{"id":"W4414282252","doi":"10.1016/j.rineng.2025.107342","title":"Edge-enabled smart agriculture framework: Integrating IoT, lightweight deep learning, and agentic AI for context-aware farming","year":2025,"lang":"en","type":"article","venue":"Results in Engineering","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Moncton","funders":"","keywords":"Cloud computing; Scalability; Precision agriculture; Python (programming language); Agriculture; Software deployment; Deep learning; Multitier architecture","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":[0.0002624257,0.0002556044,0.0003012061,0.00004328293,0.0002215864,0.0001408919,0.0001790175,0.000261285,0.00001394961],"category_scores_gemma":[0.0005881526,0.0001015916,0.0001005236,0.0005953191,0.00001837888,0.00009851597,0.00008648581,0.0005102874,0.000002880922],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000051909,"about_ca_system_score_gemma":0.000005723011,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006684366,"about_ca_topic_score_gemma":0.0005179786,"domain_scores_codex":[0.9986299,0.00003298321,0.0003699836,0.0004198001,0.0001140448,0.0004332461],"domain_scores_gemma":[0.9990864,0.0006267183,0.00007254713,0.00004924129,0.00008504935,0.00008005991],"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":[0.0004342392,0.0004412873,0.0419814,0.0009861563,0.0004061429,0.00009147458,0.005305982,0.007494227,0.3826588,0.01662407,0.0130619,0.5305142],"study_design_scores_gemma":[0.002213675,0.0004295482,0.08980869,0.003514818,0.0001207707,0.00002613386,0.005241447,0.03802759,0.02428071,0.0009793208,0.8337831,0.001574176],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9773927,0.005142251,0.00672921,0.00462859,0.001335501,0.001359948,0.00004974741,0.0005560936,0.00280591],"genre_scores_gemma":[0.9968175,0.0001027155,0.0007490383,0.0002173266,0.0003676991,0.00007017026,0.00007962487,0.000002529993,0.001593384],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8207212,"threshold_uncertainty_score":0.4142783,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005909812434776416,"score_gpt":0.217301114844704,"score_spread":0.2113913024099275,"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."}}