{"id":"W2747291751","doi":"10.1109/iccvw.2017.244","title":"Leaf Counting with Deep Convolutional and Deconvolutional Networks","year":2017,"lang":"en","type":"preprint","venue":"","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Artificial intelligence; Computer science; Convolutional neural network; Deep learning; Task (project management); Pattern recognition (psychology); Segmentation; RGB color model; Image (mathematics); Standard deviation; Mathematics; Statistics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001015845,0.001400412,0.000904545,0.001309107,0.0003763447,0.001115906,0.00269415,0.001492969,0.001614486],"category_scores_gemma":[0.002253015,0.000544942,0.0007349786,0.001195169,0.000577677,0.002032537,0.001540015,0.001385969,0.001370115],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001203639,"about_ca_system_score_gemma":0.0008097354,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004217991,"about_ca_topic_score_gemma":0.007159144,"domain_scores_codex":[0.9993893,0.00007888571,0.00002536207,0.0002503624,0.0001771883,0.00007895179],"domain_scores_gemma":[0.9991084,0.0003027444,0.0001244726,0.0002260883,0.0001809439,0.00005739037],"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":[0.0005300244,0.0002623677,0.00499907,0.0003208538,0.0001468809,0.0001945497,0.00009709095,0.2715759,0.07427142,0.008036858,0.008084365,0.6314806],"study_design_scores_gemma":[0.000006823463,0.00002812053,0.0007500923,0.000009934861,0.0000101686,0.00006141351,0.00001112605,0.9797366,0.01430184,0.003710787,0.001362119,0.00001088462],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06675743,0.001023535,0.9158871,0.0003523958,0.00007869879,0.00006552388,0.0009222634,0.01202874,0.002884325],"genre_scores_gemma":[0.4190754,0.0005151767,0.5686939,0.0004514246,0.00008882265,0.0001087936,0.004012627,0.0005312386,0.006522712],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004217991,"threshold_uncertainty_score":0.008733094,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01562166570343032,"score_gpt":0.2022812052405432,"score_spread":0.1866595395371129,"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."}}