{"id":"W4415172557","doi":"10.5539/mas.v19n2p32","title":"Development of a YOLOv11-Based Deep Learning System for Insect Pest Detection and Classification in Oil Palm Plantation","year":2025,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Date Palm Research Studies","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Universiti Teknikal Malaysia Melaka; Universiti Tun Hussein Onn Malaysia","keywords":"PEST analysis; Agriculture; Crop; Identification (biology); Deep learning; Pest control; Integrated pest management; Insect pest; Infestation","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":[0.0007903701,0.00006344604,0.00009805289,0.00006437003,0.0003757325,0.00004397621,0.0001387057,0.00002988609,6.84736e-7],"category_scores_gemma":[0.00007777311,0.00003001,0.00001028482,0.0005663552,0.0001351706,0.00005825422,0.00005005818,0.00005776971,0.000001118558],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000101984,"about_ca_system_score_gemma":0.00003895648,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002813982,"about_ca_topic_score_gemma":0.001071161,"domain_scores_codex":[0.9991496,0.00002017996,0.0001724564,0.0002711272,0.000207523,0.0001790894],"domain_scores_gemma":[0.9996494,0.0001571417,0.00006877942,0.0000284282,0.00006941988,0.00002681124],"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":[0.00003326718,0.00001105704,0.001397959,0.00004765778,9.891435e-7,4.49085e-8,0.0002160669,0.00005828718,0.6870969,0.0001348102,1.49307e-7,0.3110029],"study_design_scores_gemma":[0.0003657659,0.00005871192,0.4751066,0.00008405079,0.000004323059,3.638771e-7,0.002544527,0.3476107,0.1736139,0.000212694,0.0002699225,0.0001284822],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9859316,0.00002871623,0.01275826,0.00005856829,0.00002306792,0.0001981953,0.000003042789,0.00002787103,0.0009706406],"genre_scores_gemma":[0.9987084,0.000007177315,0.001073088,0.000007582189,0.000005701442,0.0001697811,0.000009700897,3.492544e-7,0.00001820953],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.513483,"threshold_uncertainty_score":0.2889869,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04358381038673661,"score_gpt":0.26513855023469,"score_spread":0.2215547398479534,"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."}}