{"id":"W4407785681","doi":"10.1109/icmcsi64620.2025.10883186","title":"Smart Agriculture: A Comprehensive Approach to Crop Disease Diagnosis using Offline Multilingual Technologies","year":2025,"lang":"en","type":"article","venue":"","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Horizon College and Seminary","funders":"","keywords":"Agriculture; Computer science; Crop; Precision agriculture; Agricultural engineering; Data science; Engineering; Geography; Forestry; Ecology; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000495957,0.001129797,0.0006819849,0.001195495,0.0004080591,0.001160325,0.0008282874,0.0008061528,0.002382247],"category_scores_gemma":[0.001065152,0.0002679004,0.0006531504,0.0006679263,0.0003123941,0.001694774,0.001469831,0.0007308209,0.002037903],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004797328,"about_ca_system_score_gemma":0.0007673735,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003848855,"about_ca_topic_score_gemma":0.007102939,"domain_scores_codex":[0.9995866,0.00006430518,0.00002208611,0.0001982682,0.00008650205,0.00004226142],"domain_scores_gemma":[0.9996067,0.0001040682,0.00005562003,0.00008435427,0.0001123568,0.00003688037],"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":[0.000494588,0.0004702465,0.0155734,0.0004997849,0.0003328614,0.0008570999,0.0004842277,0.04807285,0.09119707,0.003891596,0.01692944,0.8211969],"study_design_scores_gemma":[0.00004924484,0.0004100766,0.01043347,0.0001438195,0.0002390541,0.0008450066,0.0004387251,0.8591903,0.05757669,0.01817802,0.05236041,0.0001350503],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0604324,0.001449079,0.8950264,0.0007098498,0.0002413236,0.0001861592,0.002173663,0.02588602,0.01389505],"genre_scores_gemma":[0.5668623,0.001217047,0.4140898,0.001213838,0.0001750513,0.0002348398,0.004671614,0.0005614093,0.01097425],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003848855,"threshold_uncertainty_score":0.007969439,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03150561899085841,"score_gpt":0.2551451061381855,"score_spread":0.2236394871473271,"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."}}