{"id":"W4281937670","doi":"10.5194/isprs-archives-xliii-b3-2022-397-2022","title":"RETRIEVAL OF LEAF AREA INDEX AND LEAF CHLOROPHYLL CONTENT FROM HYPERSPECTRAL DATA USING DEEP LEARNING NETWORKS","year":2022,"lang":"en","type":"article","venue":"The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences","topic":"Leaf Properties and Growth Measurement","field":"Agricultural and Biological Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada; York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Hyperspectral imaging; Leaf area index; Remote sensing; Autoencoder; Vegetation (pathology); Convolutional neural network; Photochemical Reflectance Index; Ground truth; Deep learning; Chlorophyll; Environmental science; Computer science; Mathematics; Artificial intelligence; Normalized Difference Vegetation Index; Botany; Geography; Biology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002190495,0.0004716527,0.0001823955,0.0004079647,0.0001068818,0.0003232327,0.0002193586,0.0002730443,0.0004860995],"category_scores_gemma":[0.0003891766,0.0001343625,0.0002145364,0.0004289853,0.0001140171,0.0005862424,0.0002139208,0.000287651,0.0001859857],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004723373,"about_ca_system_score_gemma":0.0002636724,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005159269,"about_ca_topic_score_gemma":0.007749297,"domain_scores_codex":[0.9999435,0.00001067634,0.000002388367,0.00001720986,0.00001673623,0.000009549204],"domain_scores_gemma":[0.9998966,0.00003320522,0.00001681736,0.00001227514,0.00003553621,0.000005544895],"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":[0.0003823896,0.0004202525,0.02765473,0.0001702239,0.000185056,0.0001259104,0.00009379355,0.4733242,0.1975498,0.001073447,0.001721314,0.2972988],"study_design_scores_gemma":[0.0000036561,0.00001344771,0.00428892,0.000002551551,0.00000748338,0.000007467582,0.000007279967,0.9866112,0.008661002,0.0002477461,0.0001449957,0.0000043179],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8260713,0.0004522138,0.1700756,0.0001435501,0.00003143304,0.00002491728,0.0004988136,0.0007212985,0.001980927],"genre_scores_gemma":[0.9640101,0.0001238285,0.03393357,0.00003712831,0.00001230793,0.00001614437,0.0005698798,0.00001739576,0.001279621],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005159269,"threshold_uncertainty_score":0.01025844,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05226990452151609,"score_gpt":0.2429163554454789,"score_spread":0.1906464509239628,"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."}}