{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002700352,0.0004541849,0.0002314495,0.0008860696,0.0001193366,0.0002361586,0.0002369344,0.0002124078,0.0003066439],"category_scores_gemma":[0.0004486166,0.0001526488,0.0003535835,0.0006449786,0.00007756495,0.000415855,0.0001950244,0.0002497144,0.0001638523],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002599311,"about_ca_system_score_gemma":0.0001833467,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005487853,"about_ca_topic_score_gemma":0.00735576,"domain_scores_codex":[0.9998463,0.00002438335,0.000006555572,0.00003891732,0.00006765471,0.00001602682],"domain_scores_gemma":[0.9998842,0.000024865,0.00002858139,0.00001061846,0.00004275649,0.000008847324],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004986179,0.000255661,0.165696,0.000346781,0.0002874765,0.0003005997,0.0002010848,0.1252485,0.3685754,0.001195677,0.002871177,0.334523],"study_design_scores_gemma":[0.00001967354,0.0001779862,0.1439869,0.0000175616,0.00006444813,0.0001293643,0.00006421708,0.8121333,0.04100602,0.0003200829,0.002025401,0.00005504871],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7978132,0.0008506743,0.1956948,0.00005906866,0.0000503569,0.00006287359,0.001068229,0.00119128,0.003209547],"genre_scores_gemma":[0.9373559,0.0002521854,0.06061987,0.00001634596,0.000009750337,0.00004638734,0.0007865726,0.00003560785,0.0008773408],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005487853,"threshold_uncertainty_score":0.01091182,"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."}}