{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003280597,0.0004418761,0.0004473519,0.001072578,0.0001197372,0.0003209469,0.0002752193,0.0004096516,0.0005001561],"category_scores_gemma":[0.0005253364,0.000127828,0.000429747,0.0003863705,0.0001670594,0.0005109532,0.0001582588,0.0002813229,0.0002153872],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001570812,"about_ca_system_score_gemma":0.000169903,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001115359,"about_ca_topic_score_gemma":0.00124379,"domain_scores_codex":[0.9998319,0.0000237119,0.00001543144,0.00005040451,0.00005457423,0.00002396362],"domain_scores_gemma":[0.9997417,0.0000826172,0.00004507417,0.00002538076,0.00008518449,0.0000200491],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004276526,0.0001742646,0.0149593,0.0002657971,0.00005857131,0.0003067012,0.0001579546,0.01292272,0.4711951,0.0004454071,0.0007850173,0.4983015],"study_design_scores_gemma":[0.00003208579,0.0007374214,0.08261595,0.00004905879,0.0001056578,0.0009290986,0.0002297607,0.6890067,0.2217275,0.00103855,0.003458063,0.00007011703],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.451402,0.0008506086,0.5445705,0.0001461677,0.0000715455,0.0000966237,0.0002534591,0.001276089,0.00133285],"genre_scores_gemma":[0.8235998,0.0004525877,0.1742811,0.00005837718,0.00002903643,0.00005516723,0.0003307652,0.00002861116,0.001164507],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001115359,"threshold_uncertainty_score":0.00221777,"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."}}