{"id":"https://openalex.org/W3129741846","doi":"https://doi.org/10.1109/igarss39084.2020.9323425","title":"PREDICTION OF SORGHUM BIOMASS USING TIME SERIES UAV-BASED HYPERSPECTRAL AND LIDAR DATA","display_name":"PREDICTION OF SORGHUM BIOMASS USING TIME SERIES UAV-BASED HYPERSPECTRAL AND LIDAR DATA","publication_year":2020,"publication_date":"2020-09-26","ids":{"openalex":"https://openalex.org/W3129741846","doi":"https://doi.org/10.1109/igarss39084.2020.9323425","mag":"3129741846"},"language":"en","primary_location":{"id":"doi:10.1109/igarss39084.2020.9323425","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss39084.2020.9323425","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium","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/A5001394107","display_name":"Ali Masjedi","orcid":"https://orcid.org/0000-0003-3214-4132"},"institutions":[{"id":"https://openalex.org/I219193219","display_name":"Purdue University West Lafayette","ror":"https://ror.org/02dqehb95","country_code":"US","type":"education","lineage":["https://openalex.org/I219193219"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ali Masjedi","raw_affiliation_strings":["Purdue University, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Purdue University, USA","institution_ids":["https://openalex.org/I219193219"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5002083374","display_name":"Melba M. Crawford","orcid":null},"institutions":[{"id":"https://openalex.org/I219193219","display_name":"Purdue University West Lafayette","ror":"https://ror.org/02dqehb95","country_code":"US","type":"education","lineage":["https://openalex.org/I219193219"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Melba M. Crawford","raw_affiliation_strings":["Purdue University, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Purdue University, USA","institution_ids":["https://openalex.org/I219193219"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.8042,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.74212182,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":"16","issue":null,"first_page":"3912","last_page":"3915"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10111","display_name":"Remote Sensing in Agriculture","score":0.9975000023841858,"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"}},"topics":[{"id":"https://openalex.org/T10111","display_name":"Remote Sensing in Agriculture","score":0.9975000023841858,"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":0.9883000254631042,"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/T10616","display_name":"Smart Agriculture and AI","score":0.986299991607666,"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/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.8712857961654663},{"id":"https://openalex.org/keywords/lidar","display_name":"Lidar","score":0.6925328969955444},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6789901852607727},{"id":"https://openalex.org/keywords/ranging","display_name":"Ranging","score":0.5661789774894714},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.5589343309402466},{"id":"https://openalex.org/keywords/sensor-fusion","display_name":"Sensor fusion","score":0.5352905988693237},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.528545081615448},{"id":"https://openalex.org/keywords/data-acquisition","display_name":"Data acquisition","score":0.46402671933174133},{"id":"https://openalex.org/keywords/data-analysis","display_name":"Data analysis","score":0.4421636462211609},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.4412974715232849},{"id":"https://openalex.org/keywords/biomass","display_name":"Biomass (ecology)","score":0.41859617829322815},{"id":"https://openalex.org/keywords/precision-agriculture","display_name":"Precision agriculture","score":0.4139881432056427},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.39440345764160156},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.34792429208755493},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.34704703092575073},{"id":"https://openalex.org/keywords/agriculture","display_name":"Agriculture","score":0.16259706020355225},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.07415702939033508},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.07116344571113586}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.8712857961654663},{"id":"https://openalex.org/C51399673","wikidata":"https://www.wikidata.org/wiki/Q504027","display_name":"Lidar","level":2,"score":0.6925328969955444},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6789901852607727},{"id":"https://openalex.org/C115051666","wikidata":"https://www.wikidata.org/wiki/Q6522493","display_name":"Ranging","level":2,"score":0.5661789774894714},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.5589343309402466},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.5352905988693237},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.528545081615448},{"id":"https://openalex.org/C163985040","wikidata":"https://www.wikidata.org/wiki/Q1172399","display_name":"Data acquisition","level":2,"score":0.46402671933174133},{"id":"https://openalex.org/C175801342","wikidata":"https://www.wikidata.org/wiki/Q1988917","display_name":"Data analysis","level":2,"score":0.4421636462211609},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.4412974715232849},{"id":"https://openalex.org/C115540264","wikidata":"https://www.wikidata.org/wiki/Q2945560","display_name":"Biomass (ecology)","level":2,"score":0.41859617829322815},{"id":"https://openalex.org/C120217122","wikidata":"https://www.wikidata.org/wiki/Q740083","display_name":"Precision agriculture","level":3,"score":0.4139881432056427},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.39440345764160156},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.34792429208755493},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.34704703092575073},{"id":"https://openalex.org/C118518473","wikidata":"https://www.wikidata.org/wiki/Q11451","display_name":"Agriculture","level":2,"score":0.16259706020355225},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.07415702939033508},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.07116344571113586},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0},{"id":"https://openalex.org/C111368507","wikidata":"https://www.wikidata.org/wiki/Q43518","display_name":"Oceanography","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igarss39084.2020.9323425","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss39084.2020.9323425","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7300000190734863,"display_name":"Zero hunger","id":"https://metadata.un.org/sdg/2"}],"awards":[{"id":"https://openalex.org/G8998192501","display_name":null,"funder_award_id":"DE-AR0000593","funder_id":"https://openalex.org/F4320306084","funder_display_name":"U.S. Department of Energy"}],"funders":[{"id":"https://openalex.org/F4320306084","display_name":"U.S. Department of Energy","ror":"https://ror.org/01bj3aw27"},{"id":"https://openalex.org/F4320332815","display_name":"Advanced Research Projects Agency","ror":"https://ror.org/02caytj08"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W1951216520","https://openalex.org/W2020520344","https://openalex.org/W2402144811","https://openalex.org/W2600746131","https://openalex.org/W2604645045","https://openalex.org/W2764034829","https://openalex.org/W2782522152","https://openalex.org/W2791525675","https://openalex.org/W2795059173","https://openalex.org/W2798379399","https://openalex.org/W2901602754","https://openalex.org/W2953384591","https://openalex.org/W3001772727","https://openalex.org/W6713134421","https://openalex.org/W6735637195"],"related_works":["https://openalex.org/W2783354812","https://openalex.org/W4384112194","https://openalex.org/W2103009189","https://openalex.org/W4312958259","https://openalex.org/W2349383066","https://openalex.org/W1969901537","https://openalex.org/W4328132048","https://openalex.org/W4308259661","https://openalex.org/W3148498243","https://openalex.org/W2423553421"],"abstract_inverted_index":{"Recent":[0],"advances":[1],"in":[2,84],"remote":[3],"sensing":[4],"technology":[5],"and":[6,41,71,74,106],"algorithms":[7],"for":[8,15,101,108],"data":[9,25,34,38,81],"analysis":[10],"potentially":[11],"provide":[12],"significant":[13],"utility":[14],"agricultural":[16,109],"applications.":[17],"This":[18],"paper":[19],"focuses":[20],"on":[21,48,95],"development":[22],"of":[23,99],"a":[24],"analytics":[26],"based":[27,47],"predictive":[28],"modeling":[29],"strategy":[30],"that":[31],"incorporates":[32],"multi-temporal":[33,60],"acquired":[35],"by":[36],"multi-sensor":[37],"acquisition":[39],"systems,":[40],"accommodates":[42],"environmental":[43],"inputs.":[44],"Predictive":[45],"models":[46,86],"Recurrent":[49],"Neural":[50],"Networks":[51],"(RNNs)":[52],"are":[53,82,104],"developed":[54],"to":[55,87],"incorporate":[56],"high":[57,96],"dimensional,":[58],"multi-modal,":[59],"input":[61],"data.":[62],"Remote":[63],"Sensing":[64],"(RS)":[65],"features":[66],"derived":[67],"from":[68,91],"Light":[69],"Detection":[70],"Ranging":[72],"(LiDAR)":[73],"hyperspectral":[75],"inputs,":[76],"as":[77,79],"well":[78],"weather-related":[80],"incorporated":[83],"RNN":[85],"predict":[88],"yield.":[89],"Results":[90],"multiple":[92],"experiments":[93],"focused":[94],"throughput":[97],"phenotyping":[98],"sorghum":[100],"biomass":[102],"predictions":[103],"provided":[105],"evaluated":[107],"test":[110],"fields":[111],"at":[112],"Purdue":[113],"University.":[114]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}