{"id":"W4229440698","doi":"10.18280/ts.390224","title":"Disease Feature Recognition of Hydroponic Lettuce Images Based on Support Vector Machine","year":2022,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Support vector machine; Artificial intelligence; Pattern recognition (psychology); Feature extraction; Preprocessor; Identification (biology); Classifier (UML); Computer science; Segmentation; Computer vision; Botany; Biology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001803405,0.0001380566,0.0001363151,0.00001569748,0.0002328627,0.00001995673,0.0001758444,0.00002468973,0.01143094],"category_scores_gemma":[0.000007550931,0.00005308441,0.0001438771,0.0002393395,0.00002655247,0.00005649864,0.00004011777,0.0001478607,0.00002355165],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000336802,"about_ca_system_score_gemma":0.00001234571,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000337924,"about_ca_topic_score_gemma":0.00002124241,"domain_scores_codex":[0.9988821,0.0001104994,0.0001588366,0.0002459987,0.0004089241,0.0001936044],"domain_scores_gemma":[0.9996558,0.00007583377,0.00009244221,0.00004151886,0.0000322949,0.0001020816],"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":[0.001175532,0.00234901,0.01262304,0.00004344144,0.00005587457,0.0001020033,0.00008828293,0.0005911522,0.835977,0.0001284833,0.09904236,0.04782386],"study_design_scores_gemma":[0.00159449,0.004682028,0.850206,0.00005314336,0.0001650959,0.00001132641,0.0002794893,0.002150756,0.03011372,0.0003952003,0.1095512,0.0007975642],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9865393,0.00008189476,0.000004964167,0.01043859,0.0001363241,0.0003456304,0.001229867,0.00006711956,0.001156283],"genre_scores_gemma":[0.9963404,0.000004344943,0.00002391003,0.001377034,0.0002624996,0.00006503693,0.001585465,0.000001206747,0.0003400945],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8375829,"threshold_uncertainty_score":0.9894727,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01264034285070223,"score_gpt":0.1944583369091632,"score_spread":0.181817994058461,"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."}}