{"id":"W7089463913","doi":"10.1007/978-3-032-07336-5_18","title":"Banana Ripeness Detection using Fine-Tuned MobileNetV2 Deep Learning Model","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes on data engineering and communications technologies","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Ripeness; Categorical variable; Deep learning; Pattern recognition (psychology); Class (philosophy); Inference; FLOPS; Transfer of learning","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":[0.0001701931,0.001008653,0.0006088989,0.0008501909,0.0002645974,0.0005021744,0.0009935105,0.0007547321,0.003629682],"category_scores_gemma":[0.0002811476,0.0003006998,0.0006363222,0.0004740737,0.0001694014,0.0007026353,0.0005794092,0.0008850541,0.00275297],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004385145,"about_ca_system_score_gemma":0.0005041985,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01017896,"about_ca_topic_score_gemma":0.02428272,"domain_scores_codex":[0.9998665,0.00000829321,0.00000342637,0.00005578995,0.00002792459,0.00003811873],"domain_scores_gemma":[0.9998875,0.00001868597,0.000008426951,0.00001550727,0.00005893386,0.00001092487],"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":[0.0005237772,0.0004248105,0.008645575,0.0002296582,0.0001598163,0.0002263846,0.0000358564,0.09072611,0.07069782,0.001889033,0.0342261,0.792215],"study_design_scores_gemma":[0.0000105292,0.00005344148,0.002932317,0.00002005771,0.00003682618,0.00009159503,0.00002040583,0.9774924,0.01487035,0.0013,0.003153081,0.00001896694],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2688754,0.003245915,0.6720646,0.0008628129,0.0008870181,0.0002441616,0.007490775,0.01935834,0.02697102],"genre_scores_gemma":[0.7911937,0.0009981913,0.1587065,0.0006259232,0.0001436023,0.000145878,0.0123535,0.000475247,0.0353574],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01017896,"threshold_uncertainty_score":0.02023941,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03439732061534937,"score_gpt":0.2278356627975742,"score_spread":0.1934383421822249,"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."}}