{"id":"W4388050946","doi":"10.18280/ts.400523","title":"A Capsule Attention Network for Plant Disease Classification","year":2023,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Artificial intelligence; Computer science; Machine learning; Feed forward; Categorization; Plant disease; Identification (biology); Transfer of learning; Deep learning; Engineering; Biotechnology; Control 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":[0.0001825772,0.00008912425,0.00007971597,0.000006564742,0.0002285238,0.00004627849,0.00009927698,0.0000344077,0.0001919781],"category_scores_gemma":[0.000006903388,0.00003164904,0.00009779847,0.0002597727,0.00001334841,0.00006967835,0.00001559798,0.0000319009,0.00009891248],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001339644,"about_ca_system_score_gemma":0.000003924089,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001226876,"about_ca_topic_score_gemma":0.00005541112,"domain_scores_codex":[0.999229,0.00002488467,0.0001474172,0.0002018939,0.0001522281,0.000244565],"domain_scores_gemma":[0.9997341,0.00006121947,0.00005069822,0.00002442155,0.0000337397,0.00009579383],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.0001441766,0.0001954067,0.02313195,0.00002542955,0.00004213209,0.000007350573,0.00006896093,0.0001842268,0.4255491,0.004024968,0.5117458,0.03488043],"study_design_scores_gemma":[0.0001724305,0.0001069011,0.8881342,0.00001710125,0.00002443068,5.122732e-7,0.00009923873,0.002419315,0.0001589397,0.0006868097,0.1080583,0.0001218619],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9947048,0.00003689726,0.00006621348,0.004026973,0.000228132,0.0004493757,0.0001758301,0.0001686086,0.0001432152],"genre_scores_gemma":[0.9943792,0.00001655253,0.00003447704,0.0002747963,0.001837238,0.0001871277,0.00271309,7.925171e-7,0.000556764],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8650022,"threshold_uncertainty_score":0.2102026,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04123520812090255,"score_gpt":0.2229790666586877,"score_spread":0.1817438585377852,"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."}}