{"id":"W2097300063","doi":"","title":"Application of artificial neural networks in image recognition and classification of crop and weeds","year":2000,"lang":"en","type":"article","venue":"Canadian agricultural engineering","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":108,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Artificial neural network; Artificial intelligence; Weed; Pixel; Crop; Backpropagation; Weed control; Field (mathematics); Pattern recognition (psychology); Computer science; Agricultural engineering; Agronomy; Mathematics; Biology; Engineering","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":[0.001122155,0.000625423,0.0005091349,0.001047803,0.0002256757,0.0006674292,0.0004012601,0.0009576401,0.0006868352],"category_scores_gemma":[0.001927863,0.0003523619,0.0005840514,0.001227487,0.0003428102,0.0005828873,0.0003860412,0.0005507641,0.0003161113],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000429581,"about_ca_system_score_gemma":0.0003134272,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004518467,"about_ca_topic_score_gemma":0.004477746,"domain_scores_codex":[0.9995317,0.0001152896,0.00005248138,0.00008930457,0.0001781866,0.00003313837],"domain_scores_gemma":[0.999305,0.0003769243,0.00006130524,0.0000469267,0.0001964113,0.00001339806],"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":[0.0001599133,0.0001538807,0.005457869,0.0003543488,0.0001929333,0.0002895827,0.0001188971,0.3561116,0.03248357,0.001779319,0.001344935,0.6015532],"study_design_scores_gemma":[0.000006596423,0.00007478024,0.002591301,0.00003550058,0.00003530765,0.0001106684,0.00002907055,0.9820778,0.01180762,0.001168548,0.002038148,0.00002462137],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1167228,0.008913729,0.8667601,0.000688038,0.0002521916,0.0001363394,0.0001594921,0.001114546,0.00525277],"genre_scores_gemma":[0.6829077,0.0055262,0.3064053,0.0002099331,0.00009884482,0.0001067037,0.0002512975,0.00005038591,0.004443712],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004518467,"threshold_uncertainty_score":0.008984387,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01053080536748256,"score_gpt":0.1639196673916777,"score_spread":0.1533888620241951,"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."}}