Browsing by Author "Nsumba, Solomon"

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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 cassava whitefly count images
    (Data in Brief, 2022) Nakatumba-Nabende, Joyce; Tusubira, Jeremy Francis; Babirye, Claire; Nsumba, Solomon; Omongo Abu, Christopher
    Whiteflies are insect vectors that affect a variety of plants such as tomatoes, cabbages, sweet potatoes, eggplants, and cassava. In Uganda, whiteflies are a major contributor to the spread of Cassava Brown Streak Disease (CBSD). By suckling on infected cassava plants, whiteflies can potentially transfer the Cassava Brown Streak Virus that causes CBSD to unin- fected clean plants nearby when they migrate. When they attack the cassava plants in large numbers, whiteflies can also cause significant physical damage through suckling. This eventually can lead to leaf loss or plant death. Whiteflies also excrete “honeydew”, which harbors a fungus known as “sooty mold”that covers the leaves, limiting access to sun- light which in turn affects plant food production. As part of their work, the cassava breeders often conduct studies to as- sess the population of whiteflies in cassava fields through a manual process of visual inspection which can be arduous and time-consuming. This paper presents a cassava whitefly dataset that has been curated to enable researchers to build solutions for the automation of the count and detection of whiteflies. The dataset contains 3,0 0 0 images captured in a whitefly trial site in Uganda. It depicts different variations of whitefly infestation from low to high infestation. This data has already been used to provide a proof-of-concept solution for whitefly counting based on Machine Learning approaches.
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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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    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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    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.