{"id":"W4311163351","doi":"10.18280/ts.390537","title":"Hybrid Deep Model for Automated Detection of Tomato Leaf Diseases","year":2022,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Convolutional neural network; Artificial intelligence; Deep learning; Feature (linguistics); Computer science; Pattern recognition (psychology); Feature extraction; Artificial neural network; Process (computing); Machine learning","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":[0.00009246516,0.00009663205,0.0001273024,0.00001023914,0.0002893178,0.00001710031,0.0001460224,0.00001568293,0.0004514502],"category_scores_gemma":[0.000005653443,0.00003836311,0.0001277406,0.000126174,0.00001677103,0.00006828897,0.00004737734,0.00004052826,0.000002334511],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002659803,"about_ca_system_score_gemma":0.000005334147,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002437858,"about_ca_topic_score_gemma":0.00004945214,"domain_scores_codex":[0.9992028,0.00003419836,0.0001904673,0.0001798608,0.0002198431,0.0001728207],"domain_scores_gemma":[0.9997306,0.00006296289,0.00008335662,0.00002517571,0.00004316775,0.00005477095],"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":[0.0001977993,0.0004869958,0.0008864828,0.00002053951,0.00004536664,0.000002466204,0.0001442176,0.01674436,0.9428549,0.0002175574,0.006407248,0.03199201],"study_design_scores_gemma":[0.0006160077,0.001029145,0.03634897,0.000007413641,0.00008262315,0.00001102838,0.0004040158,0.9173784,0.03629651,0.0009289889,0.006592387,0.0003045742],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9979879,0.00008606262,0.0006889691,0.0002679305,0.00007333652,0.000349932,0.000290461,0.0001996082,0.00005580459],"genre_scores_gemma":[0.9991341,0.000001822951,0.00006074179,0.0001788656,0.0001329266,0.0001916874,0.0002097631,8.97599e-7,0.00008923432],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9065585,"threshold_uncertainty_score":0.4943063,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01438620035748171,"score_gpt":0.2048056572628177,"score_spread":0.190419456905336,"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."}}