{"id":"https://openalex.org/W2904453310","doi":"https://doi.org/10.1109/coase.2018.8560431","title":"Towards Automating Precision Irrigation: Deep Learning to Infer Local Soil Moisture Conditions from Synthetic Aerial Agricultural Images","display_name":"Towards Automating Precision Irrigation: Deep Learning to Infer Local Soil Moisture Conditions from Synthetic Aerial Agricultural Images","publication_year":2018,"publication_date":"2018-08-01","ids":{"openalex":"https://openalex.org/W2904453310","doi":"https://doi.org/10.1109/coase.2018.8560431","mag":"2904453310"},"language":"en","primary_location":{"id":"doi:10.1109/coase.2018.8560431","is_oa":false,"landing_page_url":"https://doi.org/10.1109/coase.2018.8560431","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE 14th International Conference on Automation Science and Engineering (CASE)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5025083003","display_name":"David Tseng","orcid":null},"institutions":[{"id":"https://openalex.org/I134446601","display_name":"Berkeley College","ror":"https://ror.org/02xewxa75","country_code":"US","type":"education","lineage":["https://openalex.org/I134446601"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"David Tseng","raw_affiliation_strings":["EECS, Berkeley, UC"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"EECS, Berkeley, UC","institution_ids":["https://openalex.org/I134446601"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100652804","display_name":"David Wang","orcid":"https://orcid.org/0000-0002-0827-196X"},"institutions":[{"id":"https://openalex.org/I134446601","display_name":"Berkeley College","ror":"https://ror.org/02xewxa75","country_code":"US","type":"education","lineage":["https://openalex.org/I134446601"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"David Wang","raw_affiliation_strings":["EECS, Berkeley, UC"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"EECS, Berkeley, UC","institution_ids":["https://openalex.org/I134446601"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014379165","display_name":"Carolyn Chen","orcid":"https://orcid.org/0000-0003-4933-6544"},"institutions":[{"id":"https://openalex.org/I134446601","display_name":"Berkeley College","ror":"https://ror.org/02xewxa75","country_code":"US","type":"education","lineage":["https://openalex.org/I134446601"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Carolyn Chen","raw_affiliation_strings":["EECS, Berkeley, UC"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"EECS, Berkeley, UC","institution_ids":["https://openalex.org/I134446601"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021534233","display_name":"Lauren Miller","orcid":null},"institutions":[{"id":"https://openalex.org/I134446601","display_name":"Berkeley College","ror":"https://ror.org/02xewxa75","country_code":"US","type":"education","lineage":["https://openalex.org/I134446601"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Lauren Miller","raw_affiliation_strings":["EECS, Berkeley, UC"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"EECS, Berkeley, UC","institution_ids":["https://openalex.org/I134446601"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089063205","display_name":"William Hasung Song","orcid":null},"institutions":[{"id":"https://openalex.org/I134446601","display_name":"Berkeley College","ror":"https://ror.org/02xewxa75","country_code":"US","type":"education","lineage":["https://openalex.org/I134446601"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"William Song","raw_affiliation_strings":["EECS, Berkeley, UC"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"EECS, Berkeley, UC","institution_ids":["https://openalex.org/I134446601"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029143767","display_name":"Joshua H. Viers","orcid":"https://orcid.org/0000-0001-7957-7942"},"institutions":[{"id":"https://openalex.org/I4210128050","display_name":"Watershed","ror":"https://ror.org/02cfj6k65","country_code":"GB","type":"company","lineage":["https://openalex.org/I4210128050"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Joshua Viers","raw_affiliation_strings":["Center for Watershed Sciences, Merced, UC"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Center for Watershed Sciences, Merced, UC","institution_ids":["https://openalex.org/I4210128050"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047683157","display_name":"Stavros Vougioukas","orcid":"https://orcid.org/0000-0003-2758-8900"},"institutions":[{"id":"https://openalex.org/I84218800","display_name":"University of California, Davis","ror":"https://ror.org/05rrcem69","country_code":"US","type":"education","lineage":["https://openalex.org/I84218800"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Stavros Vougioukas","raw_affiliation_strings":["Biological and Agricultural Engineering, Davis, UC"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Biological and Agricultural Engineering, Davis, UC","institution_ids":["https://openalex.org/I84218800"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081177797","display_name":"Stefano Carpin","orcid":"https://orcid.org/0000-0003-3837-7463"},"institutions":[{"id":"https://openalex.org/I156087764","display_name":"University of California, Merced","ror":"https://ror.org/00d9ah105","country_code":"US","type":"education","lineage":["https://openalex.org/I156087764"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Stefano Carpin","raw_affiliation_strings":["School of Engineering, Merced, UC"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Engineering, Merced, UC","institution_ids":["https://openalex.org/I156087764"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5038472921","display_name":"Juan Aparicio Ojea","orcid":null},"institutions":[{"id":"https://openalex.org/I1325886976","display_name":"Siemens (Germany)","ror":"https://ror.org/059mq0909","country_code":"DE","type":"company","lineage":["https://openalex.org/I1325886976"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Juan Aparicio Ojea","raw_affiliation_strings":["Siemens Corporation, Corporate Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Siemens Corporation, Corporate Technology","institution_ids":["https://openalex.org/I1325886976"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5050342525","display_name":"Ken Goldberg","orcid":"https://orcid.org/0000-0002-2661-4524"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ken Goldberg","raw_affiliation_strings":["IEOR, Berkeley, UC"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IEOR, Berkeley, UC","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":9.4076,"has_fulltext":false,"cited_by_count":38,"citation_normalized_percentile":{"value":0.98255142,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"284","last_page":"291"},"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":0.9948999881744385,"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/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.9904000163078308,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"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/irrigation","display_name":"Irrigation","score":0.6709959506988525},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5307977795600891},{"id":"https://openalex.org/keywords/agriculture","display_name":"Agriculture","score":0.5091111063957214},{"id":"https://openalex.org/keywords/water-content","display_name":"Water content","score":0.5080485343933105},{"id":"https://openalex.org/keywords/agricultural-engineering","display_name":"Agricultural engineering","score":0.5025155544281006},{"id":"https://openalex.org/keywords/environmental-science","display_name":"Environmental science","score":0.5008604526519775},{"id":"https://openalex.org/keywords/precision-agriculture","display_name":"Precision agriculture","score":0.49276426434516907},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.46528688073158264},{"id":"https://openalex.org/keywords/aerial-imagery","display_name":"Aerial imagery","score":0.4591279923915863},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.4129127264022827},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.1411486566066742},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.12989631295204163},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.1294521689414978},{"id":"https://openalex.org/keywords/agronomy","display_name":"Agronomy","score":0.09254950284957886},{"id":"https://openalex.org/keywords/geotechnical-engineering","display_name":"Geotechnical engineering","score":0.0643417239189148}],"concepts":[{"id":"https://openalex.org/C88862950","wikidata":"https://www.wikidata.org/wiki/Q11453","display_name":"Irrigation","level":2,"score":0.6709959506988525},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5307977795600891},{"id":"https://openalex.org/C118518473","wikidata":"https://www.wikidata.org/wiki/Q11451","display_name":"Agriculture","level":2,"score":0.5091111063957214},{"id":"https://openalex.org/C24939127","wikidata":"https://www.wikidata.org/wiki/Q373499","display_name":"Water content","level":2,"score":0.5080485343933105},{"id":"https://openalex.org/C88463610","wikidata":"https://www.wikidata.org/wiki/Q194118","display_name":"Agricultural engineering","level":1,"score":0.5025155544281006},{"id":"https://openalex.org/C39432304","wikidata":"https://www.wikidata.org/wiki/Q188847","display_name":"Environmental science","level":0,"score":0.5008604526519775},{"id":"https://openalex.org/C120217122","wikidata":"https://www.wikidata.org/wiki/Q740083","display_name":"Precision agriculture","level":3,"score":0.49276426434516907},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.46528688073158264},{"id":"https://openalex.org/C2987819851","wikidata":"https://www.wikidata.org/wiki/Q191839","display_name":"Aerial imagery","level":2,"score":0.4591279923915863},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.4129127264022827},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.1411486566066742},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.12989631295204163},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.1294521689414978},{"id":"https://openalex.org/C6557445","wikidata":"https://www.wikidata.org/wiki/Q173113","display_name":"Agronomy","level":1,"score":0.09254950284957886},{"id":"https://openalex.org/C187320778","wikidata":"https://www.wikidata.org/wiki/Q1349130","display_name":"Geotechnical engineering","level":1,"score":0.0643417239189148},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","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":1,"locations":[{"id":"doi:10.1109/coase.2018.8560431","is_oa":false,"landing_page_url":"https://doi.org/10.1109/coase.2018.8560431","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE 14th International Conference on Automation Science and Engineering (CASE)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Clean water and sanitation","score":0.41999998688697815,"id":"https://metadata.un.org/sdg/6"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320307791","display_name":"Cisco Systems","ror":"https://ror.org/03yt1ez60"},{"id":"https://openalex.org/F4320332195","display_name":"Samsung","ror":"https://ror.org/04w3jy968"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":50,"referenced_works":["https://openalex.org/W37583849","https://openalex.org/W98486258","https://openalex.org/W645748567","https://openalex.org/W1522301498","https://openalex.org/W1659349535","https://openalex.org/W1968896562","https://openalex.org/W1979674791","https://openalex.org/W1980467157","https://openalex.org/W1989700757","https://openalex.org/W1989882147","https://openalex.org/W1993271874","https://openalex.org/W2021417049","https://openalex.org/W2040403200","https://openalex.org/W2041137640","https://openalex.org/W2049324581","https://openalex.org/W2073727116","https://openalex.org/W2075053673","https://openalex.org/W2076196252","https://openalex.org/W2095705004","https://openalex.org/W2100844912","https://openalex.org/W2114248304","https://openalex.org/W2119599879","https://openalex.org/W2133002996","https://openalex.org/W2141894506","https://openalex.org/W2144530683","https://openalex.org/W2154083639","https://openalex.org/W2156387975","https://openalex.org/W2170128985","https://openalex.org/W2185841109","https://openalex.org/W2213241010","https://openalex.org/W2271840356","https://openalex.org/W2556319343","https://openalex.org/W2570780336","https://openalex.org/W2889954714","https://openalex.org/W2891391371","https://openalex.org/W2904985412","https://openalex.org/W2905002857","https://openalex.org/W2964121744","https://openalex.org/W6631190155","https://openalex.org/W6637031945","https://openalex.org/W6647943800","https://openalex.org/W6662821889","https://openalex.org/W6674330103","https://openalex.org/W6682889407","https://openalex.org/W6686540329","https://openalex.org/W6688387854","https://openalex.org/W6694517276","https://openalex.org/W6730439317","https://openalex.org/W6757297559","https://openalex.org/W6757349411"],"related_works":["https://openalex.org/W2382920002","https://openalex.org/W4379055770","https://openalex.org/W2980587248","https://openalex.org/W2134051725","https://openalex.org/W1963611513","https://openalex.org/W4360605079","https://openalex.org/W1551473783","https://openalex.org/W2912977869","https://openalex.org/W2170509036","https://openalex.org/W3195856620"],"abstract_inverted_index":{"Recent":[0],"advances":[1],"in":[2,85,180,190,239],"unmanned":[3],"aerial":[4,9,65],"vehicles":[5],"suggest":[6,170],"that":[7,155,171,222],"collecting":[8],"agricultural":[10,202],"images":[11,66,153,186],"can":[12,16,225],"be":[13],"cost-efficient,":[14],"which":[15,86],"subsequently":[17],"support":[18],"automated":[19],"precision":[20,210],"irrigation.":[21],"To":[22],"study":[23],"the":[24,52,57,87,96,102,182,191,194,201,214,217,223],"potential":[25],"for":[26,79],"machine":[27],"learning":[28],"to":[29,59,177,207,231,237],"learn":[30],"local":[31],"soil":[32],"moisture":[33,72,81],"conditions":[34,73,82],"directly":[35],"from":[36,83],"such":[37],"images,":[38,84],"we":[39,199],"developed":[40],"a":[41,68,156,208],"very":[42],"fast,":[43],"linear":[44,110],"discrete-time":[45],"simulation":[46],"of":[47,63,67,166,193,204,216],"plant":[48,247],"growth":[49],"based":[50],"on":[51,150],"Richards":[53],"equation.":[54],"We":[55],"use":[56],"simulator":[58,183,248],"generate":[60],"large":[61],"datasets":[62,250],"synthetic":[64],"vineyard":[69],"with":[70,161],"known":[71],"and":[74,95,140,184,220,233,243,249],"then":[75],"compare":[76,200],"seven":[77],"methods":[78,90,99],"inferring":[80],"\u201cuncorrelated":[88],"plant\u201d":[89],"look":[91,100],"at":[92,101,253],"individual":[93],"plants":[94],"\u201ccorrelated":[97],"field\u201d":[98],"entire":[103],"vineyard:":[104],"1)":[105],"constant":[106],"prediction":[107],"baseline,":[108],"2)":[109],"Support":[111],"Vector":[112],"Machines":[113],"(SVM),":[114],"3)":[115],"Random":[116,122],"Forests":[117,123],"Uncorrelated":[118,137],"Plant":[119,138],"(RFUP),":[120],"4)":[121],"Correlated":[124,146],"Field":[125,147],"(RFCF),":[126],"5)":[127],"two-layer":[128],"Neural":[129,135,144],"Networks":[130,136,145],"(NN),":[131],"6)":[132],"Deep":[133,142],"Convolutional":[134,143],"(CNNUP),":[139],"7)":[141],"(CNNCF).":[148],"Experiments":[149],"held-out":[151],"test":[152],"show":[154],"globally-connected":[157],"CNN":[158,219],"performs":[159],"best":[160],"normalized":[162],"mean":[163],"absolute":[164],"error":[165],"3.4%.":[167],"Sensitivity":[168],"experiments":[169],"learned":[172],"global":[173,218],"CNNs":[174],"are":[175,251],"robust":[176,236],"injected":[178],"noise":[179],"both":[181],"generated":[185],"as":[187,189],"well":[188],"size":[192],"training":[195],"sets.":[196],"In":[197],"simulation,":[198],"standard":[203],"flood":[205],"irrigation":[206,211,240],"proportional":[209],"controller":[212],"using":[213],"output":[215],"find":[221],"latter":[224],"reduce":[226],"water":[227],"consumption":[228],"by":[229],"up":[230],"52%":[232],"is":[234],"also":[235],"errors":[238],"level,":[241],"location,":[242],"timing.":[244],"The":[245],"first-order":[246],"available":[252],"https://github.com/BerkeleyAutomation/RAPID.":[254]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":6},{"year":2022,"cited_by_count":12},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":5},{"year":2019,"cited_by_count":3}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}