Browsing by Author "Muraya, M"

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    Features Selection in Statistical Classification of High Dimensional Image Derived Maize (Zea Mays L.) Phenomic Data
    (Science and Education Publishing, 2022) Gachoki, P.; Muraya, M; Njoroge, G
    Phenotyping has advanced with the application of high throughput phenotyping techniques such automated imaging. This has led to derivation of large quantities of high dimensional phenotypic data that could not have been achieved using manual phenotyping in a single run. Hence, the need for parallel development of statistical techniques that can appropriately handle such large and/or high dimensional data set. Moreover, there is need to come up with a statistical criteria for selecting the best image derived phenotypic features that can be used as best predictors in modelling plant growth. Information on such criteria is limited. The objective of this study is to apply feature importance, feature selection with Shapley values and LASSO regression techniques to find the subset of features with the highest predictive power for subsequent use in modelling maize plant growth using high- dimensional image derived phenotypic data. The study compared the statistical power of these features extraction methods by fitting an XGBoost model using the best features from each selection method. The image derived phenomic data was obtained from Leibniz Institute of Plant Genetics and Crop Plant Research, -Gatersleben, Germany. Data analysis was performed using R-statistical software. The data was subjected to data imputation using 𝑘 Nearest Neighbours technique. Features extraction was performed using feature importance, Shapley values and LASSO regression. The Shapley values extracted 25 phenotypic features, feature importance extracted 31 features and LASSO regression extracted 12 features. Of the three techniques, the feature importance criterion emerged the best feature selection technique, followed by Shapley values and LASSO regression, respectively. The study demonstrated the potential of using feature importance as a selection technique in reduction of input variables in of high dimensional growth data set.
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    PREVALENCE AND DISTRIBUTION OF PARASITIC ROOT KNOT NEMATODES IN SWEET POTATO FARMS OF KIRINYAGA COUNTY
    (Chuka university, 2022) Onchari, N.M; Githae, E. W; Nyabuga, I; Muraya, M
    Sweet potato production is constrained by many biotic factors which include parasitic root knot nematodes. Root knot nematodes (RKN) pose a significant threat to a wide range of agricultural crops. The effect of RKN on sweet potatoes include reduced yields and poor quality of the tubers, high costs of production and hence loss of income. Moreover, development of resistance by RKN has partly rendered various pest management strategies ineffective, therefore risking food security. It is likely more losses may be experienced in future due to ongoing withdrawal of nematocides from the market. Information on distribution and management of root knot nematodes is limited. This study aimed in the isolation and characterization of root knot nematodes from soils and root tubers of sweet potato farms in different agro ecological zones of Kirinyaga County. From the undertaken study, prevalence and distribution of root knot nematodes was analyzed based on early cropping of sweet potatoes between one to two months and post harvested farms. Across all sweet potato farms, identification through microscopy revealed parasitic RKNs that were Meloidogyne species, Pratylenchus species, Trichodorus species among other spiral nematodes (Helicotyllenchus species and Scutellonema species) that are also categorized as parasitic nematodes. Reniformis (Rotylenchus species) were also identified as well as predatory nematodes which were singled out too under microscope observations. Root tubers that were stained pink with phloxine B showed large galls with mature female root knot nematodes under microscopy. Based on the questionnaire answers from farmers, they were familiar with nematode symptoms on sweet potatoes however, awareness of nematodes was low.