{"id":"W4388036546","doi":"10.20517/ir.2023.29","title":"Deep learning approaches for object recognition in plant diseases: a review","year":2023,"lang":"en","type":"review","venue":"Intelligence & Robotics","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Computer science; Object detection; Plant disease; Artificial intelligence; Identification (biology); Convolutional neural network; Machine learning; Object (grammar); Agriculture; Intersection (aeronautics); Deep learning; Data science; Pattern recognition (psychology); Biotechnology; Geography; Biology; Cartography; Ecology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006704779,0.001091333,0.001068053,0.001815758,0.0001947147,0.001030327,0.001308465,0.00129186,0.00400097],"category_scores_gemma":[0.001079005,0.0004500504,0.0007233546,0.00260233,0.0004360712,0.001846604,0.0007482579,0.001315623,0.002850223],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005667263,"about_ca_system_score_gemma":0.0009558526,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002355005,"about_ca_topic_score_gemma":0.002420416,"domain_scores_codex":[0.9998135,0.00002381669,0.0000236365,0.00004234485,0.00007963296,0.00001718488],"domain_scores_gemma":[0.9994676,0.0002783162,0.00004995295,0.00001706817,0.0001609131,0.00002615555],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002628629,0.00006129906,0.0001994747,0.00600656,0.00007144039,0.00006281062,0.0000331963,0.001699008,0.0008178763,0.004000839,0.01204558,0.9749756],"study_design_scores_gemma":[0.00001492867,0.0001766844,0.001351073,0.005734082,0.0002360692,0.000858448,0.00008275292,0.004571873,0.001992235,0.009430738,0.9754865,0.00006476201],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0003220033,0.9917394,0.004828783,0.0003939266,0.0002327266,0.00001232243,0.00003976724,0.00004524429,0.002385761],"genre_scores_gemma":[0.00250507,0.9918165,0.003365364,0.0001996602,0.0002243228,0.00001524738,0.00009587743,0.0000109035,0.001767125],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.00400097,"threshold_uncertainty_score":0.01338452,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2113573144358013,"score_gpt":0.3175799621797972,"score_spread":0.1062226477439959,"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."}}