{"id":"W4308757313","doi":"10.3390/a15110401","title":"Image-to-Image Translation-Based Data Augmentation for Improving Crop/Weed Classification Models for Precision Agriculture Applications","year":2022,"lang":"en","type":"article","venue":"Algorithms","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":52,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Artificial intelligence; Computer science; Support vector machine; Pattern recognition (psychology); Machine learning; Convolutional neural network; Feature extraction; Feature (linguistics); Transfer of learning; Deep learning; Contextual image classification; Image (mathematics)","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003898985,0.0001749654,0.0001646161,0.00002379177,0.001008833,0.0001486369,0.000609129,0.00007200822,0.00006792055],"category_scores_gemma":[0.00002880038,0.00007826221,0.0001137112,0.0005225532,0.00002014257,0.0004436674,0.0000884186,0.0001028786,0.000008625058],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006778276,"about_ca_system_score_gemma":0.00002182424,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001374004,"about_ca_topic_score_gemma":0.0001359958,"domain_scores_codex":[0.998408,0.0000524243,0.0003139981,0.0006506711,0.0003029687,0.0002719086],"domain_scores_gemma":[0.9989734,0.0003670794,0.0001563797,0.0001811577,0.0002239685,0.00009802758],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006019015,0.0001586831,0.00000682701,0.00001604298,0.00001035335,1.542816e-7,0.00007906462,0.0006563235,0.714334,0.00009416963,0.009431466,0.2751527],"study_design_scores_gemma":[0.001375368,0.0007416466,0.004835248,0.00001233285,0.0001658519,0.000004979154,0.001825291,0.6839557,0.008396789,0.00199534,0.2959832,0.0007083428],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01828768,0.0002821572,0.9444706,0.01399304,0.0004640332,0.0122798,0.009499026,0.0003730502,0.0003505631],"genre_scores_gemma":[0.4279661,0.00001155487,0.4589475,0.002058312,0.002748954,0.02793857,0.07924441,0.00001725441,0.001067363],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7059372,"threshold_uncertainty_score":0.7759228,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06930151153362646,"score_gpt":0.2886477255569941,"score_spread":0.2193462140233676,"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."}}