{"id":"W3090011912","doi":"10.1109/ijcnn48605.2020.9206998","title":"Prediction of Strawberry Yield and Farm Price Utilizing Deep Learning","year":2020,"lang":"en","type":"article","venue":"","topic":"Greenhouse Technology and Climate Control","field":"Agricultural and Biological Sciences","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Convolutional neural network; Yield (engineering); Deep learning; Artificial intelligence; Computer science; Mean squared prediction error; Measure (data warehouse); Artificial neural network; Machine learning; Simple (philosophy); Predictive modelling; Data mining","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":[0.0002031775,0.0006197699,0.0003079083,0.0003019538,0.0001035622,0.0003860294,0.0004177636,0.000374356,0.001035036],"category_scores_gemma":[0.000486293,0.0001822938,0.0003114429,0.0003643819,0.0001069292,0.0006373562,0.0002712265,0.0004033688,0.0002806821],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005357578,"about_ca_system_score_gemma":0.000376246,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008126338,"about_ca_topic_score_gemma":0.01353456,"domain_scores_codex":[0.9999202,0.000007091343,0.000003560372,0.00003374577,0.00002119272,0.00001410807],"domain_scores_gemma":[0.9998683,0.0000418128,0.00002475952,0.000009997809,0.00004376323,0.00001136073],"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":[0.0001797868,0.0002417153,0.02343515,0.00006006601,0.0001176025,0.0002051136,0.00002309075,0.8522289,0.01874124,0.0007362358,0.001351283,0.1026798],"study_design_scores_gemma":[0.000001631352,0.00001614235,0.002650762,9.836047e-7,0.000003756802,0.000004022313,0.000002100733,0.9957776,0.001291273,0.0001726326,0.00007645871,0.000002615552],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8512595,0.0003453751,0.1437269,0.0001743529,0.00005820045,0.00002182971,0.0005686773,0.001123645,0.002721529],"genre_scores_gemma":[0.9914752,0.00007385069,0.006784687,0.0000173235,0.00001037284,0.000008992241,0.0003908211,0.00001340737,0.001225319],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008126338,"threshold_uncertainty_score":0.0161581,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04000569803913889,"score_gpt":0.2004728036239308,"score_spread":0.1604671055847919,"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."}}