{"id":"W7147199986","doi":"10.1109/icaft66710.2025.11453140","title":"Precision Agriculture Using Hybrid Deep Learning for Soil Classification and Crop Recommendation","year":2025,"lang":"","type":"article","venue":"","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Precision agriculture; Deep learning; Hyperspectral imaging; Convolutional neural network; Artificial neural network; Feature selection; Redundancy (engineering); Feature (linguistics); Scalability","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":[0.0004816949,0.0007009201,0.0004889488,0.0006485948,0.0002283013,0.0006295801,0.0009078675,0.0008049327,0.001015948],"category_scores_gemma":[0.0007260294,0.0003016792,0.0005301413,0.0007231909,0.0002195695,0.000917334,0.0005086794,0.0006786434,0.0005327224],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006762713,"about_ca_system_score_gemma":0.0005934233,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0123389,"about_ca_topic_score_gemma":0.01805475,"domain_scores_codex":[0.9997925,0.00002745847,0.00001178431,0.00007038967,0.00005537905,0.00004239156],"domain_scores_gemma":[0.9997186,0.00008934947,0.00003345196,0.00003923625,0.0001025942,0.00001673628],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002107038,0.0003729998,0.008820876,0.00006462161,0.0002235631,0.00008943248,0.00005183236,0.4571703,0.01386987,0.001459616,0.003221552,0.5144446],"study_design_scores_gemma":[0.000005020136,0.00002560241,0.0006393011,0.000003466351,0.00001122811,0.00001062645,0.000005630721,0.9967452,0.001673292,0.0005789225,0.0002958002,0.000005916729],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1841413,0.001006993,0.8043085,0.0005644353,0.0001346547,0.00008057609,0.0005459437,0.003519407,0.005698113],"genre_scores_gemma":[0.8949534,0.0002555658,0.1001004,0.0002298701,0.00005362838,0.0000568523,0.0005757704,0.00003303231,0.003741536],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0123389,"threshold_uncertainty_score":0.02453417,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03139418575018251,"score_gpt":0.2683725594652822,"score_spread":0.2369783737150996,"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."}}