{"id":"W4403869876","doi":"10.3390/technologies12110214","title":"Rice Leaf Disease Classification—A Comparative Approach Using Convolutional Neural Network (CNN), Cascading Autoencoder with Attention Residual U-Net (CAAR-U-Net), and MobileNet-V2 Architectures","year":2024,"lang":"en","type":"article","venue":"Technologies","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Convolutional neural network; Overfitting; Computer science; Artificial intelligence; Autoencoder; Deep learning; Residual; Pattern recognition (psychology); Machine learning; Contextual image classification; Artificial neural network; Random forest; Algorithm; Image (mathematics)","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":[0.0001488542,0.0002484915,0.0002211971,0.00003768936,0.0004430729,0.0002044441,0.0002284697,0.0001485632,0.000009649972],"category_scores_gemma":[0.00002510413,0.00009008565,0.00005894905,0.0006239362,0.0004126779,0.0001550623,0.0001334783,0.0003201667,0.000006635787],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004320005,"about_ca_system_score_gemma":0.00001770841,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007750771,"about_ca_topic_score_gemma":0.0001212783,"domain_scores_codex":[0.9985542,0.00007615319,0.0002031214,0.0005505743,0.0002484921,0.0003674591],"domain_scores_gemma":[0.9995137,0.0002025603,0.00007665773,0.00008727391,0.00005506884,0.000064676],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001182465,0.001006885,0.2918732,0.0007613266,0.001189749,0.0002841904,0.002552011,0.08147582,0.2914247,0.05687561,0.1069909,0.1643832],"study_design_scores_gemma":[0.0001893587,0.0002649805,0.8156585,0.0001826739,0.000140669,0.0001197944,0.003874119,0.171329,0.0002698085,0.002762159,0.004600002,0.0006090294],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9902299,0.004836579,0.0002785553,0.002133283,0.00009663197,0.0003757189,0.0000471686,0.00172366,0.0002785189],"genre_scores_gemma":[0.9973834,0.00005580284,0.001860629,0.00005255987,0.0003129758,0.00007123551,0.0001422486,0.000002315281,0.0001188456],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5237853,"threshold_uncertainty_score":0.3673587,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04571586518062996,"score_gpt":0.2516466166863306,"score_spread":0.2059307515057007,"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."}}