{"id":"W2117405177","doi":"","title":"Recognition of weeds with image processing and their use with fuzzy logic for precision farming","year":2000,"lang":"en","type":"article","venue":"Canadian agricultural engineering","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":53,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"RGB color model; Fuzzy logic; Pixel; Precision agriculture; Weed; Weed control; Artificial intelligence; Computer science; Field (mathematics); Image processing; Fuzzy control system; Computer vision; Mathematics; Image (mathematics); Agriculture; Geography; Agronomy","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":[0.00005338979,0.0002003121,0.0001866874,0.00002057971,0.0001511308,0.0001122191,0.0000879316,0.00007801318,0.00004189226],"category_scores_gemma":[0.00001289624,0.00005538059,0.00003987715,0.0003507488,0.00002806826,0.0004037359,0.000006336511,0.00008727563,0.000002076848],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003768298,"about_ca_system_score_gemma":0.00001172412,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003462542,"about_ca_topic_score_gemma":0.02701656,"domain_scores_codex":[0.9992158,0.000007944011,0.0001417688,0.0002375934,0.00008160475,0.0003152515],"domain_scores_gemma":[0.9995119,0.00007550653,0.00004592374,0.00002910099,0.0001285402,0.0002090295],"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":[0.0001040205,0.00003706528,0.001131696,0.0001060297,0.00005695854,0.00001018727,0.0007469975,0.0005925731,0.4450622,0.00005012392,0.0005224359,0.5515798],"study_design_scores_gemma":[0.001615592,0.002543066,0.8669267,0.001804624,0.0001856114,0.0006491363,0.006185683,0.001756095,0.05017525,0.000135588,0.06555892,0.002463717],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9986264,0.000179232,0.0000199613,0.0002627531,0.0000151182,0.0003530915,0.00006784546,0.00005182996,0.000423739],"genre_scores_gemma":[0.9977358,0.00002174241,0.00157925,0.00006305565,0.0001577505,0.00003590701,0.0001810748,0.000002317867,0.0002230978],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.865795,"threshold_uncertainty_score":0.9907379,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01175288241331142,"score_gpt":0.16060071173819,"score_spread":0.1488478293248786,"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."}}