{"id":"W4225372885","doi":"10.36939/ir.202205031043","title":"Exploring Deep Neural Networks for Plant Image Classification","year":2022,"lang":"en","type":"dissertation","venue":"","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs; University of Winnipeg","keywords":"Convolutional neural network; Artificial intelligence; Computer science; Plant identification; Identification (biology); Machine learning; Task (project management); Categorization; Object (grammar); Generalist and specialist species; Cash crop; Automation; Deep learning; Precision agriculture; Pattern recognition (psychology); Agriculture; Ecology; Biology; Engineering","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":[0.0004189376,0.0009234683,0.0005026758,0.0005444213,0.0002035711,0.0007738479,0.001233471,0.0009714096,0.001446822],"category_scores_gemma":[0.0008670751,0.0003745156,0.0005984053,0.000679518,0.0003749391,0.001197531,0.0005773181,0.00109239,0.0005071955],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001093464,"about_ca_system_score_gemma":0.0005537777,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009044257,"about_ca_topic_score_gemma":0.01134315,"domain_scores_codex":[0.9998507,0.00002718776,0.000005373752,0.00004275001,0.0000385204,0.00003547827],"domain_scores_gemma":[0.9997723,0.0001106864,0.0000279418,0.00002079577,0.00005648782,0.00001187244],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001708922,0.0001865251,0.003210421,0.0001932134,0.0001337371,0.0001258444,0.00006218402,0.7347993,0.01225238,0.00888898,0.004777241,0.2351994],"study_design_scores_gemma":[0.000003279222,0.00001502545,0.0002497867,0.000006728899,0.000006017681,0.000008007548,0.000005418829,0.9950879,0.0009179299,0.003258723,0.0004384382,0.000002759934],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2337519,0.008465776,0.7405082,0.002011971,0.0002099889,0.0001155031,0.001157545,0.003168237,0.01061082],"genre_scores_gemma":[0.8431926,0.002316888,0.1429458,0.0005383814,0.0001073236,0.0001140501,0.001892226,0.0001052284,0.008787465],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009044257,"threshold_uncertainty_score":0.01798326,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08501421682625661,"score_gpt":0.247164479656904,"score_spread":0.1621502628306474,"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."}}