Browsing by Author "Akera, Benjamin"
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Item A dataset of necrotized cassava root cross-section images(Data in brief, 2020) Nakatumba-Nabende, Joyce; Akera, Benjamin; Tusubira, Jeremy Francis; Nsumba, Solomon; Mwebaze, ErnestCassava 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.Item Keyword Spotter Model for Crop Pest and Disease Monitoring from Community Radio Data(arXiv preprint arXiv, 2019) Akera, Benjamin; Nakatumba-Nabende, Joyce; Mukiibi, Jonathan; Hussein, Ali; Baleeta, Nathan; Ssendiwala, Daniel; Nalwooga, SamiihaIn societies with well developed internet infrastructure, social media is the leading medium of communication for various social issues especially for breaking news situations. In rural Uganda however, public community radio is still a dominant means for news dissemination. Community radio gives audience to the general public especially to individuals living in rural areas, and thus plays an important role in giving a voice to those living in the broadcast area. It is an avenue for participatory communication and a tool relevant in both economic and social development.This is supported by the rise to ubiquity of mobile phones providing access to phone-in or text-in talk shows. In this paper, we describe an approach to analysing the readily available community radio data with machine learning-based speech keyword spotting techniques. We identify the keywords of interest related to agriculture and build models to automatically identify these keywords from audio streams. Our contribution through these techniques is a cost-efficient and effective way to monitor food security concerns particularly in rural areas. Through keyword spotting and radio talk show analysis, issues such as crop diseases, pests, drought and famine can be captured and fed into an early warning system for stakeholders and policy makers.Item 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, JohnReliable 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 atItem Scoring Root Necrosis in Cassava Using Semantic Segmentation(arXiv preprint arXiv, 2020) Tusubira, Jeremy Francis; Akera, Benjamin; Nsumba, Solomon; Nakatumba-Nabende, Joyce; Mwebaze, ErnestCassava 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.