{"id":"W3083611323","doi":"10.3390/s20185055","title":"Modified Red Blue Vegetation Index for Chlorophyll Estimation and Yield Prediction of Maize from Visible Images Captured by UAV","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":98,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Institute of Crop Sciences, Chinese Academy of Agricultural Sciences; National Key Research and Development Program of China; Chinese Academy of Agricultural Sciences","keywords":"Backpropagation; Yield (engineering); Support vector machine; Vegetation (pathology); Mathematics; Random forest; Mean squared error; Extreme learning machine; Chlorophyll; Chlorophyll a; Vegetation Index; Artificial intelligence; Artificial neural network; Leaf area index; Environmental science; Agronomy; Statistics; Computer science; Horticulture; Botany; Normalized Difference Vegetation Index; Biology; Materials science","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005405578,0.0001146476,0.0001396477,0.0000135951,0.00005658386,0.00002275215,0.00006062005,0.0001133352,0.00001980753],"category_scores_gemma":[0.0001502786,0.00009840055,0.0000347559,0.0001264939,0.00007425616,0.0001345898,0.00002634768,0.00008262155,0.000009601289],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003772601,"about_ca_system_score_gemma":0.000002942377,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003828948,"about_ca_topic_score_gemma":0.00002205158,"domain_scores_codex":[0.9991702,0.00002717739,0.0001970371,0.0002818945,0.0002048355,0.0001188269],"domain_scores_gemma":[0.9996023,0.00007540585,0.0001267234,0.0001083283,0.00001639333,0.00007086247],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009951468,0.00002244909,0.001365213,0.00003158116,0.00001687551,9.964757e-7,0.001626421,0.2014395,0.7842857,0.000003611379,0.007517977,0.003590191],"study_design_scores_gemma":[0.0005141414,0.0001028243,0.09001172,0.00003042976,0.00003440871,0.000001602772,0.0001572264,0.7126142,0.1958761,0.0003084534,0.0002264979,0.0001223332],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9852695,0.00003603395,0.01263153,0.001026757,0.00007637465,0.0003773766,0.00007272308,0.00004776679,0.0004619267],"genre_scores_gemma":[0.9937437,0.00001262412,0.005843937,0.0001316151,0.00005471979,0.000001938689,0.0001001445,0.00001224176,0.00009911233],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5884096,"threshold_uncertainty_score":0.4012658,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01129509205542873,"score_gpt":0.1974736214355214,"score_spread":0.1861785293800926,"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."}}