{"id":"W2947354493","doi":"10.1139/juvs-2018-0036","title":"Multi-crop recognition using UAV-based high-resolution NDVI time-series","year":2019,"lang":"en","type":"article","venue":"Journal of Unmanned Vehicle Systems","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Multispectral image; Normalized Difference Vegetation Index; Principal component analysis; Multispectral pattern recognition; Computer science; Pattern recognition (psychology); Remote sensing; Sampling (signal processing); Decision tree; Artificial intelligence; Pixel; Data mining; Geography; Computer vision; Leaf area index","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001702924,0.0003631406,0.0002921617,0.0008644867,0.0001580382,0.0003554549,0.0002151182,0.0002355219,0.0007261179],"category_scores_gemma":[0.0003209441,0.0001218051,0.0003532809,0.0006209298,0.00007607621,0.0003738165,0.000152083,0.0002136623,0.0004954715],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001728614,"about_ca_system_score_gemma":0.0001321838,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002352129,"about_ca_topic_score_gemma":0.003293773,"domain_scores_codex":[0.9999005,0.00001215073,0.00000570166,0.00003446308,0.00003103511,0.00001621259],"domain_scores_gemma":[0.9998801,0.00002307833,0.00002252528,0.00001800497,0.00004552727,0.00001074797],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0003845192,0.0003077139,0.02561378,0.0002051598,0.0001542434,0.0003937643,0.0001306473,0.07108291,0.316744,0.0005290813,0.002749213,0.5817049],"study_design_scores_gemma":[0.00001484572,0.0001619391,0.06952572,0.00001449746,0.00004794041,0.00024279,0.0001222216,0.8704598,0.05731586,0.0003929117,0.00167076,0.00003071576],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6978732,0.0003585143,0.2949119,0.0001222456,0.0001085403,0.0001201868,0.001247736,0.00220365,0.00305402],"genre_scores_gemma":[0.8837409,0.0001405976,0.1143257,0.00002149645,0.00001464182,0.00003336823,0.0008793786,0.00003192149,0.000812115],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002352129,"threshold_uncertainty_score":0.004676878,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01479190675847309,"score_gpt":0.2141800365661895,"score_spread":0.1993881298077164,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}