{"id":"W4414806515","doi":"10.1016/j.atech.2025.101491","title":"Precision agriculture in the age of AI: A systematic review of machine learning methods for crop disease detection","year":2025,"lang":"en","type":"article","venue":"Smart Agricultural Technology","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"Zayed University","keywords":"Precision agriculture; Convolutional neural network; Agriculture; Scalability; Deep learning; Big data; Plant disease; Key (lock)","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":[0.0009113994,0.0002658969,0.0008071825,0.00006985996,0.0001748406,0.00002365693,0.0006646556,0.0002552717,0.00001264357],"category_scores_gemma":[0.001367103,0.00006745686,0.0003446599,0.002573955,0.00009872381,0.0001015769,0.0001242438,0.0004085771,0.000002017468],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003697916,"about_ca_system_score_gemma":0.000009443963,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007778386,"about_ca_topic_score_gemma":0.0005774267,"domain_scores_codex":[0.9978704,0.0004539656,0.0008101702,0.0003654505,0.0002036508,0.0002963215],"domain_scores_gemma":[0.9984633,0.0006582825,0.0003613793,0.0001467509,0.0003366516,0.00003366894],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00008851951,0.0003809081,0.004029096,0.05931243,0.0001255469,0.000005771133,0.0001313605,0.00000987222,0.9131843,0.003241951,0.001958217,0.01753205],"study_design_scores_gemma":[0.002221616,0.003160925,0.409969,0.2183539,0.003129902,0.0001576116,0.006229045,0.0002495007,0.2437094,0.01631898,0.09433752,0.002162658],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7763569,0.1620764,0.001483539,0.04286295,0.0008327129,0.01458345,0.0001428466,0.0005478727,0.001113424],"genre_scores_gemma":[0.9944615,0.002291514,0.0006733221,0.0007312066,0.00007524979,0.0008540562,0.0002936059,0.000001773863,0.0006177943],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6694749,"threshold_uncertainty_score":0.2750811,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0111120990992565,"score_gpt":0.283652051204057,"score_spread":0.2725399521048005,"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."}}