{"id":"W4411049744","doi":"10.1016/j.eja.2025.127727","title":"Early detection of clubroot in canola using drone-based hyperspectral imaging and machine learning","year":2025,"lang":"en","type":"article","venue":"European Journal of Agronomy","topic":"Plant Disease Resistance and Genetics","field":"Agricultural and Biological Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph; Agriculture and Agri-Food Canada; Prince Albert Grand Council","funders":"Saskatchewan Canola Development Commission; Ministry of Agriculture - Saskatchewan","keywords":"Clubroot; Hyperspectral imaging; Canola; Agronomy; Drone; Environmental science; Biology; Artificial intelligence; Computer science; Botany; Brassica","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002548028,0.00005793784,0.0001064601,0.00003883778,0.00005281668,0.00002549364,0.00007226781,0.000007788442,0.000005725225],"category_scores_gemma":[0.0000163135,0.00002746275,0.00005359777,0.0001323322,0.00002854945,0.00006131396,0.00001582202,0.0001146147,3.700357e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000219774,"about_ca_system_score_gemma":0.00001443237,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007681757,"about_ca_topic_score_gemma":0.0001000983,"domain_scores_codex":[0.9994125,0.0001531959,0.000203697,0.00007063759,0.0000634505,0.00009653025],"domain_scores_gemma":[0.9997389,0.00004189774,0.0001314478,0.00001467714,0.00003425786,0.0000388347],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0001247809,0.00003506207,0.2508625,0.00001173274,0.00001713947,0.00005004649,0.00005351687,0.000606792,0.6949158,0.000006967618,0.000006772326,0.05330881],"study_design_scores_gemma":[0.0003494704,0.0001058402,0.985117,0.0001509699,0.00002377963,0.00001141296,0.0001756224,0.0006004203,0.01233588,0.00002215133,0.001039093,0.0000683694],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9966449,0.002571182,0.0001535533,0.0001283799,0.00003016698,0.00002835559,0.000002588137,0.000003190911,0.0004376831],"genre_scores_gemma":[0.999653,0.00004569813,0.0002191049,0.00002415151,0.00004037604,6.946092e-8,0.000001023063,6.366358e-7,0.00001597399],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7342545,"threshold_uncertainty_score":0.1119899,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01122163481288923,"score_gpt":0.196276352862105,"score_spread":0.1850547180492157,"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."}}