Browsing by Author "Mwebaze, Ernest"

Now showing 1 - 7 of 7
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    Automated image-based diagnosis of cowpea diseases
    (AGILE, 2018) Nsumba, Solomon; Mwebaze, Ernest; Bagarukayo, Emily; Maiga, Gilbert
    Cowpea 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.
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    A dataset of necrotized cassava root cross-section images
    (Data in brief, 2020) Nakatumba-Nabende, Joyce; Akera, Benjamin; Tusubira, Jeremy Francis; Nsumba, Solomon; Mwebaze, Ernest
    Cassava brown streak disease is a major disease affecting cas- sava. Along with foliar chlorosis and stem lesions, a very common symptom of cassava brown streak disease is the development of a dry, brown corky rot within the starch bearing tuberous roots, also known as necrosis. This paper presents a dataset of curated image data of necrosis bearing roots across different cassava varieties. The dataset contains images of cassava root cross-sections based on trial harvests from Uganda and Tanzania. The images were taken using a smartphone camera. The resulting dataset consists of 10,052 images making this the largest publicly available dataset for crop root necrosis. The data is comprehensive and contains different variations of necrosis expression including root cross-section types, number of necrosis lesions, presentation of the necrosis le- sions. The dataset is important and can be used to train ma- chine learning models which quantify the percentage of cas- sava root damage caused by necrosis.
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    Early detection of plant diseases using spectral data
    (Las Palmas de Gran Canaria, Spain., 2020-01-08) Owomugisha, Godliver; Nuwamanya, Ephraim; Quinn, John A.; Mwebaze, Ernest
    Early detection of crop disease is an essential step in food security. Usually, the detection becomes possible in a stage where disease symptoms are already visible on the aerial part of the plant. However, once the disease has manifested in different parts of the plant, little can be done to salvage the situation. Here, we suggest that the use of visible and near infrared spectral information facilitates disease detection in cassava crops before symptoms can be seen by the human eye. To test this hypothesis, we grow cassava plants in a screen house where they are inoculated with disease viruses. We monitor the plants over time collecting both spectra and plant tissue for wet chemistry analysis. Our results demonstrate that suitably trained classifiers are indeed able to detect cassava diseases. Specifically, we consider Generalized Matrix Relevance Learning Vector Quantization (GMLVQ) applied to original spectra and, alternatively, in combination with dimension reduction by Principal Component Analysis (PCA). We show that successful detection is possible shortly after the infection can be confirmed by wet lab chemistry, several weeks before symptoms manifest on the plants.
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    Machine Translation for African Languages: Community Creation of Datasets and Models in Uganda
    (n African Natural Language Processing, 2022) Akera, Benjamin; Mukiibi, Jonathan; Sanyu Naggayi, Lydia; Babirye, Claire; Owomugisha, Isaac; Nsumba, Solomon; Nakatumba-Nabende, Joyce; Bainomugisha, Engineer; Mwebaze, Ernest; Quinn, John
    Reliable machine translation systems are only available for a small proportion of the world’s languages, the key limitation being a shortage of training and evaluation data. We provide a case study in the creation of such resources by NLP teams who are local to the communities in which these languages are spoken. A parallel text corpus, SALT, was created for five Ugandan languages (Luganda, Runyankole, Acholi, Lugbara and Ateso) and various methods were explored to train and evaluate translation models. The resulting models were found to be effective for practical translation applications, even for those languages with no previous NLP data available, achieving mean BLEU score of 26.2 for translations to English, and 19.9 from English. The SALT dataset and models described are publicly available at
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    Machine Translation For African Languages: Community Creation Of Datasets And Models In Uganda
    (ICLR, 2022-03-30) Akera, Benjamin; Mukiibi, Jonathan; Naggayi, Lydia Sanyu; Nsumba, Solomon; Mwebaze, Ernest; Quinn, John
    Reliable machine translation systems are only available for a small proportion of the world’s languages, the key limitation being a shortage of training and evaluation data. We provide a case study in the creation of such resources by NLP teams who are local to the communities in which these languages are spoken. A parallel text corpus, SALT, was created for five Ugandan languages (Luganda, Runyankole, Acholi, Lugbara and Ateso) and various methods were explored to train and evaluate translation models. The resulting models were found to be effective for practical translation applications, even for those languages with no previous NLP data available, achieving mean BLEU score of 26.2 for translations to English, and 19.9 from English. The SALT dataset and models described are publicly available at https://github.com/SunbirdAI/salt.
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    Ontology Driven Machine learning Approach for Disease Name Extraction from Twitter Messages
    (IEEE, 2017) Mwebaze, Ernest; Nabende, Peter; Magumba, Mark Abraham
    Twitter and social media as a whole has great potential as a source of disease surveillance data however the general messiness of tweets presents several challenges for standard information extraction methods. Current methods for disease surveillance on twitter rely on inflexible keyword based approaches that require messages to be pre-filtered on the basis of a disease name which is supplied a priori and are not capable of detecting new ailments. In this paper we present an ontology based machine learning approach to extract disease names and expressions describing ailments from tweets which may be employed as part of a larger general purpose system for automated disease incidence monitoring. We also propose a simple methodology for automatic detection and correction of errors.
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    Scoring Root Necrosis in Cassava Using Semantic Segmentation
    (arXiv preprint arXiv, 2020) Tusubira, Jeremy Francis; Akera, Benjamin; Nsumba, Solomon; Nakatumba-Nabende, Joyce; Mwebaze, Ernest
    Cassava a major food crop in many parts of Africa, has ma- jorly been a ected by Cassava Brown Streak Disease (CBSD). The dis- ease a ects tuberous roots and presents symptoms that include a yel- low/brown, dry, corky necrosis within the starch-bearing tissues. Cassava breeders currently depend on visual inspection to score necrosis in roots based on a qualitative score which is quite subjective. In this paper we present an approach to automate root necrosis scoring using deep convo- lutional neural networks with semantic segmentation. Our experiments show that the UNet model performs this task with high accuracy achiev- ing a mean Intersection over Union (IoU) of 0.90 on the test set. This method provides a means to use a quantitative measure for necrosis scor- ing on root cross-sections. This is done by segmentation and classifying the necrotized and non-necrotized pixels of cassava root cross-sections without any additional feature engineering.