{"id":"W4411635855","doi":"10.1016/j.isprsjprs.2025.06.012","title":"STANet-TLA: leveraging deep learning and prior knowledge for large-scale soybean breeding plot segmentation and high-yielding variety screening from UAV time-series data","year":2025,"lang":"en","type":"article","venue":"ISPRS Journal of Photogrammetry and Remote Sensing","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"Ministry of Agriculture","funders":"State Key Laboratory for Agrobiotechnology; Young Elite Scientists Sponsorship Program by Tianjin; Jiangsu Provincial Key Research and Development Program; Innovation and Technology Fund; Fundamental Research Funds for the Central Universities; China Association for Science and Technology; Natural Science Foundation of Jiangsu Province; Guangzhou University; National Natural Science Foundation of China","keywords":"Segmentation; Scale (ratio); Series (stratigraphy); Plot (graphics); Variety (cybernetics); Computer science; Artificial intelligence; Machine learning; Pattern recognition (psychology); Geography; Statistics; Cartography; Mathematics; 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":[0.0006087045,0.001528276,0.001137188,0.001741454,0.0004206171,0.0009074018,0.002143853,0.001561406,0.004581843],"category_scores_gemma":[0.001517626,0.0007668206,0.001447536,0.001599435,0.0003005656,0.001207542,0.001304445,0.001667793,0.003199176],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007648505,"about_ca_system_score_gemma":0.001422024,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02140421,"about_ca_topic_score_gemma":0.06034442,"domain_scores_codex":[0.9997279,0.00003148394,0.00001348165,0.0001150862,0.00005367491,0.00005835566],"domain_scores_gemma":[0.9995499,0.000178223,0.00003841425,0.00009170389,0.00009625354,0.0000454533],"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":[0.0007543059,0.001054947,0.0110504,0.00031661,0.000643652,0.0003600413,0.00009562516,0.2282476,0.0183954,0.001718977,0.07004376,0.6673187],"study_design_scores_gemma":[0.00002857041,0.00004036815,0.0009736866,0.00001156245,0.00002335425,0.00003117098,0.00001594567,0.9938075,0.002026565,0.001178666,0.001848485,0.00001414717],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1540666,0.002452017,0.7307447,0.0009070331,0.0005631273,0.0003504135,0.02508752,0.08075921,0.005069473],"genre_scores_gemma":[0.417937,0.0006614745,0.5109909,0.000923859,0.0001900565,0.0004459347,0.0564922,0.001500102,0.01085852],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02140421,"threshold_uncertainty_score":0.04255927,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01768406462357609,"score_gpt":0.2512873734908848,"score_spread":0.2336033088673087,"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."}}