{"id":"W4353100249","doi":"10.54097/hset.v34i.5430","title":"Fruit Image Classification Using Convolution Neural Networks","year":2023,"lang":"en","type":"article","venue":"Highlights in Science Engineering and Technology","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Artificial intelligence; Convolutional neural network; Deep learning; Computer science; Field (mathematics); Pattern recognition (psychology); Artificial neural network; Contextual image classification; Machine learning; Convolution (computer science); Texture (cosmology); Image (mathematics); 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":[0.0003153251,0.0005329306,0.0003883394,0.0009218024,0.0001934331,0.0005093055,0.0005298515,0.000522677,0.001439815],"category_scores_gemma":[0.0005879825,0.0002046216,0.0006063224,0.0006982986,0.000184039,0.0006416854,0.0002616272,0.0003844803,0.0004660191],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008687191,"about_ca_system_score_gemma":0.0003622382,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01206275,"about_ca_topic_score_gemma":0.009503615,"domain_scores_codex":[0.9998503,0.00001344067,0.00000805821,0.0000477067,0.00005215542,0.00002826451],"domain_scores_gemma":[0.999826,0.00004393094,0.00002225484,0.00001600051,0.00008343777,0.000008323682],"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":[0.0003884489,0.0002339515,0.01087545,0.0001457897,0.0001728865,0.0002495558,0.00005565932,0.2527253,0.05113916,0.002152544,0.004937611,0.6769236],"study_design_scores_gemma":[0.000002717655,0.00002278272,0.002780153,0.000005800711,0.00001272652,0.00003414131,0.000006507253,0.9905213,0.005681654,0.0004238445,0.0005013341,0.000006941682],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4359614,0.002554168,0.5434663,0.0006053231,0.0002811231,0.0001440058,0.0008150707,0.003866951,0.01230559],"genre_scores_gemma":[0.9279331,0.0005776783,0.06557381,0.0001147859,0.00004622641,0.00004206025,0.0006707804,0.00004012721,0.005001409],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01206275,"threshold_uncertainty_score":0.02398509,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01620577342961727,"score_gpt":0.2169548917245807,"score_spread":0.2007491182949635,"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."}}