{"id":"W4311974999","doi":"10.3390/electronics11244100","title":"Prediction of Fruit Maturity, Quality, and Its Life Using Deep Learning Algorithms","year":2022,"lang":"en","type":"article","venue":"Electronics","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":148,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakehead University","funders":"King Saud University","keywords":"Convolutional neural network; Maturity (psychological); Deep learning; Artificial intelligence; Computer science; Machine learning; Crop; Capability Maturity Model; Agricultural engineering; Artificial neural network; Agronomy; Engineering; Biology","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":[0.0002398668,0.0007813995,0.0004159368,0.0007092587,0.0001451193,0.0005102849,0.0005604143,0.0006032116,0.0008757927],"category_scores_gemma":[0.000718712,0.0002142676,0.000589299,0.0005123781,0.0001434927,0.0006870447,0.0003361415,0.0006776784,0.0003245151],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007598918,"about_ca_system_score_gemma":0.0003304544,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008691773,"about_ca_topic_score_gemma":0.01283541,"domain_scores_codex":[0.9998842,0.000009153405,0.000007586051,0.0000450751,0.00002696899,0.00002691133],"domain_scores_gemma":[0.9997688,0.00006878922,0.00003796406,0.00001595688,0.00008382483,0.00002465616],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006656496,0.000864063,0.06313382,0.0003074693,0.0002275382,0.0004489254,0.00007116287,0.5835106,0.02776816,0.001129723,0.01265939,0.3092135],"study_design_scores_gemma":[0.000005285469,0.00003577693,0.006536548,0.00000794381,0.000008785821,0.00002004318,0.00001021209,0.9908391,0.001824242,0.0003598394,0.0003451493,0.000007018676],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8425437,0.003066731,0.1420884,0.0005056811,0.00009947472,0.00009659163,0.005651366,0.001941864,0.004006324],"genre_scores_gemma":[0.9633942,0.0005295429,0.02574665,0.0001138952,0.0000293618,0.0000555586,0.007478075,0.00003856173,0.002614185],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008691773,"threshold_uncertainty_score":0.01728237,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04267632985990146,"score_gpt":0.2526148989790267,"score_spread":0.2099385691191252,"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."}}