{"id":"W4387445412","doi":"10.1109/icivc58118.2023.10270642","title":"Leaf Recognition Using K-Nearest Neighbors Algorithm with Zernike Moments","year":2023,"lang":"en","type":"article","venue":"","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Winnipeg","funders":"","keywords":"Zernike polynomials; Artificial intelligence; Support vector machine; Pattern recognition (psychology); Computer science; Convolutional neural network; Classifier (UML); Feature extraction; Contextual image classification; Algorithm; Machine learning; Image (mathematics)","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":[0.0003729193,0.0005663494,0.0008189845,0.002154283,0.0004433875,0.0007789514,0.0006827007,0.0007092475,0.001671256],"category_scores_gemma":[0.001057928,0.000198149,0.0006405166,0.001398569,0.0002879666,0.001287787,0.0004577604,0.0005660078,0.001182078],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004264039,"about_ca_system_score_gemma":0.0006141511,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003377269,"about_ca_topic_score_gemma":0.006380447,"domain_scores_codex":[0.9994698,0.00004382663,0.00003305514,0.0001396306,0.0002554719,0.00005834621],"domain_scores_gemma":[0.999548,0.00009614574,0.00008046335,0.00006460993,0.0001867969,0.00002392969],"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":[0.0004268395,0.0002756716,0.005024862,0.0002862744,0.00009475488,0.0002121984,0.0001527953,0.02210066,0.1133738,0.002337287,0.005664954,0.85005],"study_design_scores_gemma":[0.00005916876,0.0003206427,0.01505421,0.00004664187,0.00007598832,0.0009835415,0.0003796658,0.8437783,0.1247549,0.004213469,0.01021849,0.0001149336],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.167784,0.00180189,0.8164319,0.0003454892,0.0002356678,0.0002541917,0.0007144379,0.004438634,0.007993878],"genre_scores_gemma":[0.5186829,0.0009153078,0.4739446,0.0001273242,0.00008311744,0.0001127656,0.001240029,0.0001664985,0.004727439],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003377269,"threshold_uncertainty_score":0.006715178,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04389433873582126,"score_gpt":0.2217775836872615,"score_spread":0.1778832449514402,"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."}}