BananaGAN : Augmenting major banana disease detection using generated diseased pseudostem and rachis images
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DescriptionEarly detection and timely management of crop diseases are essential for reducing yield loss. Traditional manual inspection is often time-consuming, laborious, and biased. Recently, automated imaging techniques have been successfully applied to the detection of crop diseases. Almost this type of research requires a huge amount of images with key typical symptoms from rare classes. The rare class images are the key to di erentiated closely related diseased symptoms, but it is mostly internal and di cult to get them. Thus we exploited generative adversarial networks for generating rare classes such as banana pseudostem and rachis images creating new datasets with synthetic images and doing domain disease translation, converting an image with a certain disease into another image with another di erent disease. These synthetic images were tested in pre-trained disease detection models to see if they are good enough to balance the banana disease datasets and improve the object detection models' overall accuracy and can be applied to other deep learning techniques such as classi cation and semantic segmentation. mAP score from the trained models with synthetic images was between 64% and 89% accuracy, which conclude that synthetic images are a useful tool as a data augmentation technique.
KeywordsFacultad de Ingeniería
Maestría en Ingeniería Electrónica
Arti cial intelligence
Generative adversarial networks
CitationVergara Zorrilla, J. a. (2021, marzo 26) BananaGAN : Augmenting major banana disease detection using generated diseased pseudostem and rachis images. Pontificia Universidad Javeriana, Cali.
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