Browsing by Author "Quinn, John A."

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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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    Increased-specificity famine prediction using satellite observation data
    (First ACM Symposium on Computing for Development, 2010) Quinn, John A.; Okori, Washington; Gidudu, Anthony
    This paper examines the use of remote sensing satellite data to predict food shortages among di erent categories of house- holds in famine-prone areas. Normalized Di erence Vegeta- tion Index (NDVI) and rainfall estimate data, which can be derived from multi-spectral satellite radiometer images, has long been used to predict crop yields and hence famine. This gives an overall prediction of food insecurity in an area, though in a heterogeneous population it does not directly predict which sectors of society or households are most at risk. In this work we use information on 3094 households across Uganda collected between 2004-2005. We describe a method for clustering households in such a way that the cluster de- cision boundaries are both relevant for improved-speci city famine prediction and are easily communicated. We then give classi cation results for predicting food security status at a household level given di erent combinations of satel- lite data, demographic data, and household category indices found by our clustering method. The food security classi - cation performance of this model demonstrates the potential of this approach for making predictions of famine for speci c areas and demographic groups.