{"id":"W4405194977","doi":"10.1016/j.compag.2024.109719","title":"Research on unmanned aerial vehicle (UAV) rice field weed sensing image segmentation method based on CNN-transformer","year":2024,"lang":"en","type":"article","venue":"Computers and Electronics in Agriculture","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":34,"is_retracted":false,"has_abstract":false,"ca_institutions":"Ministry of Agriculture","funders":"","keywords":"Artificial intelligence; Aerial image; Computer vision; Drone; Aerial imagery; Aerial survey; Segmentation; Transformer; Image segmentation; Aerial photography; Computer science; Engineering; Remote sensing; Image (mathematics); Geography; Electrical engineering; Voltage","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005375957,0.0002261024,0.0002046106,0.00005521756,0.000294661,0.0003412739,0.0001649984,0.0002151088,0.00003033556],"category_scores_gemma":[0.00001529361,0.00008183632,0.00009771311,0.0009947412,0.00002804173,0.0001397961,0.00002745365,0.0007417164,0.00002368377],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001190557,"about_ca_system_score_gemma":0.00001860622,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002116388,"about_ca_topic_score_gemma":0.0004402243,"domain_scores_codex":[0.9981144,0.0002333091,0.0002056474,0.000538047,0.0003653228,0.0005432554],"domain_scores_gemma":[0.9988967,0.0008483603,0.00002824369,0.00005717712,0.00007386885,0.00009562801],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001883308,0.000141492,0.00002807104,0.00004387513,0.00002711942,0.00004587463,0.0002576616,0.0004840835,0.8373731,0.001557987,0.04409453,0.1157579],"study_design_scores_gemma":[0.003235084,0.01214292,0.03669649,0.001865882,0.0001349194,0.000107027,0.003224587,0.09675838,0.4662474,0.005663193,0.3712579,0.002666227],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9782532,0.0008471245,0.0007838704,0.01493344,0.0006578797,0.0007468185,0.00001678708,0.0001763928,0.003584436],"genre_scores_gemma":[0.995306,0.0001520181,0.001277302,0.001634595,0.001103732,0.00002443632,0.0001625251,0.000003397684,0.0003359737],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3711257,"threshold_uncertainty_score":0.3337188,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0190702665722872,"score_gpt":0.3028685538742205,"score_spread":0.2837982873019332,"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."}}