{"id":"https://openalex.org/W3169737088","doi":"https://doi.org/10.3390/rs13112140","title":"Influence of Image Quality and Light Consistency on the Performance of Convolutional Neural Networks for Weed Mapping","display_name":"Influence of Image Quality and Light Consistency on the Performance of Convolutional Neural Networks for Weed Mapping","publication_year":2021,"publication_date":"2021-05-29","ids":{"openalex":"https://openalex.org/W3169737088","doi":"https://doi.org/10.3390/rs13112140","mag":"3169737088"},"language":"en","primary_location":{"id":"doi:10.3390/rs13112140","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs13112140","pdf_url":"https://www.mdpi.com/2072-4292/13/11/2140/pdf?version=1622276970","source":{"id":"https://openalex.org/S43295729","display_name":"Remote Sensing","issn_l":"2072-4292","issn":["2072-4292"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Remote Sensing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/2072-4292/13/11/2140/pdf?version=1622276970","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5066548429","display_name":"Chengsong Hu","orcid":"https://orcid.org/0000-0003-4220-8566"},"institutions":[{"id":"https://openalex.org/I91045830","display_name":"Texas A&M University","ror":"https://ror.org/01f5ytq51","country_code":"US","type":"education","lineage":["https://openalex.org/I91045830"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chengsong Hu","raw_affiliation_strings":["Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843, USA"],"affiliations":[{"raw_affiliation_string":"Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843, USA","institution_ids":["https://openalex.org/I91045830"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057936616","display_name":"Bishwa Sapkota","orcid":"https://orcid.org/0000-0002-0674-7442"},"institutions":[{"id":"https://openalex.org/I91045830","display_name":"Texas A&M University","ror":"https://ror.org/01f5ytq51","country_code":"US","type":"education","lineage":["https://openalex.org/I91045830"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Bishwa B. Sapkota","raw_affiliation_strings":["Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843, USA"],"affiliations":[{"raw_affiliation_string":"Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843, USA","institution_ids":["https://openalex.org/I91045830"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5022600991","display_name":"J. Alex Thomasson","orcid":"https://orcid.org/0000-0002-5557-105X"},"institutions":[{"id":"https://openalex.org/I99041443","display_name":"Mississippi State University","ror":"https://ror.org/0432jq872","country_code":"US","type":"education","lineage":["https://openalex.org/I4210141039","https://openalex.org/I99041443"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"J. Alex Thomasson","raw_affiliation_strings":["Department of Agricultural and Biological Engineering, Mississippi State University, Starkville, MS 39759, USA"],"affiliations":[{"raw_affiliation_string":"Department of Agricultural and Biological Engineering, Mississippi State University, Starkville, MS 39759, USA","institution_ids":["https://openalex.org/I99041443"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5030804351","display_name":"Muthukumar Bagavathiannan","orcid":"https://orcid.org/0000-0002-1107-7148"},"institutions":[{"id":"https://openalex.org/I91045830","display_name":"Texas A&M University","ror":"https://ror.org/01f5ytq51","country_code":"US","type":"education","lineage":["https://openalex.org/I91045830"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Muthukumar V. Bagavathiannan","raw_affiliation_strings":["Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843, USA"],"affiliations":[{"raw_affiliation_string":"Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843, USA","institution_ids":["https://openalex.org/I91045830"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5030804351"],"corresponding_institution_ids":["https://openalex.org/I91045830"],"apc_list":{"value":2500,"currency":"CHF","value_usd":2707},"apc_paid":{"value":2500,"currency":"CHF","value_usd":2707},"fwci":7.0803,"has_fulltext":true,"cited_by_count":57,"citation_normalized_percentile":{"value":0.96649521,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":95,"max":100},"biblio":{"volume":"13","issue":"11","first_page":"2140","last_page":"2140"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10616","display_name":"Smart Agriculture and AI","score":0.9991000294685364,"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":0.9991000294685364,"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":0.9923999905586243,"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/T10825","display_name":"Plant Pathogens and Fungal Diseases","score":0.9904000163078308,"subfield":{"id":"https://openalex.org/subfields/1307","display_name":"Cell Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8218653202056885},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.803921103477478},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7206681966781616},{"id":"https://openalex.org/keywords/motion-blur","display_name":"Motion blur","score":0.692237377166748},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5315185785293579},{"id":"https://openalex.org/keywords/image-quality","display_name":"Image quality","score":0.5163310766220093},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.507358968257904},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.46925923228263855},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4603090286254883},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.45260727405548096},{"id":"https://openalex.org/keywords/gaussian-blur","display_name":"Gaussian blur","score":0.42893919348716736},{"id":"https://openalex.org/keywords/image-restoration","display_name":"Image restoration","score":0.33068838715553284},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.3116123676300049},{"id":"https://openalex.org/keywords/image-processing","display_name":"Image processing","score":0.2311697006225586}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8218653202056885},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.803921103477478},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7206681966781616},{"id":"https://openalex.org/C2777708103","wikidata":"https://www.wikidata.org/wiki/Q852589","display_name":"Motion blur","level":3,"score":0.692237377166748},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5315185785293579},{"id":"https://openalex.org/C55020928","wikidata":"https://www.wikidata.org/wiki/Q3813865","display_name":"Image quality","level":3,"score":0.5163310766220093},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.507358968257904},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.46925923228263855},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4603090286254883},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.45260727405548096},{"id":"https://openalex.org/C104317376","wikidata":"https://www.wikidata.org/wiki/Q1894545","display_name":"Gaussian blur","level":5,"score":0.42893919348716736},{"id":"https://openalex.org/C106430172","wikidata":"https://www.wikidata.org/wiki/Q6002272","display_name":"Image restoration","level":4,"score":0.33068838715553284},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3116123676300049},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.2311697006225586}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.3390/rs13112140","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs13112140","pdf_url":"https://www.mdpi.com/2072-4292/13/11/2140/pdf?version=1622276970","source":{"id":"https://openalex.org/S43295729","display_name":"Remote Sensing","issn_l":"2072-4292","issn":["2072-4292"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Remote Sensing","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:a1e3372b1f184a0b8baba38124558a93","is_oa":true,"landing_page_url":"https://doaj.org/article/a1e3372b1f184a0b8baba38124558a93","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Remote Sensing, Vol 13, Iss 11, p 2140 (2021)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/2072-4292/13/11/2140/","is_oa":true,"landing_page_url":"https://dx.doi.org/10.3390/rs13112140","pdf_url":null,"source":{"id":"https://openalex.org/S4306400947","display_name":"MDPI (MDPI AG)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210097602","host_organization_name":"Multidisciplinary Digital Publishing Institute (Switzerland)","host_organization_lineage":["https://openalex.org/I4210097602"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Remote Sensing","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/rs13112140","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs13112140","pdf_url":"https://www.mdpi.com/2072-4292/13/11/2140/pdf?version=1622276970","source":{"id":"https://openalex.org/S43295729","display_name":"Remote Sensing","issn_l":"2072-4292","issn":["2072-4292"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Remote Sensing","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G7910822968","display_name":null,"funder_award_id":"000-000","funder_id":"https://openalex.org/F4320332782","funder_display_name":"Natural Resources Conservation Service"}],"funders":[{"id":"https://openalex.org/F4320306114","display_name":"U.S. Department of Agriculture","ror":"https://ror.org/01na82s61"},{"id":"https://openalex.org/F4320332782","display_name":"Natural Resources Conservation Service","ror":"https://ror.org/03j7rgg33"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3169737088.pdf","grobid_xml":"https://content.openalex.org/works/W3169737088.grobid-xml"},"referenced_works_count":59,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W1526662621","https://openalex.org/W1861492603","https://openalex.org/W1963983606","https://openalex.org/W1966058822","https://openalex.org/W1967422549","https://openalex.org/W1980041094","https://openalex.org/W1996312143","https://openalex.org/W2008857867","https://openalex.org/W2037227137","https://openalex.org/W2037525384","https://openalex.org/W2041344854","https://openalex.org/W2064782300","https://openalex.org/W2069281566","https://openalex.org/W2109923042","https://openalex.org/W2117393779","https://openalex.org/W2122410868","https://openalex.org/W2126619114","https://openalex.org/W2130754596","https://openalex.org/W2146404768","https://openalex.org/W2148255984","https://openalex.org/W2153207916","https://openalex.org/W2156015172","https://openalex.org/W2160715448","https://openalex.org/W2163446914","https://openalex.org/W2163984493","https://openalex.org/W2329398414","https://openalex.org/W2337024056","https://openalex.org/W2511484725","https://openalex.org/W2523246573","https://openalex.org/W2609077090","https://openalex.org/W2612573399","https://openalex.org/W2630837129","https://openalex.org/W2739413041","https://openalex.org/W2804860796","https://openalex.org/W2805859680","https://openalex.org/W2807720854","https://openalex.org/W2810270273","https://openalex.org/W2904067944","https://openalex.org/W2922259208","https://openalex.org/W2943118732","https://openalex.org/W2949292735","https://openalex.org/W2962953743","https://openalex.org/W2963150697","https://openalex.org/W2963661166","https://openalex.org/W2963980515","https://openalex.org/W2970971581","https://openalex.org/W2985321619","https://openalex.org/W3010677011","https://openalex.org/W3104668471","https://openalex.org/W4211263593","https://openalex.org/W4242761880","https://openalex.org/W4254663953","https://openalex.org/W4295312788","https://openalex.org/W6668062122","https://openalex.org/W6682931779","https://openalex.org/W6683488002","https://openalex.org/W6702310129","https://openalex.org/W6766978945"],"related_works":["https://openalex.org/W4378212301","https://openalex.org/W2809398103","https://openalex.org/W2189446904","https://openalex.org/W2325088400","https://openalex.org/W2029783634","https://openalex.org/W1610044448","https://openalex.org/W2140292627","https://openalex.org/W2147326311","https://openalex.org/W2547347870","https://openalex.org/W2111547392"],"abstract_inverted_index":{"Recent":[0],"computer":[1],"vision":[2],"techniques":[3],"based":[4],"on":[5,44,54,156,188,255],"convolutional":[6],"neural":[7],"networks":[8],"(CNNs)":[9],"are":[10],"considered":[11],"state-of-the-art":[12],"tools":[13],"in":[14,31,59,97,215,262,275],"weed":[15,40,60,277],"mapping.":[16,41],"However,":[17,184],"their":[18],"performance":[19,56,116,222],"has":[20],"been":[21],"shown":[22],"to":[23,26,39,144,268],"be":[24,266],"sensitive":[25],"image":[27,49,65,250],"quality":[28,50,173,195,251,259],"degradation.":[29],"Variation":[30],"lighting":[32,213,240],"conditions":[33,214],"adds":[34],"another":[35],"level":[36],"of":[37,48,57,123,147,212,235,249,272],"complexity":[38],"We":[42],"focus":[43],"determining":[45],"the":[46,55,64,114,127,133,157,176,210,216,221,228,232,238,247,270],"influence":[47],"and":[51,72,83,108,129,138,153,164,178,252],"light":[52,253],"consistency":[53,254],"CNNs":[58],"mapping":[61,278],"by":[62,92,120,200],"simulating":[63],"formation":[66],"pipeline.":[67],"Faster":[68,136],"Region-based":[69],"CNN":[70,78,115,182,185,207,256],"(R-CNN)":[71],"Mask":[73,139],"R-CNN":[74,137,140],"were":[75,141,191],"used":[76,267],"as":[77],"examples":[79],"for":[80],"object":[81],"detection":[82],"instance":[84],"segmentation,":[85],"respectively,":[86],"while":[87],"semantic":[88],"segmentation":[89],"was":[90,117],"represented":[91],"Deeplab-v3.":[93],"The":[94,110,258],"degradations":[95],"simulated":[96],"this":[98,263],"study":[99,264],"included":[100],"resolution":[101],"reduction,":[102],"overexposure,":[103,148,161],"Gaussian":[104,149],"blur,":[105,107,150,152,163],"motion":[106,151,162],"noise.":[109,154],"results":[111,243],"showed":[112],"that":[113],"most":[118,171],"impacted":[119],"resolution,":[121],"regardless":[122],"plant":[124],"size.":[125],"When":[126],"training":[128,177,217],"testing":[130,179],"images":[131,180,190,218,236],"had":[132],"same":[134,229,239],"quality,":[135],"moderately":[142],"tolerant":[143,193],"low":[145],"levels":[146],"Deeplab-v3,":[155],"other":[158],"hand,":[159],"tolerated":[160],"noise":[165],"at":[166],"all":[167],"tested":[168],"levels.":[169],"In":[170],"cases,":[172],"inconsistency":[174,196,204],"between":[175],"reduced":[181,206],"performance.":[183,208,257],"models":[186],"trained":[187,199],"low-quality":[189],"more":[192],"against":[194],"than":[197],"those":[198],"high-quality":[201],"images.":[202],"Light":[203],"also":[205],"Increasing":[209],"diversity":[211],"may":[219],"alleviate":[220],"reduction":[223],"but":[224],"does":[225],"not":[226],"provide":[227,244],"benefit":[230],"from":[231],"number":[233],"increase":[234],"with":[237],"condition.":[241],"These":[242],"insights":[245],"into":[246],"impact":[248],"threshold":[260],"established":[261],"can":[265],"guide":[269],"selection":[271],"camera":[273],"parameters":[274],"future":[276],"applications.":[279]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":22},{"year":2024,"cited_by_count":10},{"year":2023,"cited_by_count":8},{"year":2022,"cited_by_count":11},{"year":2021,"cited_by_count":3}],"updated_date":"2026-03-31T07:56:22.981413","created_date":"2025-10-10T00:00:00"}
