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TableĀ 3 mAP metric score for different models developed from this study

From: AI-powered banana diseases and pest detection

Architecture Model Training time (h) Accuracy
Faster R-CNN InceptionV2 Entire plant 30 0.728579
Faster R-CNN InceptionV2 Leaves 30 0.701833
Faster R-CNN InceptionV2 Pseudostem 30 0.999447
Faster R-CNN InceptionV2 Fruit bunch 30 0.973025
Faster R-CNN InceptionV2 Cut fruit 30 0.953296
Faster R-CNN InceptionV2 Corm 30 0.979151
Faster R-CNN ResNet50 Entire plant 20 0.734611
Faster R-CNN ResNet50 Leaves 20 0.703871
Faster R-CNN ResNet50 Pseudostem 20 0.999905
Faster R-CNN ResNet50 Fruit bunch 20 0.973634
Faster R-CNN ResNet50 Cut fruit 20 0.941152
Faster R-CNN ResNet50 Corm 20 0.976888
SSD MobileNetV1 Entire plant 50 0.446880
SSD MobileNetV1 Leaves 50 0.619923
SSD MobileNetV1 Pseudostem 50 0.98239
SSD MobileNetV1 Fruit bunch 50 0.936463
SSD MobileNetV1 Cut fruit 50 0.923548
SSD MobileNetV1 Corm 50 0.997296