{"id":"W7116681090","doi":"10.18280/mmep.121121","title":"Automatic Field Monitoring in Smart Agriculture Using Segmentation and Classification Based on YOLO-V4 and Xception Networks","year":2025,"lang":"","type":"article","venue":"Mathematical Modelling and Engineering Problems","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Field (mathematics); Segmentation; Image segmentation; Agriculture; Precision agriculture; Pattern recognition (psychology)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002120339,0.0004376113,0.0004042404,0.0008907324,0.0002805063,0.0004556839,0.0004969121,0.0004404486,0.001184059],"category_scores_gemma":[0.0002862784,0.0002053193,0.0002522981,0.0007199411,0.0002241546,0.000496848,0.0003391422,0.0002125363,0.0002993405],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004394515,"about_ca_system_score_gemma":0.0003750295,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01064589,"about_ca_topic_score_gemma":0.01428197,"domain_scores_codex":[0.9998864,0.00001492429,0.000004324333,0.00003465428,0.00002698289,0.00003279768],"domain_scores_gemma":[0.9998952,0.00002985937,0.00001685493,0.000005356543,0.00004211568,0.00001064435],"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":[0.001246091,0.0002978256,0.0209691,0.0002434834,0.0001132633,0.0002463914,0.0002230427,0.1630478,0.1814474,0.002971416,0.003312553,0.6258817],"study_design_scores_gemma":[0.0000164437,0.00007730978,0.01019462,0.00001312239,0.0000332516,0.00003497066,0.00004780784,0.9697301,0.01790148,0.0007553734,0.001178589,0.00001682703],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5265923,0.0008927057,0.4628818,0.0002409203,0.0001400226,0.00008269112,0.0006097427,0.001681022,0.006878804],"genre_scores_gemma":[0.8818391,0.0003228301,0.1129432,0.00006936263,0.00003908571,0.00006348365,0.000632782,0.0000638979,0.004026245],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01064589,"threshold_uncertainty_score":0.02116787,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02845071406436828,"score_gpt":0.2258522027541729,"score_spread":0.1974014886898046,"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."}}