{"id":"W3142834936","doi":"10.18280/ts.380108","title":"Comparison of Plant Leaf Classification Using Modified AlexNet and Support Vector Machine","year":2021,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":59,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Support vector machine; Artificial intelligence; Convolutional neural network; Computer science; Pattern recognition (psychology); Kernel (algebra); Classifier (UML); Plant identification; Radial basis function kernel; Transfer of learning; Identification (biology); Machine learning; Artificial neural network; Plant disease; Kernel method; Mathematics; Botany; 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":[0.0005882353,0.0006901096,0.000392568,0.0007381341,0.0001471541,0.0004639257,0.0005434529,0.0006313749,0.001211389],"category_scores_gemma":[0.0007305664,0.0001300864,0.0003408681,0.0003530084,0.000146778,0.0008156477,0.0002622211,0.0003040704,0.0004037091],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000453403,"about_ca_system_score_gemma":0.0003358481,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005442651,"about_ca_topic_score_gemma":0.006287506,"domain_scores_codex":[0.9997745,0.00002694687,0.00001739522,0.0000509793,0.00007462798,0.00005556505],"domain_scores_gemma":[0.9997677,0.00005833611,0.00002285268,0.0000204357,0.0001082244,0.00002241076],"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":[0.002409607,0.0007419842,0.02664934,0.0004832149,0.0004120708,0.0004432855,0.0001391915,0.1512537,0.09627759,0.001455929,0.008257044,0.711477],"study_design_scores_gemma":[0.00002038326,0.0003658933,0.01613854,0.00001879095,0.00004121823,0.0001129873,0.00006271294,0.9562693,0.02533874,0.0003078728,0.001304001,0.00001948921],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.931183,0.001472809,0.05775069,0.0002731654,0.0002638201,0.00006845653,0.0006540496,0.002198792,0.006135193],"genre_scores_gemma":[0.9720506,0.000370135,0.02208652,0.00006862161,0.00002375601,0.00003356236,0.001636679,0.0000444623,0.003685551],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.005442651,"threshold_uncertainty_score":0.01082194,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.078864074939499,"score_gpt":0.2743236516865951,"score_spread":0.1954595767470961,"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."}}