{"id":"W4403592063","doi":"10.1016/j.jclepro.2024.144024","title":"High-throughput identification of fusarium head blight resistance in wheat varieties using field robot-assisted imaging and deep learning techniques","year":2024,"lang":"en","type":"article","venue":"Journal of Cleaner Production","topic":"Mycotoxins in Agriculture and Food","field":"Agricultural and Biological Sciences","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"Ministry of Agriculture","funders":"Central Public-interest Scientific Institution Basal Research Fund, Chinese Academy of Fishery Sciences; National Key Research and Development Program of China; Chinese Academy of Agricultural Sciences; National Natural Science Foundation of China","keywords":"Fusarium; Field (mathematics); Throughput; Identification (biology); Resistance (ecology); Artificial intelligence; Deep learning; Engineering; Computer science; Agronomy; Machine learning; Biology; Mathematics; Botany","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006630038,0.00008900088,0.0001729791,0.00005911512,0.0000924202,0.00009476773,0.00007964767,0.0000660187,0.00001480766],"category_scores_gemma":[0.00008985023,0.00003448301,0.00005316585,0.0002714388,0.00003696267,0.0004568038,0.00002393119,0.0002649753,4.76476e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004151052,"about_ca_system_score_gemma":0.000006030685,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005765696,"about_ca_topic_score_gemma":0.0002056499,"domain_scores_codex":[0.9989437,0.00009202015,0.0004737814,0.0001812671,0.0001942438,0.0001150116],"domain_scores_gemma":[0.9995276,0.00005482376,0.0001983553,0.00003719198,0.0001583181,0.00002366818],"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":[0.0000522801,0.00003419647,0.0009600658,0.00005463789,0.00001119894,0.00000616487,0.0003694562,0.00007562251,0.933151,0.0001307133,0.0001438827,0.06501079],"study_design_scores_gemma":[0.00006218031,0.0001761368,0.04336596,0.0002873892,0.00004563502,0.0001813001,0.0007561002,0.00008717093,0.9416214,0.001735308,0.01154378,0.0001376433],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9898094,0.00403969,0.0004844159,0.004857027,0.0005527452,0.0001100055,9.185386e-7,0.00003159737,0.0001142229],"genre_scores_gemma":[0.9973902,0.0004856578,0.001184784,0.00001973447,0.000667904,0.000001589967,0.000005077673,0.000001332217,0.0002437162],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06487315,"threshold_uncertainty_score":0.1406176,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01427692411515985,"score_gpt":0.2497196861113948,"score_spread":0.2354427619962349,"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."}}