{"id":"W4389041634","doi":"10.1109/isncc58260.2023.10323932","title":"Detection of Plant Diseases in an Industrial Greenhouse: Development, Validation &amp; Exploitation","year":2023,"lang":"en","type":"article","venue":"","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Collège de Maisonneuve; Laboratoire Recherche Informatique Maisonneuve","funders":"","keywords":"Greenhouse; Computer science; Object detection; Robustness (evolution); Context (archaeology); Hyperparameter; Artificial intelligence; Machine learning; Pattern recognition (psychology)","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":[0.002053711,0.001344212,0.0006921735,0.0006795087,0.0003506478,0.0007701343,0.001737611,0.001437447,0.001042987],"category_scores_gemma":[0.003182332,0.0004243313,0.0007730324,0.0004081919,0.0004603998,0.001133617,0.0009622671,0.0008726357,0.0006829499],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00104345,"about_ca_system_score_gemma":0.001298198,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01272773,"about_ca_topic_score_gemma":0.01598339,"domain_scores_codex":[0.9992023,0.0001932133,0.00004937888,0.0002484242,0.0002107504,0.00009595276],"domain_scores_gemma":[0.9985233,0.0005839186,0.0001114365,0.0002939582,0.0003971083,0.00009032759],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0008116207,0.001920531,0.05405626,0.001189351,0.0003382811,0.0007223183,0.0003518348,0.421963,0.1189699,0.00140464,0.01220859,0.3860637],"study_design_scores_gemma":[0.00005638655,0.0006605375,0.01163635,0.00006747654,0.00005449969,0.000190686,0.00013722,0.9328412,0.0502158,0.0006218386,0.00347414,0.00004384714],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7956299,0.001892268,0.1891837,0.0008569073,0.0002926599,0.0005870184,0.001861191,0.006509244,0.003187158],"genre_scores_gemma":[0.8404838,0.0006385671,0.1507815,0.00028546,0.00004765532,0.0002518431,0.005168602,0.0001258179,0.002216829],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01272773,"threshold_uncertainty_score":0.0253073,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08302874964671374,"score_gpt":0.2482984300604388,"score_spread":0.1652696804137251,"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."}}