{"id":"W3043765253","doi":"10.48550/arxiv.1906.07771","title":"Crop Lodging Prediction from UAV-Acquired Images of Wheat and Canola\\n using a DCNN Augmented with Handcrafted Texture Features","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canada First Research Excellence Fund","keywords":"Canola; Convolutional neural network; Computer science; Artificial intelligence; Deep learning; Crop; Texture (cosmology); Pattern recognition (psychology); Machine learning; Agricultural engineering; Image (mathematics); Agronomy; 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":[0.00007685569,0.0006395821,0.000188848,0.0006170371,0.0001407028,0.0002999896,0.0002954888,0.0003511754,0.0009389445],"category_scores_gemma":[0.0002230732,0.0001641582,0.0003561879,0.0003828893,0.00009320404,0.000273095,0.0002230992,0.0003063018,0.0005042503],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003166207,"about_ca_system_score_gemma":0.0002221233,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01209323,"about_ca_topic_score_gemma":0.03135604,"domain_scores_codex":[0.9999576,0.000001820909,0.000001314874,0.00001896107,0.000007773661,0.00001263057],"domain_scores_gemma":[0.999953,0.00001015231,0.000006483177,0.000007122258,0.0000163221,0.000006934505],"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":[0.0005008401,0.0003671182,0.05055302,0.0002351123,0.0002018735,0.0005655191,0.0001297557,0.1606325,0.1159491,0.0004583646,0.01560597,0.6548008],"study_design_scores_gemma":[0.0000104791,0.0000708236,0.03570558,0.00002331633,0.0000408447,0.00008308879,0.00006481696,0.9477934,0.01337425,0.000307835,0.002508043,0.00001761527],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.870181,0.001634772,0.1127954,0.0002192181,0.0002513801,0.00008672025,0.003317983,0.005121951,0.006391632],"genre_scores_gemma":[0.936416,0.0005650707,0.05240351,0.0001231183,0.00002186153,0.00004227206,0.005493503,0.0001014356,0.004833389],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01209323,"threshold_uncertainty_score":0.02404565,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03185298252426191,"score_gpt":0.1571667427272912,"score_spread":0.1253137602030293,"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."}}