{"id":"W4316096560","doi":"10.5194/isprs-annals-x-4-w1-2022-25-2023","title":"CONVOLUTIONAL SVM NETWORKS FOR DETECTION OF <i>GANODERMA BONINENSE</i> AT EARLY STAGE IN OIL PALM USING UAV AND MULTISPECTRAL PLEIADES IMAGES","year":2023,"lang":"en","type":"article","venue":"ISPRS annals of the photogrammetry, remote sensing and spatial information sciences","topic":"Date Palm Research Studies","field":"Agricultural and Biological Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"United Malacca Berhad","keywords":"Pleiades; Ganoderma; Multispectral image; Stage (stratigraphy); Support vector machine; Palm oil; Elaeis guineensis; Palm; Artificial intelligence; Computer science; Remote sensing; Biology; Geography; Computer vision; Agroforestry","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003338951,0.0004942173,0.0002816624,0.0005201758,0.000173497,0.0003374082,0.0003197479,0.0003656564,0.0007905252],"category_scores_gemma":[0.0004883338,0.0001648074,0.0004169034,0.0002201164,0.0001091287,0.0002919565,0.0001757701,0.000364911,0.0002584286],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003806562,"about_ca_system_score_gemma":0.0003047356,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01045768,"about_ca_topic_score_gemma":0.009640045,"domain_scores_codex":[0.9999065,0.00001496937,0.000006017528,0.00002326026,0.00002126196,0.00002808151],"domain_scores_gemma":[0.9998319,0.00005801263,0.00002185734,0.00001082294,0.00006429961,0.00001317674],"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":[0.001289491,0.0007131427,0.05345612,0.0002284911,0.0002552396,0.0006567863,0.0002088697,0.3021307,0.1134974,0.0008055859,0.00405616,0.522702],"study_design_scores_gemma":[0.000003759972,0.0000754541,0.008428218,0.000007337424,0.00001824913,0.00003495622,0.00004170756,0.9842837,0.00680014,0.0001022012,0.0001980718,0.000006149877],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.9285845,0.0006486364,0.06691871,0.0002098312,0.00007958525,0.00004101635,0.0003287664,0.000811737,0.002377195],"genre_scores_gemma":[0.9861653,0.0001559469,0.01174445,0.00002626226,0.000008275134,0.00001284482,0.000329148,0.000008349221,0.001549495],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.01045768,"threshold_uncertainty_score":0.02079368,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07182239318010372,"score_gpt":0.3127444512696963,"score_spread":0.2409220580895926,"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."}}