{"id":"W4378418316","doi":"10.18280/ria.370218","title":"Tomato Crop Disease Classification Using Semantic Segmentation Algorithm in Deep Learning","year":2023,"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":"Artificial intelligence; Segmentation; Computer science; Crop; Deep learning; Pattern recognition (psychology); Machine learning; Algorithm; Agronomy; Biology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002965375,0.0006351761,0.0005516183,0.0008887118,0.0002533698,0.0005777579,0.0007481142,0.0008663854,0.002184572],"category_scores_gemma":[0.000356157,0.0002145952,0.0006225131,0.0006398717,0.0003015343,0.0006052852,0.0003767153,0.0006006383,0.0006163815],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009691992,"about_ca_system_score_gemma":0.0009252028,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01062148,"about_ca_topic_score_gemma":0.01130111,"domain_scores_codex":[0.9998914,0.000009505077,0.000007283974,0.00003968949,0.00002445422,0.00002766036],"domain_scores_gemma":[0.9998962,0.00002436028,0.00001359382,0.00001007278,0.00004420105,0.00001155386],"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":[0.0004716606,0.0003859751,0.005030979,0.0001533674,0.00009584016,0.0001848726,0.0001053206,0.2286583,0.0491445,0.004080236,0.007718646,0.7039704],"study_design_scores_gemma":[0.00001107367,0.00005519834,0.0008323018,0.000009201566,0.00001001141,0.00002876297,0.00001560857,0.9900292,0.006678408,0.001486289,0.0008369042,0.000007101291],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2416951,0.001518338,0.7406972,0.0005935165,0.000188921,0.0002288902,0.0007548757,0.005484011,0.008839096],"genre_scores_gemma":[0.7472994,0.0006070042,0.239696,0.0003941889,0.00005726055,0.0001665151,0.002211283,0.0001135688,0.009454769],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01062148,"threshold_uncertainty_score":0.02111936,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0567408604319453,"score_gpt":0.2784909940757398,"score_spread":0.2217501336437945,"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."}}