/0000-0002-3071-5563"},"institutions":[{"id":"https://openalex.org/I94624287","display_name":"University of Twente","ror":"https://ror.org/006hf6230","country_code":"NL","type":"education","lineage":["https://openalex.org/I94624287"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Mahdi Farnaghi","raw_affiliation_strings":["University of Twente,Faculty of Geoinformation Science and Earth Observation (ITC),Enschede,The Netherlands,7500 AE"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Twente,Faculty of Geoinformation Science and Earth Observation (ITC),Enschede,The Netherlands,7500 AE","institution_ids":["https://openalex.org/I94624287"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5075715085","display_name":"R. Zurita\u2010Milla","orcid":"https://orcid.org/0000-0002-1769-6310"},"institutions":[{"id":"https://openalex.org/I94624287","display_name":"University of Twente","ror":"https://ror.org/006hf6230","country_code":"NL","type":"education","lineage":["https://openalex.org/I94624287"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Ra\u00fal Zurita-Milla","raw_affiliation_strings":["University of Twente,Faculty of Geoinformation Science and Earth Observation (ITC),Enschede,The Netherlands,7500 AE"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Twente,Faculty of Geoinformation Science and Earth Observation (ITC),Enschede,The Netherlands,7500 AE","institution_ids":["https://openalex.org/I94624287"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I94624287"],"apc_list":null,"apc_paid":null,"fwci":4.2136,"has_fulltext":true,"cited_by_count":5,"citation_normalized_percentile":{"value":0.93974901,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"7478","last_page":"7482"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10616","display_name":"Smart Agriculture and AI","score":0.9988999962806702,"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":0.9988999962806702,"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/T10320","display_name":"Neural Networks and Applications","score":0.9460999965667725,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T13693","display_name":"Smart Systems and Machine Learning","score":0.9258999824523926,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/yield","display_name":"Yield (engineering)","score":0.5879914164543152},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5601948499679565},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4822276532649994},{"id":"https://openalex.org/keywords/estimation","display_name":"Estimation","score":0.45948800444602966},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4143407940864563},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.09843048453330994},{"id":"https://openalex.org/keywords/materials-science","display_name":"Materials science","score":0.054525226354599}],"concepts":[{"id":"https://openalex.org/C134121241","wikidata":"https://www.wikidata.org/wiki/Q899301","display_name":"Yield (engineering)","level":2,"score":0.5879914164543152},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5601948499679565},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4822276532649994},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.45948800444602966},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4143407940864563},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.09843048453330994},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.054525226354599},{"id":"https://openalex.org/C191897082","wikidata":"https://www.wikidata.org/wiki/Q11467","display_name":"Metallurgy","level":1,"score":0.0},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/igarss53475.2024.10641426","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss53475.2024.10641426","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium","raw_type":"proceedings-article"},{"id":"pmh:oai:ris.utwente.nl:openaire/dddf3aa2-8b2c-411c-ab41-5284ea8d39fc","is_oa":true,"landing_page_url":"https://research.utwente.nl/en/publications/dddf3aa2-8b2c-411c-ab41-5284ea8d39fc","pdf_url":null,"source":{"id":"https://openalex.org/S4406922991","display_name":"University of Twente Research Information","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":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Venugopal, A, Farnaghi, M & Zurita-Milla, R 2024, Comparative Evaluation of XAI Methods for Transparent Crop Yield Estimation Using CNN. in IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium., 10641426, IEEE, Athens, pp. 7478-7482, 2024 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2024, Athens, Greece, 7/07/24. https://doi.org/10.1109/IGARSS53475.2024.10641426","raw_type":"info:eu-repo/semantics/conferenceObject"},{"id":"pmh:oai:ris.utwente.nl:openaire_cris_publications/dddf3aa2-8b2c-411c-ab41-5284ea8d39fc","is_oa":true,"landing_page_url":"https://research.utwente.nl/files/459343225/Comparative_Evaluation_of_XAI_Methods_for_Transparent_Crop_Yield_Estimation_Using_CNN.pdf","pdf_url":"https://ris.utwente.nl/ws/files/459343225/Comparative_Evaluation_of_XAI_Methods_for_Transparent_Crop_Yield_Estimation_Using_CNN.pdf","source":{"id":"https://openalex.org/S4406922991","display_name":"University of Twente Research Information","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":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Venugopal, A, Farnaghi, M & Zurita-Milla, R 2024, Comparative Evaluation of XAI Methods for Transparent Crop Yield Estimation Using CNN. in IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium., 10641426, IEEE, Athens, pp. 7478-7482, 2024 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2024, Athens, Greece, 7/07/24. https://doi.org/10.1109/IGARSS53475.2024.10641426","raw_type":"info:eu-repo/semantics/conferenceObject"}],"best_oa_location":{"id":"pmh:oai:ris.utwente.nl:openaire/dddf3aa2-8b2c-411c-ab41-5284ea8d39fc","is_oa":true,"landing_page_url":"https://research.utwente.nl/en/publications/dddf3aa2-8b2c-411c-ab41-5284ea8d39fc","pdf_url":null,"source":{"id":"https://openalex.org/S4406922991","display_name":"University of Twente Research Information","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":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Venugopal, A, Farnaghi, M & Zurita-Milla, R 2024, Comparative Evaluation of XAI Methods for Transparent Crop Yield Estimation Using CNN. in IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium., 10641426, IEEE, Athens, pp. 7478-7482, 2024 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2024, Athens, Greece, 7/07/24. https://doi.org/10.1109/IGARSS53475.2024.10641426","raw_type":"info:eu-repo/semantics/conferenceObject"},"sustainable_development_goals":[{"display_name":"Zero hunger","id":"https://metadata.un.org/sdg/2","score":0.7699999809265137}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W1787224781","https://openalex.org/W2069466983","https://openalex.org/W2195388612","https://openalex.org/W2616247523","https://openalex.org/W2919115771","https://openalex.org/W2945976633","https://openalex.org/W2971201880","https://openalex.org/W2973136764","https://openalex.org/W2999884014","https://openalex.org/W3079760979","https://openalex.org/W3090979035","https://openalex.org/W3102564565","https://openalex.org/W3146764839","https://openalex.org/W3199384803","https://openalex.org/W4293861706","https://openalex.org/W6739575509","https://openalex.org/W6762929394","https://openalex.org/W7019859633"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052","https://openalex.org/W2033914206","https://openalex.org/W2042327336"],"abstract_inverted_index":{"While":[0],"Deep":[1,95],"Learning":[2],"(DL)":[3],"has":[4],"significantly":[5],"improved":[6],"the":[7,15,18,22,38,52,104,127,167,172,185],"estimation":[8,83,183],"of":[9,17,41,68,174],"crop":[10,34,136,160,181],"yields":[11],"from":[12,84],"satellite":[13,86],"imagery,":[14],"intricacies":[16],"decision-making":[19],"processes":[20],"within":[21],"utilized":[23],"DL":[24,42],"models":[25,43],"remain":[26],"obscure.":[27],"To":[28],"ensure":[29],"transparency":[30],"and":[31,97,154],"dependability":[32],"in":[33,184],"yield":[35,82,161,182],"predictions,":[36],"unravelling":[37],"complex":[39,175],"mechanisms":[40],"is":[44,48],"imperative.":[45],"This":[46,63],"task":[47],"challenging":[49],"due":[50],"to":[51,75,102,134],"varying":[53],"outcomes":[54],"presented":[55],"by":[56],"different":[57],"eXplainable":[58,69],"Artificial":[59,70],"Intelligence":[60,71],"(XAI)":[61,72],"methods.":[62],"study":[64,119],"examines":[65,121],"a":[66,115],"range":[67],"techniques":[73],"applied":[74],"Convolutional":[76],"Neural":[77],"Networks":[78],"(CNNs)":[79],"for":[80,159,180],"Soybean":[81,131],"Sentinel-2":[85],"imagery.":[87],"Methods":[88],"like":[89],"Layerwise":[90],"Relevance":[91],"Propagation":[92],"(LRP),":[93],"SmoothGrad,":[94],"Taylor,":[96],"gradCAM":[98],"have":[99],"been":[100],"employed":[101],"elucidate":[103],"CNN":[105,176],"model,":[106],"producing":[107],"saliency":[108,152],"maps":[109,153],"that":[110,144],"are":[111],"subsequently":[112],"evaluated":[113],"through":[114],"perturbation":[116],"analysis.":[117],"The":[118,163],"also":[120],"how":[122],"each":[123],"XAI":[124,168],"method":[125],"identifies":[126],"model's":[128],"focus":[129],"on":[130],"fields":[132,137],"compared":[133],"other":[135,147],"or":[138],"land":[139],"uses.":[140],"Our":[141],"findings":[142],"indicate":[143],"LRP":[145],"outperforms":[146],"methods,":[148],"offering":[149],"more":[150],"accurate":[151],"highlighting":[155],"critical":[156],"spatial":[157],"information":[158],"estimation.":[162],"knowledge":[164],"gained":[165],"about":[166],"methods":[169],"facilitates":[170],"understanding":[171],"behaviour":[173],"model":[177],"architecture":[178],"used":[179],"future.":[186]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":1}],"updated_date":"2026-08-18T07:49:30.821534","created_date":"2025-10-10T00:00:00"}