{"id":"W2996142909","doi":"10.1109/iecon.2019.8927543","title":"IoT based Plant Monitoring and Identification using Low-Cost Image Sensors","year":2019,"lang":"en","type":"article","venue":"","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Naive Bayes classifier; Classifier (UML); Artificial intelligence; Image sensor; Cloud computing; Identification (biology); Image processing; Data mining; Real-time computing; Computer vision; Pattern recognition (psychology); Image (mathematics); Support vector machine","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":[0.0001318315,0.0003693684,0.0003890764,0.0005660229,0.0002125687,0.000472351,0.0006004448,0.0004658379,0.001870849],"category_scores_gemma":[0.0003055673,0.0001951844,0.0002189403,0.000400374,0.0001189385,0.0007036505,0.0003216637,0.0002236905,0.0009142547],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001823355,"about_ca_system_score_gemma":0.0001494567,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008836793,"about_ca_topic_score_gemma":0.001518841,"domain_scores_codex":[0.9997472,0.00001931301,0.00001390024,0.00006754545,0.000132009,0.00001995463],"domain_scores_gemma":[0.999845,0.00004083781,0.00003093684,0.00002244961,0.00004910211,0.00001151366],"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":[0.0005936939,0.0003189432,0.01130265,0.0004953128,0.00008905216,0.0006101913,0.0001404748,0.01235983,0.4431909,0.001947182,0.007125794,0.521826],"study_design_scores_gemma":[0.0001045418,0.0008070762,0.04116368,0.0001657805,0.000240019,0.002362293,0.000205718,0.5539551,0.3494732,0.003524277,0.04786061,0.0001378148],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.178923,0.002421063,0.790423,0.0003354221,0.0004300678,0.0002369662,0.0008196043,0.005863106,0.0205478],"genre_scores_gemma":[0.8069423,0.001207597,0.1767169,0.0004365355,0.000169901,0.0002322805,0.0008880603,0.0001119725,0.0132944],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001870849,"threshold_uncertainty_score":0.006258607,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0179839099080884,"score_gpt":0.2169353574400606,"score_spread":0.1989514475319722,"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."}}