Nsumba, SolomonMwebaze, ErnestBagarukayo, EmilyMaiga, Gilbert2023-03-162023-03-162018Nsumba, S., Mwebaze, E., Bagarukayo, E., Maiga, G., & Uganda, K. (2018). Automated image-based diagnosis of cowpea diseases. In Proceedings of the AGILE.https://agile-online.org/images/conferences/2018/documents/posters/154_AGILE_2018_v1cKL_20180507_final.pdfhttps://nru.uncst.go.ug/handle/123456789/8212Cowpea is the third most important legume food crop in Uganda with the eastern and northern regions accounting for most of the production in the country. However, it is vulnerable to virus and fungal diseases, which threaten to destabilize food security in sub-Saharan Africa. Unique methods of cowpea disease detection are needed to support improved control which will prevent this crisis. In this paper, we discuss automated disease detection model for cowpea based on deep neural network computational techniques that can be used by non-experts and smallholder farmers to do the field-based diagnosis of cowpea diseases. Image recognition offers both a cost-effective and scalable technology for disease detection. New transfer learning methods offer an avenue for this technology to be easily deployed on mobile devices. Using a dataset of cowpea disease images taken in the field in Uganda, we applied transfer learning to train a deep convolutional neural network to identify three cowpea diseases and to identify healthy plants as well. The best-trained model accuracies were 98% for healthy, 95% for powdery mildew, 98% for cercospora, and 96% for the mosaic virus. The best model achieved an overall accuracy of 93% for data not used in the training process. Our results show that the transfer learning approach for image recognition of field images offers a fast, affordable, and easily deployable strategy for digital plant disease detection.enTransfer learningMobile epidemiologyInception v3 modelMobileNet V1 modelOther