{"id":"W4412566594","doi":"10.1002/aisy.70071","title":"Artificial Intelligence‐Driven Robotic Sensing System for Noninvasive Crop Health Monitoring and Autonomous Irrigation Management","year":2025,"lang":"en","type":"article","venue":"Advanced Intelligent Systems","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Artificial intelligence; Computer science; Crop management; Irrigation management; Agricultural engineering; Irrigation; Systems engineering; Crop; Environmental science; Engineering; Agronomy; 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":[0.0001998425,0.0002793473,0.0002646319,0.0002393069,0.0002986732,0.0003583728,0.0005963939,0.0004235386,0.003153474],"category_scores_gemma":[0.0002933231,0.0001767305,0.0002274858,0.0001441141,0.0001863354,0.0003388203,0.0003645423,0.0002997435,0.0009378635],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002360035,"about_ca_system_score_gemma":0.0004223913,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000656871,"about_ca_topic_score_gemma":0.001150655,"domain_scores_codex":[0.9997945,0.00001808353,0.00001181368,0.00005346336,0.000106803,0.00001537058],"domain_scores_gemma":[0.9998653,0.00002817131,0.00002160371,0.00001758147,0.00005363956,0.00001367372],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002234423,0.0002352846,0.00146552,0.0002166343,0.00003713481,0.0003249719,0.0001582151,0.01809707,0.7931158,0.003056325,0.007369056,0.1757005],"study_design_scores_gemma":[0.0001269829,0.00110323,0.00787377,0.00005101113,0.00008347077,0.000784862,0.00008291334,0.6100973,0.3122325,0.002946571,0.06447271,0.0001446294],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1442084,0.0006524138,0.8123035,0.0007430271,0.0004700764,0.0004266907,0.000371793,0.00748804,0.0333361],"genre_scores_gemma":[0.8390375,0.0002483445,0.1367787,0.0005468863,0.0000610833,0.0004248795,0.0002675265,0.00009786475,0.02253723],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003153474,"threshold_uncertainty_score":0.01054943,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03174897388137903,"score_gpt":0.2752794059137349,"score_spread":0.2435304320323559,"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."}}