01981naa a2200289 a 450000100080000000500110000800800410001902200140006002400510007410000160012524501060014126000090024752011640025665000200142065000120144065000120145265300120146465300110147665300220148765300250150965300230153465300210155765300180157870000120159670000150160877300680162321726372025-02-11       2025    bl uuuu     u00u1 u    #d  a1548-09927 ahttps://doi.org/10.1109/TLA.2025.108791932DOI1 aTACA, B. S.  aA comparative study between deep learning approaches for aphid classification.h[electronic resource]  c2025  aThis study presents a performance comparison between two convolutional neural networks in the task of detecting aphids in digital images: AphidCV, customized for counting, classifying, and measuring aphids, and YOLOv8, state-of-the-art in real-time object detection. Our work considered 48,000 images for training for six different insect species (8,000 images divided into four classes), in addition to data augmentation techniques. For comparative purposes, we considered evaluation metrics available to both architectures (Accuracy, Precision, Recall, and F1-Score) and additional metrics (ROC Curve and PR AUC for AphidCV; mAP50 and mAP50-95 for YOLOv8). The results revealed an average F1-Score=0.891 for the AphidCV architecture, version 3.0, and an average F1-Score=0.882 for the YOLOv8, medium version, demonstrating the effectiveness of both architectures for training aphid detection models. Overall, AphidCV performed slightly better for the majority of metrics and species in the study, serving its design purpose very well. YOLOv8 proved to be faster to converge the models, with the potential to apply in research considering many aphid species.  aNeural networks  aAfídeo  aPulgão  aAphidCV  aAphids  aComparative study  aDetecção de objeto  aEstudo comparativo  aObject detection  aRedes neurais1 aLAU, D.1 aRIEDER, R.  tIEEE Latin America Transactionsgv. 23, n. 3, p. 198-204, 2025.