{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002171966,0.00009213965,0.0001011913,0.0001390871,0.0001853443,0.00005116103,0.0002080196,0.0001154078,0.000003497471],"category_scores_gemma":[0.00004880582,0.00003789106,0.00001392714,0.003313857,0.000195707,0.0001934428,0.00008444425,0.0001225702,0.00002047531],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003168151,"about_ca_system_score_gemma":0.000004367402,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002582666,"about_ca_topic_score_gemma":0.00002827302,"domain_scores_codex":[0.999128,0.000006327127,0.0001267919,0.0002872887,0.0001152777,0.0003363159],"domain_scores_gemma":[0.9997892,0.00004240668,0.00003008737,0.0000513304,0.00004217589,0.00004479588],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000001123394,0.000009154614,0.003409068,0.00000183317,0.000001066848,0.000007610261,0.00001984915,0.001302759,0.9772164,0.01361643,0.00008848542,0.004326277],"study_design_scores_gemma":[0.0001014389,0.00006140401,0.2291766,0.0000251613,0.000004099266,0.00002909828,0.0002127988,0.7552584,0.008830914,0.0003290263,0.005737937,0.0002331938],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9966799,0.00008064679,0.00005879327,0.002431585,0.0002609793,0.00008034028,0.000001633169,0.0003627578,0.00004334907],"genre_scores_gemma":[0.9995782,0.00006398184,0.000198445,0.00001208604,0.0001109656,0.00000986135,0.000005969248,6.917652e-7,0.00001984423],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9683855,"threshold_uncertainty_score":0.1592198,"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."}}