Department of Artificial Intelligence

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

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Browsing Department of Artificial Intelligence by Author "Quinn, A. John"

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    A low-cost 3-D printed smartphone add-on spectrometer for diagnosis of crop diseases in field
    (Association for Computing Machinery, 2020) Owomugisha, Godliver; Mugagga, K. B. Pius; Melchert, Friedrich; Mwebaze, Ernest; Quinn, A. John; Biehl, Michael
    We present our initial proof of concept study towards the development of a low-cost 3-D printed smartphone add-on spectrometer. The study aimed at developing a cheap technology (less than 5USD) to be used for detection of crop diseases in the field using spectrometry. Previously, we experimented with the problem of disease diagnosis using an off-the-shelf and expensive spectrometer (approximately 1000 USD). However, in real world practice, this off-the-shelf device cannot be used by typical users (smallholder farmers). Therefore, the study presents a tool that is cheap and user friendly. We present preliminary results and identify requirements for a future version aiming at an accurate diagnostic technology to be used in the field before disease symptoms are visibly seen by the naked eye. Evaluation shows performance of the tool is better than random however below performance of an industry grade spectrometer. CCS CONCEPTS • Applied computing → Physical sciences and engineering; • Computing methodologies → Machine learning. KEYWORDS Low-cost, Spectrometery, Crop disease, Diagnosis, 3-D printed, Smartphone
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    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.
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    Matrix Relevance Learning From Spectral Data for Diagnosing Cassava Diseases
    (IEEE, 2021) Owomugisha, Godliver; Melchert, Friedrich; Mwebaze, Ernest; Quinn, A. John; Biehl, Michael
    We discuss the use of matrix relevance learning, a popular extension to prototype learning algorithms, applied to a three-class classification task of diagnosing cassava diseases from spectral data. Previously this diagnosis has been done using plant image data taken with a smartphone. However for this method disease symptoms need to be visible. Unfortunately 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. This research is premised on the hypothesis that diseased crops without visible symptoms can be detected using spectral information, allowing for early interventions. In this paper, we analyze visible and near-infrared spectra captured from leaves infected with two common cassava diseases (cassava brown streak disease and cassava mosaic virus disease) found in Sub-Saharan Africa. We also take spectra from leaves of healthy plants. The spectral data come with thousands of dimensions, therefore different wavelengths are analyzed in order to identify the most relevant spectral bands for diagnosing these disease. To cope with the nominally high number of input dimensions of data, functional decomposition of the spectra is applied. The classification task is addressed using Generalized Matrix Relevance Learning Vector Quantization and compared with the standard classification techniques performed in the space of expansion coefficients. INDEX TERMS Cassava disease diagnosis, feature selection, matrix relevance learning, spectral data.