{"id":"https://openalex.org/W7128534908","doi":"https://doi.org/10.18420/giljt2026_14","title":"Improving transparency in machine learning for agriculture: a case study on mushroom classification using CNN and Grad-CAM","display_name":"Improving transparency in machine learning for agriculture: a case study on mushroom classification using CNN and Grad-CAM","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7128534908","doi":"https://doi.org/10.18420/giljt2026_14"},"language":"en","primary_location":{"id":"pmh:doi:10.18420/giljt2026_14","is_oa":false,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Text/Conference Paper"},"type":"article","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.18420/giljt2026_14","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5125511793","display_name":"Xiao Wei","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Wei, Xiao","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5120811776","display_name":"Alina Griesel","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Griesel, Alina","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5125549480","display_name":"Martin Atzmueller","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Atzmueller, Martin","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5125511793"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.36836831,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10616","display_name":"Smart Agriculture and AI","score":0.8794999718666077,"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":0.8794999718666077,"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/T10111","display_name":"Remote Sensing in Agriculture","score":0.06759999692440033,"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"}},{"id":"https://openalex.org/T10640","display_name":"Spectroscopy and Chemometric Analyses","score":0.011800000444054604,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/interpretability","display_name":"Interpretability","score":0.8658999800682068},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6344000101089478},{"id":"https://openalex.org/keywords/transparency","display_name":"Transparency (behavior)","score":0.5327000021934509},{"id":"https://openalex.org/keywords/precision-and-recall","display_name":"Precision and recall","score":0.4903999865055084},{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.478300005197525},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4147999882698059},{"id":"https://openalex.org/keywords/comparability","display_name":"Comparability","score":0.4049000144004822},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.3628000020980835}],"concepts":[{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.8658999800682068},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.78329998254776},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.7544000148773193},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7228000164031982},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6344000101089478},{"id":"https://openalex.org/C2780233690","wikidata":"https://www.wikidata.org/wiki/Q535347","display_name":"Transparency (behavior)","level":2,"score":0.5327000021934509},{"id":"https://openalex.org/C81669768","wikidata":"https://www.wikidata.org/wiki/Q2359161","display_name":"Precision and recall","level":2,"score":0.4903999865055084},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.478300005197525},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4147999882698059},{"id":"https://openalex.org/C197947376","wikidata":"https://www.wikidata.org/wiki/Q5155608","display_name":"Comparability","level":2,"score":0.4049000144004822},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3628000020980835},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.35409998893737793},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.3167000114917755},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.31540000438690186},{"id":"https://openalex.org/C100660578","wikidata":"https://www.wikidata.org/wiki/Q18733","display_name":"Recall","level":2,"score":0.3050000071525574},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.290800005197525},{"id":"https://openalex.org/C120217122","wikidata":"https://www.wikidata.org/wiki/Q740083","display_name":"Precision agriculture","level":3,"score":0.2840999960899353},{"id":"https://openalex.org/C2776474821","wikidata":"https://www.wikidata.org/wiki/Q83093","display_name":"Mushroom","level":2,"score":0.2815000116825104},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.27630001306533813},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.27549999952316284},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2754000127315521},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.2653999924659729},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.25450000166893005}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:doi:10.18420/giljt2026_14","is_oa":false,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Text/Conference Paper"},{"id":"doi:10.18420/giljt2026_14","is_oa":true,"landing_page_url":"https://doi.org/10.18420/giljt2026_14","pdf_url":null,"source":{"id":"https://openalex.org/S7407052918","display_name":"Gesellschaft f\u00fcr Informatik (GI)","issn_l":null,"issn":[],"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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article-journal"}],"best_oa_location":{"id":"doi:10.18420/giljt2026_14","is_oa":true,"landing_page_url":"https://doi.org/10.18420/giljt2026_14","pdf_url":null,"source":{"id":"https://openalex.org/S7407052918","display_name":"Gesellschaft f\u00fcr Informatik (GI)","issn_l":null,"issn":[],"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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article-journal"},"sustainable_development_goals":[{"display_name":"Zero hunger","score":0.7587760090827942,"id":"https://metadata.un.org/sdg/2"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Machine":[0],"learning":[1],"(ML)":[2],"is":[3,17],"a":[4,49,60,77],"powerful":[5],"tool":[6],"for":[7,57],"precision":[8,94],"agriculture":[9],"(PA)":[10],"in":[11],"general.":[12],"However,":[13],"its":[14],"widespread":[15],"adoption":[16],"constrained":[18],"by":[19],"lacking":[20],"transparency.":[21],"To":[22],"address":[23],"this":[24,26],"gap,":[25],"study":[27,79],"integrates":[28],"ML":[29,52],"with":[30,59,123],"Explainable":[31],"AI":[32],"(XAI)":[33],"methods":[34],"to":[35,105],"enhance":[36],"both":[37],"the":[38,44,107,112,119],"predictive":[39,129],"performance":[40],"and":[41,53,68,98],"interpretability":[42],"of":[43,51,121],"respective":[45],"models.":[46],"We":[47],"provide":[48],"review":[50],"XAI":[54],"techniques":[55],"suitable":[56],"PA,":[58],"specific":[61],"focus":[62],"on":[63,80,131],"Convolutional":[64],"Neural":[65],"Networks":[66],"(CNNs)":[67],"Gradient-weighted":[69],"Class":[70],"Activation":[71],"Mapping":[72],"(Grad-CAM).":[73],"Furthermore,":[74],"we":[75],"conducted":[76],"case":[78],"mushroom":[81],"hyperspectral":[82],"image":[83],"classification.":[84],"A":[85],"2D-CNN":[86],"model":[87,138],"was":[88,102],"developed,":[89],"achieving":[90],"average":[91],"accuracy":[92,130],"98.04%,":[93],"98.96%,":[95],"recall":[96],"96.30%,":[97],"F1-score":[99],"97.51%.":[100],"Grad-CAM":[101,124],"then":[103],"applied":[104],"identify":[106],"critical":[108],"pixels":[109],"that":[110,118],"informed":[111],"model\u2019s":[113],"classifications.":[114],"The":[115],"results":[116],"demonstrate":[117],"integration":[120],"CNNs":[122],"not":[125],"only":[126],"achieves":[127],"high":[128],"complex":[132],"agricultural":[133],"data":[134],"but":[135],"also":[136],"enhances":[137],"interpretability.":[139]},"counts_by_year":[],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2026-02-11T00:00:00"}