{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002876469,0.0007610933,0.000406918,0.0004568308,0.0001687324,0.000390339,0.0007332807,0.000654861,0.0009039798],"category_scores_gemma":[0.0004226064,0.0002105154,0.000572183,0.0003799854,0.0001670599,0.0005133359,0.0003823531,0.0005818917,0.0003683313],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007243321,"about_ca_system_score_gemma":0.0006009752,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02096757,"about_ca_topic_score_gemma":0.01755651,"domain_scores_codex":[0.9998723,0.00001129482,0.000007118933,0.00004445215,0.00002803891,0.00003675859],"domain_scores_gemma":[0.9998773,0.00002278478,0.00001527957,0.00001369302,0.00005921873,0.00001170587],"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":[0.0004560643,0.0004341911,0.01578165,0.0001464602,0.0002021912,0.0003662293,0.0000667438,0.450411,0.04796664,0.001103085,0.006208544,0.4768573],"study_design_scores_gemma":[0.00000401024,0.00003100359,0.001330964,0.000003763767,0.00001358895,0.00001949858,0.000004552778,0.9953049,0.002808321,0.0001605412,0.0003146152,0.000004229026],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5267577,0.002653505,0.4549613,0.0009284928,0.0003445648,0.0001123481,0.001077548,0.004210992,0.008953598],"genre_scores_gemma":[0.9485006,0.0005642177,0.04255176,0.0002559672,0.00005206601,0.00004089402,0.001107824,0.00002833394,0.006898399],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02096757,"threshold_uncertainty_score":0.04169106,"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."}}