{"id":"W2080031760","doi":"10.1109/crv.2014.53","title":"An Integrated Bud Detection and Localization System for Application in Greenhouse Automation","year":2014,"lang":"en","type":"article","venue":"","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Greenhouse; Hough transform; Automation; Pruning; Floriculture; Computer science; Robot; Artificial intelligence; Computer vision; Engineering; Horticulture; Image (mathematics); Biology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002832221,0.0004401988,0.0005526567,0.0004421666,0.0003092215,0.000358054,0.0008905827,0.0007039386,0.004547847],"category_scores_gemma":[0.0002499719,0.0002833523,0.000340964,0.0002755525,0.000152504,0.000422979,0.0004746246,0.0003667506,0.001421808],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002526905,"about_ca_system_score_gemma":0.0004059031,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001281451,"about_ca_topic_score_gemma":0.002024192,"domain_scores_codex":[0.9997386,0.00001934451,0.000009229272,0.0000883604,0.0001182387,0.00002622717],"domain_scores_gemma":[0.9997377,0.00004375342,0.00002081081,0.00005337916,0.0001098895,0.00003446335],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000334378,0.0001350576,0.002699484,0.0002126449,0.00004282561,0.000248656,0.00008568975,0.00201324,0.8394563,0.00030388,0.002319919,0.1521479],"study_design_scores_gemma":[0.0002794726,0.002612381,0.05608042,0.00007968236,0.0003377612,0.002541267,0.0001261668,0.1262932,0.7593092,0.0005178821,0.05164516,0.0001773761],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1359174,0.0007094053,0.8393391,0.0001709836,0.000196923,0.0002892977,0.0005713771,0.01889589,0.003909553],"genre_scores_gemma":[0.5674682,0.0003165072,0.4193503,0.0002393514,0.00006238561,0.0003217932,0.001120011,0.0003139078,0.01080758],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004547847,"threshold_uncertainty_score":0.01521403,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00664216465917929,"score_gpt":0.1939820639756432,"score_spread":0.1873398993164639,"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."}}