{"id":"W4412627704","doi":"10.1016/j.asoc.2025.113662","title":"Artificial intelligence-based semi-supervised crop and weed semantic segmentation","year":2025,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Institute for Information and Communications Technology Promotion; Information Technology Research Centre; Ministry of Science and ICT, South Korea; Iran Telecommunication Research Center","keywords":"Weed; Artificial intelligence; Computer science; Segmentation; Crop; Natural language processing; Machine learning; Pattern recognition (psychology); Agronomy; Biology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.0004943516,0.0008019758,0.001103636,0.002120021,0.0005178425,0.00109156,0.001126029,0.001024839,0.002160726],"category_scores_gemma":[0.0008445514,0.0004030107,0.001369297,0.001800354,0.0005924812,0.00111514,0.0007988822,0.0006209352,0.001223559],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005045932,"about_ca_system_score_gemma":0.001311713,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005899696,"about_ca_topic_score_gemma":0.0103088,"domain_scores_codex":[0.9995196,0.00004916381,0.00002990807,0.0002092627,0.0001073784,0.0000846827],"domain_scores_gemma":[0.999537,0.0001330179,0.00006161557,0.00006740975,0.0001651965,0.00003584936],"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":[0.0008158825,0.0004041329,0.003896454,0.0003384285,0.0002242953,0.0002900925,0.0002646388,0.1238339,0.1520595,0.003924678,0.005372545,0.7085754],"study_design_scores_gemma":[0.00001357568,0.00006112055,0.0025343,0.00001314878,0.00005567574,0.0001006033,0.00006236089,0.9764505,0.01591184,0.003049097,0.001730833,0.0000169538],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1051859,0.0003943047,0.8848085,0.0001426739,0.00007112381,0.0001388641,0.0007489657,0.004271364,0.004238221],"genre_scores_gemma":[0.6600285,0.0002399192,0.3307926,0.0001477944,0.00006415989,0.0001898245,0.00275136,0.00040207,0.005383826],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005899696,"threshold_uncertainty_score":0.01173073,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01844654394269182,"score_gpt":0.2322385634505317,"score_spread":0.2137920195078399,"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."}}