{"id":"W4414338711","doi":"10.1016/j.plaphe.2025.100110","title":"Edge computing-based computer vision and deep transfer learning for high-throughput assessment of Aspergillus flavus infection in crop seeds","year":2025,"lang":"en","type":"article","venue":"Plant Phenomics","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"National Key Research and Development Program of China; China Scholarship Council; Ministry of Science and Technology of the People's Republic of China; McGill University","keywords":"Aspergillus flavus; Enhanced Data Rates for GSM Evolution; Crop; Machine vision; Segmentation; Transferability; Deep learning; Edge detection","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004162224,0.0005718722,0.0003532651,0.0006891642,0.0001676177,0.0005190711,0.0006410958,0.0006048849,0.0007549227],"category_scores_gemma":[0.001061287,0.0002285093,0.0004211827,0.0004322545,0.0002901736,0.0005021096,0.0004370971,0.0005735745,0.0002810573],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000563166,"about_ca_system_score_gemma":0.0004687448,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00386931,"about_ca_topic_score_gemma":0.00562726,"domain_scores_codex":[0.9998339,0.00002638011,0.000006487885,0.00005471734,0.00005009369,0.00002841835],"domain_scores_gemma":[0.9997223,0.0001005279,0.0000385317,0.00002963634,0.00008849068,0.0000205218],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006924577,0.0004076442,0.0110378,0.000215734,0.00008791259,0.0003358157,0.000129119,0.4305674,0.231078,0.002394155,0.002540698,0.3205132],"study_design_scores_gemma":[0.00000355867,0.00004212246,0.001311502,0.000002835883,0.000006810876,0.00002471885,0.000009335091,0.9834754,0.01436701,0.0005857878,0.0001622665,0.00000858505],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.3527743,0.0004416011,0.640745,0.0002250753,0.00005371456,0.0001045705,0.000319736,0.002611817,0.00272424],"genre_scores_gemma":[0.8573464,0.000165829,0.1405059,0.0001468194,0.00001679349,0.00007873801,0.000294455,0.00007236592,0.00137268],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00386931,"threshold_uncertainty_score":0.007693589,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008866288694207464,"score_gpt":0.2293946493639209,"score_spread":0.2205283606697135,"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."}}