{"id":"W4379930483","doi":"10.1109/iciccs56967.2023.10142517","title":"Tomato Leaf Disease Detection through Machine Learning based Parallel Convolutional Neural Networks","year":2023,"lang":"en","type":"article","venue":"","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Artificial intelligence; Computer science; Convolutional neural network; Pyramid (geometry); Process (computing); Histogram; Obstacle; Pattern recognition (psychology); Computer vision; Pixel; Histogram of oriented gradients; Image (mathematics); Geography; Mathematics","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":[0.00009577941,0.0001353012,0.0001097105,0.000007235515,0.0003953116,0.00005898302,0.0001162095,0.00006757396,0.0007158605],"category_scores_gemma":[0.00003403955,0.00004425619,0.0001221853,0.0005286563,0.00003213011,0.0001457325,0.00004706046,0.0001550087,0.0001533511],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001387177,"about_ca_system_score_gemma":0.00000338084,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002433756,"about_ca_topic_score_gemma":0.0006675652,"domain_scores_codex":[0.9990348,0.00007230548,0.0001414228,0.0002599729,0.0001900018,0.0003014807],"domain_scores_gemma":[0.9996187,0.0001487714,0.00004267555,0.00003219014,0.00003907028,0.0001185951],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006997831,0.0004492585,0.3595727,0.00003567872,0.00009900281,0.0001468756,0.0001148336,0.452931,0.05661673,0.00215852,0.05207796,0.07509764],"study_design_scores_gemma":[0.0001701888,0.00009609346,0.4464758,0.000005538514,0.00001159195,0.000002821921,0.00007262425,0.5241219,0.0001336832,0.0001683782,0.02854691,0.0001943945],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.991984,0.0001960551,0.0007693078,0.005032867,0.000325654,0.0001937179,0.00001468957,0.0008443545,0.0006393548],"genre_scores_gemma":[0.9963018,0.00001819626,0.00002841445,0.0008531281,0.0005429442,0.00002786622,0.0004677669,9.835413e-7,0.001758842],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08690308,"threshold_uncertainty_score":0.783817,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01899826493224852,"score_gpt":0.2097981746529942,"score_spread":0.1907999097207456,"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."}}