{"id":"W4312177527","doi":"10.18280/ria.360512","title":"Automatic Detection and Classification of Apple Leaves Diseases Using MobileNet V2","year":2022,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Context (archaeology); Software deployment; Computer science; Convolutional neural network; Artificial intelligence; Agricultural engineering; Machine learning; Biology; Engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001233167,0.00008163945,0.0001214132,0.00001567681,0.0003179947,0.00002299423,0.0001217195,0.00002703735,0.0005074542],"category_scores_gemma":[0.00002342676,0.00003791206,0.00005460222,0.0003790177,0.00005106242,0.00007338393,0.0000766511,0.00007299321,0.000009712089],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002377362,"about_ca_system_score_gemma":0.000004406848,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008229134,"about_ca_topic_score_gemma":0.00005112891,"domain_scores_codex":[0.9992415,0.00006429313,0.0002271342,0.0002099665,0.0001258672,0.0001312031],"domain_scores_gemma":[0.9996352,0.0001109675,0.0001171333,0.00005766739,0.00003389678,0.00004514591],"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":[0.000008936247,0.0001207859,0.004093165,0.00002041156,0.000006349136,8.505621e-7,0.0002629563,0.001842546,0.7563537,0.0001943203,0.00009854787,0.2369975],"study_design_scores_gemma":[0.00005825725,0.00081754,0.1526966,0.00005998658,0.0000900328,0.00007560169,0.01987276,0.5774034,0.2252747,0.001887343,0.02123086,0.000532889],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9986787,0.0004415661,0.000255628,0.0001529143,0.000104406,0.0001925577,0.00003604025,0.00004106718,0.0000971542],"genre_scores_gemma":[0.9996346,0.00002856796,0.00004625796,0.00002622242,0.0000836782,0.00003719479,0.00002535846,8.792972e-7,0.0001172662],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5755609,"threshold_uncertainty_score":0.5556267,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04169103577234452,"score_gpt":0.2437864794909658,"score_spread":0.2020954437186213,"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."}}