{"id":"W4313247118","doi":"10.3390/agriengineering5010003","title":"Trends and Prospect of Machine Vision Technology for Stresses and Diseases Detection in Precision Agriculture","year":2022,"lang":"en","type":"article","venue":"AgriEngineering","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":63,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary; Dalhousie University","funders":"","keywords":"Automation; Machine vision; Agriculture; Precision agriculture; Productivity; Flexibility (engineering); Computer science; Process (computing); Artificial intelligence; Population; Risk analysis (engineering); Engineering; Business; Geography; Economic growth; Management; Economics","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":[0.00005292699,0.00009013593,0.0001319913,0.00004841678,0.0001132174,0.00001387698,0.00006457423,0.00004269549,0.00001392548],"category_scores_gemma":[0.0000243655,0.00003402951,0.00002769872,0.0005788177,0.00001523338,0.00007551423,0.00009575731,0.00008201742,5.099021e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001305844,"about_ca_system_score_gemma":8.627923e-7,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004022062,"about_ca_topic_score_gemma":0.0002630356,"domain_scores_codex":[0.9994712,0.00000969181,0.00012301,0.0001894985,0.00008442326,0.0001221041],"domain_scores_gemma":[0.9998133,0.000075996,0.00003971991,0.00002355217,0.00001755779,0.00002991045],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00004336499,0.00008297287,0.03128845,0.00002655199,0.000007709967,0.000001898909,0.00006722561,0.0003029433,0.6218371,0.0001809218,0.00009943054,0.3460614],"study_design_scores_gemma":[0.000305997,0.0008123796,0.963369,0.00003077347,0.00001657129,0.00002258655,0.0003788534,0.001028206,0.0233069,0.0002596638,0.01029179,0.0001772766],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9973537,0.001847551,0.00001815516,0.0004321828,0.00005871055,0.0001611299,0.00006567116,0.00004589601,0.00001695423],"genre_scores_gemma":[0.9996642,0.00005995034,0.00005803787,0.000005510994,0.00004651898,0.00007752549,0.00004199383,7.638055e-7,0.00004546716],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9320806,"threshold_uncertainty_score":0.1387683,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.002937702280061139,"score_gpt":0.183009358009841,"score_spread":0.1800716557297799,"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."}}