{"id":"W4229439833","doi":"10.18280/ts.390236","title":"Deep Residual CNN with Contrast Limited Adaptive Histogram Equalization for Weed Detection in Soybean Crops","year":2022,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Overfitting; Residual; Convolutional neural network; Computer science; Artificial intelligence; Adaptive histogram equalization; Weed; Deep learning; Pattern recognition (psychology); Precision agriculture; Residual neural network; Machine learning; Histogram; Artificial neural network; Agriculture; Histogram equalization; Agronomy; Image (mathematics); Algorithm","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":[0.0001825333,0.0004969467,0.0002336134,0.0002872734,0.000109155,0.0002356163,0.0005423434,0.0002980704,0.00135846],"category_scores_gemma":[0.0003258481,0.0001610113,0.0003214852,0.0002436218,0.0001244089,0.0004293032,0.0002700017,0.0003707533,0.0003932634],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004801828,"about_ca_system_score_gemma":0.0004431202,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0128334,"about_ca_topic_score_gemma":0.02153622,"domain_scores_codex":[0.9999284,0.000005293591,0.000002657047,0.00002436504,0.00001917222,0.00002022022],"domain_scores_gemma":[0.9999309,0.00001472691,0.00001052342,0.00001035789,0.00002656095,0.000006990097],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000691209,0.0002462478,0.008012313,0.0002163606,0.0001635782,0.0003612359,0.00008065299,0.2463927,0.1777191,0.00183913,0.007521921,0.5567555],"study_design_scores_gemma":[0.00001075333,0.00009677338,0.003595259,0.00001109283,0.00002765862,0.00006972239,0.00001868749,0.965019,0.02896016,0.0005457332,0.001634676,0.00001049601],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5736849,0.002419521,0.4039918,0.0004870133,0.0002137237,0.00009461079,0.001653819,0.006553886,0.01090082],"genre_scores_gemma":[0.9230992,0.0004307913,0.06553549,0.0001336387,0.00001851583,0.00002700519,0.001875588,0.0000852953,0.008794611],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0128334,"threshold_uncertainty_score":0.0255174,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01908596008080461,"score_gpt":0.1989924435153932,"score_spread":0.1799064834345886,"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."}}