{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008211547,0.0003476138,0.0003203801,0.0008031864,0.0002994924,0.0005834711,0.0005636907,0.0005164536,0.001267643],"category_scores_gemma":[0.001166999,0.0002930483,0.0006989054,0.000598937,0.0004892605,0.0005847391,0.000315885,0.0005319237,0.0003024222],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005463827,"about_ca_system_score_gemma":0.0003796929,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003518182,"about_ca_topic_score_gemma":0.003540696,"domain_scores_codex":[0.999679,0.00007280889,0.00002790186,0.00005670784,0.0001387586,0.00002495115],"domain_scores_gemma":[0.9996129,0.0001858684,0.00004059049,0.00003348717,0.0001161996,0.00001095216],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003109908,0.0002305756,0.003977195,0.0004352673,0.0001123269,0.0003437726,0.0004181364,0.1573061,0.2274686,0.01066156,0.001399445,0.5973359],"study_design_scores_gemma":[0.00003427637,0.0003419248,0.00377631,0.00006831829,0.0000893144,0.0002945008,0.0001035721,0.9166617,0.06430302,0.008747063,0.005515062,0.00006491716],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05364145,0.001034113,0.9412188,0.0002104809,0.00004762,0.0001408835,0.00005005691,0.000367115,0.003289513],"genre_scores_gemma":[0.3969846,0.0009005714,0.6001981,0.00008081293,0.00002620511,0.0001377118,0.00007082373,0.00001867939,0.001582413],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003518182,"threshold_uncertainty_score":0.00699544,"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."}}