{"id":"W2994236510","doi":"10.20431/2349-4050.0602002","title":"A Literature Review on Image Processing and Classification Techniques for Agriculture Produce and Modeling of Quality Assessment system for Soybean industry Sample","year":2019,"lang":"en","type":"review","venue":"International Journal of Innovative Research in Electronics and Communications","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Quality (philosophy); Sample (material); Image processing; Agriculture; Computer science; Agricultural engineering; Artificial intelligence; Process engineering; Image (mathematics); Engineering; Geography; Chromatography; Chemistry","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002829658,0.0001920782,0.0008535013,0.0006116303,0.0001239749,0.0001378701,0.000717494,0.0002771693,0.000001051155],"category_scores_gemma":[0.0008855325,0.0001418424,0.0001018395,0.001010156,0.0001246241,0.0001779236,0.0001764455,0.00190135,2.571471e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005576944,"about_ca_system_score_gemma":0.0007677914,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006749889,"about_ca_topic_score_gemma":0.000003973859,"domain_scores_codex":[0.997896,0.000187528,0.001057224,0.0002542197,0.0004096773,0.0001953607],"domain_scores_gemma":[0.9928854,0.001203488,0.001145141,0.0003509017,0.004376336,0.00003879246],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"systematic_review","study_design_scores_codex":[0.0000952624,0.000488735,0.00008137788,0.1104813,0.0008782171,9.715081e-7,0.0001736501,0.000001094493,0.001977654,0.06846835,0.0002206071,0.8171328],"study_design_scores_gemma":[0.002229053,0.001418611,0.00004684481,0.5667891,0.001062875,0.0003696203,0.003354328,0.005965739,0.002650498,0.01208244,0.4027765,0.001254379],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00007970948,0.9949399,0.00240191,0.001271955,0.000009626,0.000662394,0.0002513745,0.000005712098,0.0003773946],"genre_scores_gemma":[0.008823899,0.982459,0.008023368,0.00001958019,0.00007620855,0.0002818906,0.0002723583,0.00001837117,0.00002530628],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.8158784,"threshold_uncertainty_score":0.8260524,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3058526918161472,"score_gpt":0.5512048456984996,"score_spread":0.2453521538823524,"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."}}