{"id":"W4293770138","doi":"10.3390/plants11172230","title":"Deep Learning Utilization in Agriculture: Detection of Rice Plant Diseases Using an Improved CNN Model","year":2022,"lang":"en","type":"article","venue":"Plants","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":237,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Chicoutimi","funders":"Commonwealth Cyber Initiative","keywords":"Convolutional neural network; Rice plant; Transfer of learning; Artificial intelligence; Deep learning; Computer science; Machine learning; Task (project management); Population; Leaf spot; Agriculture; Agricultural engineering; Agronomy; Biology; Engineering","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":[0.0000897224,0.00009718459,0.0001272855,0.00001753175,0.0002566801,0.00001834689,0.0001235072,0.00004330057,0.00003597288],"category_scores_gemma":[0.00001737384,0.0000384864,0.00003879573,0.0003097542,0.000009447329,0.0001491804,0.00005908618,0.0001352494,9.296832e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003691817,"about_ca_system_score_gemma":0.000004462213,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000524663,"about_ca_topic_score_gemma":0.0009580899,"domain_scores_codex":[0.9991781,0.0000997084,0.000165603,0.0002084893,0.0001756474,0.0001724847],"domain_scores_gemma":[0.9997454,0.00004461246,0.0001094624,0.00002570991,0.00002207913,0.00005273678],"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":[0.00006022031,0.0001847673,0.007252873,0.000005990169,0.000005043216,0.000003184316,0.0002083595,0.02812307,0.959004,0.000004049435,0.00001729355,0.005131111],"study_design_scores_gemma":[0.000332866,0.000409989,0.3261728,0.00001751446,0.00003176573,0.00003338132,0.002932564,0.647162,0.02145257,0.0001312527,0.000983435,0.0003398804],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9992768,0.0002033765,0.00007953625,0.0000167936,0.00007966008,0.000131922,0.0001312542,0.00004046172,0.00004022377],"genre_scores_gemma":[0.999212,0.00002565847,0.00002431664,0.00003551611,0.00008382335,0.00001522271,0.000569563,7.581413e-7,0.00003318524],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9375514,"threshold_uncertainty_score":0.1974202,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03690455920233257,"score_gpt":0.2211348372992433,"score_spread":0.1842302780969108,"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."}}