{"id":"W4395078528","doi":"10.18280/ria.380209","title":"Deep Learning and Machine Learning Based Method for Crop Disease Detection and Identification Using Autoencoder and Neural Network","year":2024,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Autoencoder; Artificial intelligence; Artificial neural network; Deep learning; Identification (biology); Machine learning; Computer science; Pattern recognition (psychology); Biology; Botany","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":[0.0004048464,0.0001262782,0.0001200187,0.00001779062,0.0005385479,0.0002807829,0.00004513279,0.00005804282,0.00003487997],"category_scores_gemma":[0.0001028272,0.0000598006,0.00004866822,0.0002652838,0.00004615842,0.0001460416,0.00003795009,0.0001633204,0.000002663734],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001194171,"about_ca_system_score_gemma":0.000002793904,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006573794,"about_ca_topic_score_gemma":0.0001242422,"domain_scores_codex":[0.9990392,0.00009783267,0.0002006995,0.0003895485,0.00006869562,0.0002040751],"domain_scores_gemma":[0.9993703,0.0004057946,0.00005451818,0.0000283656,0.00003938211,0.0001016639],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005191421,0.00002130122,0.005577893,0.0001288223,0.00001428069,0.000005637542,0.0002922069,0.1793487,0.1821917,0.0001774601,0.00001495074,0.6321751],"study_design_scores_gemma":[0.00001594887,0.0000903352,0.003501444,0.00005780262,0.00004338786,0.00001477231,0.0002403854,0.9848754,0.003627602,0.0004123126,0.006979588,0.0001409531],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6189132,0.008286829,0.3710366,0.000970237,0.0002508685,0.0003712548,0.000005600053,0.0001469647,0.00001844548],"genre_scores_gemma":[0.9976001,0.0001770739,0.00147665,0.00004905276,0.00028868,0.00002056229,0.00002608318,0.000002626493,0.0003591971],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8055267,"threshold_uncertainty_score":0.414213,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03844093496666715,"score_gpt":0.2797496073673668,"score_spread":0.2413086724006996,"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."}}