{"id":"W4390430553","doi":"10.18280/ts.400625","title":"Detection and Classification of Plant Stress Using Hybrid Deep Convolution Neural Networks: A Multi-Scale Vision Transformer Approach","year":2023,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":13,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Artificial intelligence; Computer science; Artificial neural network; Transformer; Pattern recognition (psychology); Convolution (computer science); Scale (ratio); Engineering; Cartography; Geography; Electrical engineering; Voltage","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":[],"consensus_categories":[],"category_scores_codex":[0.0001621243,0.00011344,0.0001293898,0.00001941193,0.0001675299,0.00003202248,0.00006613789,0.00005792962,0.00002638391],"category_scores_gemma":[0.000001966358,0.0000461101,0.00005953793,0.0002633235,0.00004520829,0.000151528,0.00001147945,0.00007138249,0.000001804848],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001679394,"about_ca_system_score_gemma":0.000001634475,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006124657,"about_ca_topic_score_gemma":0.0002730454,"domain_scores_codex":[0.9991389,0.00005645578,0.0002216369,0.0002227803,0.0001691937,0.0001910436],"domain_scores_gemma":[0.9997585,0.00004262087,0.00008286315,0.00002258013,0.00003762145,0.0000557511],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006874695,0.0001445745,0.006747817,0.00001947326,0.00001406403,9.343732e-7,0.0001525018,0.002569966,0.9401888,0.00001002079,0.00006339538,0.0500197],"study_design_scores_gemma":[0.0002390595,0.0001484653,0.3645372,0.00001418715,0.00002215116,0.000007032124,0.0003540237,0.6285143,0.005946869,0.000006249573,0.000116109,0.0000942659],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9914476,0.00006928531,0.007878207,0.00007473408,0.00007136924,0.0003047277,0.00006671089,0.00006140071,0.00002597953],"genre_scores_gemma":[0.9992653,0.00002996076,0.00009250985,0.00001944748,0.0001826435,0.00001805173,0.0003810785,0.000001046995,0.00001002568],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.934242,"threshold_uncertainty_score":0.1880315,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03313428633819654,"score_gpt":0.2263938042350531,"score_spread":0.1932595178968566,"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."}}