{"id":"W7093372727","doi":"10.2316/j.2025.201-0511","title":"DEEP LEARNING-BASED SEMANTIC SEGMENTATION AND RECOGNITION FOR AGRICULTURAL ROBOTS, 1-9.","year":2025,"lang":"en","type":"article","venue":"Mechatronic systems and control","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Segmentation; Pattern recognition (psychology); Feature (linguistics); Image segmentation; Field (mathematics); Agriculture","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001909152,0.0001331535,0.0002052033,0.00001283267,0.0002767921,0.0001349586,0.00004777446,0.00009283049,0.00001035814],"category_scores_gemma":[0.00002320123,0.00004748805,0.00006048071,0.0001120915,0.00001621424,0.00008470578,0.00001027743,0.00006847581,0.000002832533],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001827401,"about_ca_system_score_gemma":0.000004938267,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000194825,"about_ca_topic_score_gemma":0.0003406711,"domain_scores_codex":[0.9992051,0.00007608658,0.0001824296,0.000249048,0.00008138025,0.0002060165],"domain_scores_gemma":[0.9995722,0.00019913,0.00008237406,0.00002149892,0.00007716005,0.00004767116],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0003803547,0.0002131218,0.01725494,0.000546463,0.0003832704,0.000003349327,0.0002718366,0.001818515,0.4401695,0.005407413,0.00160822,0.531943],"study_design_scores_gemma":[0.02431519,0.00540224,0.61436,0.001716983,0.002110232,0.0001107575,0.03018321,0.230914,0.014024,0.006062406,0.06753601,0.003264883],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9777395,0.007075834,0.0108331,0.002089679,0.0004264414,0.00154349,0.00002462049,0.0001175672,0.0001497512],"genre_scores_gemma":[0.9987442,0.0000520096,0.00002692951,0.0001493285,0.0002165673,0.0002312216,0.0001257873,7.201542e-7,0.0004532008],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5971051,"threshold_uncertainty_score":0.2128889,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007597387467541423,"score_gpt":0.1958607349438899,"score_spread":0.1882633474763485,"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."}}