{"id":"W4367322630","doi":"10.1117/12.2663834","title":"Early pest detection in cannabis plants with multispectral imaging: artificial intelligence and machine learning","year":2023,"lang":"en","type":"article","venue":"","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"","keywords":"Multispectral image; Convolutional neural network; Artificial intelligence; Cannabis; Computer science; Machine learning; Psychology","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":[0.0002246084,0.0003066091,0.000214342,0.0004687779,0.0001687657,0.0003623672,0.0003576876,0.0004612533,0.000798365],"category_scores_gemma":[0.0004710011,0.0001622873,0.0002905451,0.0002324675,0.0002150388,0.0004511474,0.000280873,0.0003350149,0.0002394968],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003709843,"about_ca_system_score_gemma":0.0002927149,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004344531,"about_ca_topic_score_gemma":0.007250978,"domain_scores_codex":[0.9999046,0.00001307662,0.000004579239,0.00002645806,0.00003218643,0.00001908192],"domain_scores_gemma":[0.9998083,0.00006754138,0.00003178188,0.00002377385,0.00005279694,0.00001588178],"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":[0.000294784,0.0004783772,0.01026036,0.0001584642,0.00008758162,0.0003370009,0.0001325016,0.08808662,0.3883682,0.001191212,0.001116378,0.5094886],"study_design_scores_gemma":[0.000006697592,0.000112943,0.009871905,0.00001163908,0.00002305362,0.0001389435,0.00003586606,0.911597,0.0763175,0.0008732009,0.0009916839,0.00001950662],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5299237,0.0009327482,0.4611203,0.0004653734,0.0000766442,0.00009843524,0.0001509163,0.002325296,0.00490659],"genre_scores_gemma":[0.8598354,0.0002661821,0.1365529,0.000103735,0.00002152942,0.00003304084,0.0001132245,0.0000290539,0.003044977],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004344531,"threshold_uncertainty_score":0.008638442,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0225817227494427,"score_gpt":0.2131336462641525,"score_spread":0.1905519235147098,"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."}}