{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005738515,0.00009277966,0.0001265423,0.00002191674,0.0000341253,0.00001930615,0.00005132619,0.0000814809,0.00003695913],"category_scores_gemma":[0.000009280629,0.00003943956,0.00002365698,0.000332793,0.00002707656,0.0001273625,0.000005777556,0.00007900304,0.000001960703],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002226619,"about_ca_system_score_gemma":0.000002598636,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.009714578,"about_ca_topic_score_gemma":0.03624124,"domain_scores_codex":[0.9994379,0.00001182806,0.000193127,0.0001447151,0.00005417545,0.0001582784],"domain_scores_gemma":[0.9997555,0.00003206622,0.00004116055,0.00002201218,0.00003993338,0.0001093439],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.000007541982,0.00001391854,0.003615005,0.00001645835,0.000005486272,0.000001087468,0.00009789184,0.0009387934,0.6660509,0.00009250103,0.00005850482,0.3291019],"study_design_scores_gemma":[0.00005636907,0.00003743808,0.9819844,0.00002154211,0.000007600233,0.000009292915,0.0001231364,0.01587668,0.001204011,0.00001744294,0.0005420675,0.0001200223],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9991728,0.0001815372,0.000007205044,0.000284234,0.00002301155,0.0001497705,0.00002523973,0.00001351847,0.0001426875],"genre_scores_gemma":[0.9996101,0.0000438162,0.00004839626,0.00001626302,0.0001102755,0.00001179252,0.0001443512,6.967926e-7,0.00001435009],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9783694,"threshold_uncertainty_score":0.9968798,"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."}}