{"id":"W4402037454","doi":"10.1016/j.atech.2024.100550","title":"An efficient and lightweight banana detection and localization system based on deep CNNs for agricultural robots","year":2024,"lang":"en","type":"article","venue":"Smart Agricultural Technology","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministry of Agriculture","funders":"","keywords":"Robot; Artificial intelligence; Agriculture; Computer science; Agricultural engineering; Engineering; Geography; Archaeology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001706366,0.0004067173,0.0003477244,0.00009133935,0.0006224107,0.0002805504,0.000215128,0.0004906128,0.00001294541],"category_scores_gemma":[0.00003149214,0.0001299755,0.0001188468,0.001133729,0.0001194315,0.0002015362,0.0000588199,0.000244161,0.00001858288],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001065647,"about_ca_system_score_gemma":0.000005829536,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005054968,"about_ca_topic_score_gemma":0.0008245727,"domain_scores_codex":[0.9980052,0.00006615545,0.0003380239,0.0008604411,0.0002316717,0.0004985309],"domain_scores_gemma":[0.9993091,0.0001612578,0.00008942395,0.0000980037,0.0001687122,0.0001735581],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0001352599,0.0003545411,0.002801859,0.0003857003,0.0001330806,0.00003590072,0.0003024816,0.002327246,0.8117457,0.02558546,0.001745463,0.1544473],"study_design_scores_gemma":[0.001947834,0.00855641,0.5216559,0.001277152,0.0006682976,0.000994696,0.01269425,0.219677,0.1691898,0.001027277,0.0586045,0.003707013],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9920851,0.0009974496,0.0006663128,0.003085286,0.0006043462,0.0009760358,0.00004417088,0.001243796,0.0002975047],"genre_scores_gemma":[0.9986376,0.00002783758,0.0001917847,0.00009906367,0.0005179362,0.0002053513,0.0002305618,0.000003962799,0.00008588607],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.642556,"threshold_uncertainty_score":0.5300248,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0053242677398637,"score_gpt":0.1908964486220732,"score_spread":0.1855721808822095,"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."}}