Department of Artificial Intelligence

Permanent URI for this collection

https://bdears.busitema.ac.ug/handle/123456789/699

Browse

Browsing Department of Artificial Intelligence by Author "Nuwamanya, Ephraim"

Now showing 1 - 3 of 3
  • Thumbnail Image
    Item
    A labeled spectral dataset with cassava disease occurrences using virus titre determination protocol
    (Elsevier, 2023) Owomugisha, Godliver; Nakatumba-Nabende, Joyce; Dhikusooka, Joshua Jeremy; Taravera, Estefania; Nuwamanya, Ephraim; Mwebaze, Ernest
    In this work, we present a novel dataset composed of spectral data and images of cassava crops with and without diseases. Together with the description of the dataset, we describe the protocol to collect such data in a controlled environment and in an open field where pests are not controlled. Crop disease diagnosis has been done in the past through the analysis of plant images taken with a smartphone camera. However, in some cases, disease symptoms are not visible. Furthermore, for some cassava diseases, once symptoms have manifested on the aerial part of the plant, the root which is the edible part of the plant has been totally destroyed. The goal of collecting this multimodality of the crop disease is early intervention, following the hypothesis that diseased crops without visible symptoms can be detected using spectral information. We collected visible and near-infrared spectra captured from leaves infected with two common cassava diseases namely; Cassava Brown Streak Disease and Cassava Mosaic Disease, as well as from healthy plants. Together, we also captured leaf imagery data that corresponds to the spectral information. In our experiments, biochemical data is collected and taken as the ground truth. Finally, agricultural experts provided a disease score per plant leaf from 1 to 5, 1 representing healthy and 5 severely diseased. The process of disease monitoring and data collection took 19 and 15 consecutive weeks for screen house and open field, respectively, until disease symptoms were visibly seen by the human eye. Keywords: Spectral data protocol, Cassava diseases, Crop diagnosis, Smart agriculture, Early disease detection
  • Thumbnail Image
    Item
    A light spectrometer device for crop disease monitoring
    (ICLR, 2023) Dhikusooka, J Joshua; Nuwamanya, Ephraim; Talavera, Estefania; Owomugisha, Godliver
    Portable devices for the early detection of crop diseases are needed to support the farmers working in the field. Spectrometers showed their potential in the detection of crop diseases. However, high interpretation skills are needed to use the currently available spectrometers. In this project, we propose a portable device that obtains a spectrum wavelength of 700 nanometers describing the information of the crop. The output of this tool is integrated into a smartphone in the form of an app, making it accessible for use in the field in real applications.
  • No Thumbnail Available
    Item
    Early detection of plant diseases using spectral data
    (APPIS, 2020) Owomugisha, Godliver; Nuwamanya, Ephraim; Quinn, A. John; Biehl, Michael; 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.