A Deep Learning-based Detector for Brown Spot Disease in Passion Fruit Plant Leaves

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dc.contributor.authorKatumba, Andrew
dc.contributor.authorBomera, Moses
dc.contributor.authorMwikirize, Cosmas
dc.contributor.authorNamulondo, Gorret
dc.contributor.authorAjeroy, Mary Gorret
dc.contributor.authorRamathaniy, Idd
dc.contributor.authorNakayima, Olivia
dc.contributor.authorNakabonge, Grace
dc.contributor.authorOkello, Dorothy
dc.contributor.authorSerugunda, Jonathan
dc.date.accessioned2022-11-27T16:43:25Z
dc.date.available2022-11-27T16:43:25Z
dc.date.issued2020
dc.description.abstractPests and diseases pose a key challenge to passion fruit farmers across Uganda and East Africa in general. They lead to loss of investment as yields reduce and losses increases. As the majority of the farmers including passion fruit farmers, in the country are smallholder farmers from low-income households, they do not have sufficient information and means to combat these challenges. While, passion fruits have the potential to improve the well-being of these farmers given their short maturity period and high market value [1], without the required knowledge about the health of their crops, farmers can not intervene promptly to turn the situation around. For this work, we partnered with the Uganda National Crop Research Institute (NaCRRI) to develop a dataset of expertly labeled passion fruit plant leaves and fruits, both diseased and healthy. We made use of their extension service to collect images from five districts in Uganda to create the dataset. Using the dataset, we are applying state-of-the-art techniques in machine learning, specifically deep learning at scale for object detection and classification for accurate plant health status prediction. While deep learning techniques have been applied to various disease diagnosis contexts with varying degrees of success([2], [3], [4], [5], [6]), there has not been any significant effort, to the best of our knowledge, to create a dataset or apply machine learning techniques to passion fruits despite their obvious financial benefits. With this work, we hope to fill this gap by generating and making publically available an image dataset focusing on passion fruit plant diseases and pest damage and training the first generation of machine learning-based models for passion fruit plant disease identification using this dataset. The initial focus is on the locally prevalent woodiness (viral) and brown spot (fungal) diseases.en_US
dc.identifier.citationKatumba, A., Bomera, M., Mwikirize, C., Namulondo, G., Ajero, M. G., Ramathani, I., ... & Serugunda, J. (2020). A Deep Learning-based Detector for Brown Spot Disease in Passion Fruit Plant Leaves. arXiv preprint arXiv:2007.14103.en_US
dc.identifier.urihttps://arxiv.org/abs/2007.14103
dc.identifier.urihttps://nru.uncst.go.ug/handle/123456789/5481
dc.language.isoenen_US
dc.publisherarXiv preprint arXiven_US
dc.subjectLearning-based Detectoren_US
dc.subjectBrown Spot Diseaseen_US
dc.subjectPassion Fruit Plant Leavesen_US
dc.titleA Deep Learning-based Detector for Brown Spot Disease in Passion Fruit Plant Leavesen_US
dc.typeArticleen_US
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