{"id":"W2892957234","doi":"10.5194/isprs-archives-xlii-1-181-2018","title":"AN EFFICIENT WEED DETECTION PROCEDURE USING LOW-COST UAV IMAGERY SYSTEM FOR PRECISION AGRICULTURE APPLICATIONS","year":2018,"lang":"en","type":"article","venue":"The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Weed; Precision agriculture; Computer science; Satellite imagery; Remote sensing; Field (mathematics); Aerial imagery; Artificial intelligence; Agriculture; Computer vision; Environmental science; Real-time computing; Geography; Mathematics; Ecology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001371237,0.0004592227,0.0002984904,0.0007238297,0.000221062,0.0003313838,0.0004333327,0.0003486869,0.001521981],"category_scores_gemma":[0.0002477941,0.000191736,0.000279558,0.0003924696,0.0001393007,0.0003665198,0.0002084939,0.0002833542,0.0006560323],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002041385,"about_ca_system_score_gemma":0.0002265758,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009723702,"about_ca_topic_score_gemma":0.001580335,"domain_scores_codex":[0.999781,0.00001837047,0.000009743263,0.00005203883,0.0001195955,0.00001937101],"domain_scores_gemma":[0.9998119,0.00002572685,0.00003886373,0.00002649101,0.0000867682,0.00001027243],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001606604,0.0000670365,0.002473865,0.0003494168,0.00002710484,0.0002714985,0.00009881829,0.002434287,0.7922053,0.0004858615,0.002062759,0.1993635],"study_design_scores_gemma":[0.00006886684,0.001084182,0.02636478,0.00006934227,0.0001076649,0.001384603,0.0002115545,0.1157272,0.8297175,0.0004565391,0.02471638,0.00009138639],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2724302,0.001656117,0.7136155,0.0002507125,0.0002730714,0.0003631993,0.0004601784,0.004568683,0.006382256],"genre_scores_gemma":[0.5721303,0.0006543067,0.4207757,0.0001141896,0.00003781319,0.0001332461,0.0005000353,0.00007354225,0.005580904],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001521981,"threshold_uncertainty_score":0.005091488,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01494832971864767,"score_gpt":0.2484454179937254,"score_spread":0.2334970882750777,"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."}}