{"id":"W4408794029","doi":"10.1109/icccit62592.2025.10928096","title":"AI for Smart Farming: Machine Learning Models for Precision Crop Yield Prediction","year":2025,"lang":"en","type":"article","venue":"","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Horizon College and Seminary","funders":"","keywords":"Yield (engineering); Computer science; Precision agriculture; Crop; Machine learning; Agricultural engineering; Artificial intelligence; Agriculture; Crop yield; Agronomy; Engineering; Materials science; Biology","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":[0.0001913896,0.0001138745,0.0001355851,0.000011935,0.0003620418,0.00007268411,0.0001281677,0.000115929,0.00008310846],"category_scores_gemma":[0.0001000496,0.00003530025,0.0001396684,0.0001859101,0.00001154063,0.0001532481,0.00004141386,0.00008975726,0.000003383373],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001385465,"about_ca_system_score_gemma":0.000004634645,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001087025,"about_ca_topic_score_gemma":0.0004175989,"domain_scores_codex":[0.9992317,0.00001517257,0.000183654,0.0002720241,0.00008953235,0.0002079465],"domain_scores_gemma":[0.9993761,0.0003947399,0.00003839753,0.00003065151,0.0001182146,0.00004186916],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004186381,0.0002594078,0.007702969,0.0000540094,0.00006809166,3.899113e-7,0.0001188125,0.001704752,0.3070501,0.01150491,0.1843109,0.486807],"study_design_scores_gemma":[0.000486751,0.0008309209,0.006992677,0.00009042204,0.00005817737,0.000001805745,0.0002066611,0.09166722,0.02362201,0.01533906,0.8604446,0.0002597046],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7778981,0.00109733,0.1399462,0.04289475,0.002287263,0.005107183,0.0004275297,0.001106842,0.02923484],"genre_scores_gemma":[0.9634664,0.00003667842,0.0007620433,0.001707526,0.0004123695,0.0002135044,0.0003189571,0.000001010414,0.03308152],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6761337,"threshold_uncertainty_score":0.278457,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03660681146137273,"score_gpt":0.2423387959945775,"score_spread":0.2057319845332047,"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."}}