School of Pure and Applied Sciences
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Item Identification of Maize leaf diseases based on Support Vector Macina and Convolutional Neural Networks Alex Net and ResNet(Karatina University, 2023) Murimi, Micheni MauriceProtecting maize crops from devastating plant diseases ensures global food security. Accurate disease identification is essential for implementing effective control measures. However, traditional visual analysis of symptomatic leaves used by maize farmers in Kenya is time consuming, costly, subjective and prone to errors. Embracing computer vision technologies, such as deep learning and machine learning, offers promising solutions to these challenges, enhancing crop productivity. The general objective of this study was to develop models for maize lethal necrosis (MLN) disease, maize streak disease (MSD) and Gray leaf spot diseases (GLS) detection and classification using AlexNet and ResNet 50 convolutional neural networks (CNN) architectures and machine learning Support Vector Machine (SVM). The specific objectives of this study were to: identify maize leaf disease (MLN, MSD and GLS) using AlexNet, ResNet-0 and SVM models, to evaluate the performance of the AlexNet, ResNet-50 and SVM models in the classification of MLN, MSD and GLS. Digital maize leaf disease images were collected from maize farms in Embu County, resulting in a dataset of 3200 images, with 800 images for each disease category. The results indicate that AlexNet and ResNet50 achieved high accuracy in identifying maize leaf diseases, recording average accuracies of 98.3% and 96.6%, respectively. In contrast, the SVM model exhibited the lowest average accuracy of 85.5%. AlexNet demonstrated exceptional accuracy in classifying Maize Streak Virus (MSV) with a rate of 99.85%, followed by ResNet50 at 99.2%. Conversely, SVM had a lower recall value of 81.7% for Grey Leaf Spot disease. By incorporating these advanced models, farmers and stakeholders in maize crop protection can identify diseases early, allowing for timely interventions and improved disease management strategies. Consequently, this will lead to increased maize productivity and enhanced crop quality. Early disease detection also facilitates the judicious use of pesticides, safeguarding the environment and human health. The findings underscore the importance of leveraging these technologies to enhance food security, optimize agricultural practices, and promote sustainable maize production.Item IDENTIFICATION OF MAIZE LEAF DISEASES USING SUPPORT VECTOR MACHINE AND CONVOLUTIONAL NEURAL NETWORKS ALEXNET AND RESNET50(KARATINA UNIVERSITY, 2023-11) Murimi, Micheni MauriceItem UTILIZATION OF MOBILE DEVICES IN ACCESSING INFORMATION BY LECTURERS AND STUDENTS IN PUBLIC UNIVERSITIES IN KENYA(Karatina University, 2023-11) Burudi, Peter ShibonjeThe application of mobile devices is essential in the dissemination of information. In institutions of higher learning, apart from providing convenience, mobile devices open up new avenues for academic libraries to enhance access to information. However, more studies need to be carried out that directly look at the use of mobile devices in enhancing access and use of information. This study aimed at assessing the utilization of mobile devices in libraries in public universities in Kenya. The objectives that guided the study were: to identify various mobile devices available in the libraries; to determine the different ways in which mobile devices are utilized; to examine the benefits of mobile device utilization; to evaluate the challenges faced in the utilization of mobile devices, and to determine viable ways of enhancing utilization of mobile devices in public university libraries in Kenya. The study was guided by the Technology Acceptance Model. The study adopted the descriptive research design. The study targeted 1620 students, 57 teaching staff from three academic departments, 91 library staff, and 38 ICT staff from KU and UoN universities. The study sample size was determined using 10% of the target hence 162 students and six teaching staff were sampled using stratified random sampling while nine library staff and four ICT staff were sampled using purposive sampling. Questionnaires and document analysis were used to collect both primary and secondary data. Descriptive (frequency, percentage, and mean) and inferential statistics (Chi-Square test and Fisher’s test) were used in analyzing data. The Statistical Package for Social Sciences (SPSS, ver. 28) was used for data analysis. The study found that the majority of university students access libraries via mobile devices and that they were mostly used for accessing e-resources, and online searches for educational materials. The study found a strong correlation between the use of mobile devices and ease of access to library resources, exposure to diverse content, convenience of utilization of study materials, and interactive usability of study materials. The study established the shortage of power outlets for charging mobile devices, lack of technical assistance, and inadequate internet access were some of the challenges faced in the utilization of mobile devices in public university libraries. The study concluded that using mobile devices in university libraries benefits users significantly and relieves pressure on more traditional library services. The findings of the study will be useful to policymakers and library managers in improving access to information in libraries.Item PREVALENCE OF GASTRO INTESTINAL PARASITES OF CATTLE IN MATHIRA CONSTITUENCY, KENYA(Karatina University, 2021-03) NYUTU, CAROLYNE WAMBUICattle’s farming is a crucial activity for Mathira constituency; since it acts as a source of livelihood to many people. However, gastrointestinal parasitic infection is a limiting factor in cattle management. Understanding the epidemiological characteristics of the infections is necessary to recommend control and preventive measures. There is however inadequate knowledge regarding the prevalence of gastrointestinal tract parasite infection of cattle in the study area. The current study was to assess the prevalence of gastrointestinal parasites of cattle in Mathira constituency. The specific objectives included determination of the association of farmers' knowledge and prevalence of gastrointestinal parasites, the association of farming practice and prevalence of gastrointestinal parasites, the combined association between prevalence of gastrointestinal parasites and farmer’s knowledge and farming practice. A total of 387 faecal samples were collected and subjected to parasitological analysis: modified McMaster technique was used to determine the number of Eggs per Gram (EPG); Willis technique to identify any stages for nematodes and cestodes; sedimentation method for trematodes identification and; direct smear to identify any stages for protozoans. Point prevalence was used to determine the prevalence of gastrointestinal parasites among cattle. The association between the prevalence of gastro-intestinal parasite and farming practice or farmers' knowledge was tested statistically using the Chi-square test of independence. Binary logistic regression analysis was used to determine the relationship between dependent and independent variables while data obtained from the farm and laboratory were analysed using SPSS version 21 software. The risk factors (farming practice and farmers' knowledge) associated with the prevalence of intestinal parasite infection were drawn from the analysis of the questionnaires that were administered during faecal collection. The overall prevalence of parasitic infection was 69.4%. The percentage prevalence by gender shows that females (67%) had relatively high percentage prevalence compare to males (64%). Percentage prevalence on breed Ayrshire (70%) had a relatively high percentage prevalence compared to Guernsey (60%). The percentage prevalence by ward was highest in Kirimukuyu (86%) and lowest in Iriaini (44%). Cattle of age 1-2 (69%), had relatively high percentage prevalence compared to age 3-4 years (55%). It was equally observed that the intensity of infection of cattle was generally very low. Most of the cattle (64.3%) had between 0-200 eggs per gram (epg). The gastrointestinal parasites identified in the study were Schistosoma 12.14%, Strongyloides 4.39%, Fasciola 5.43%, Entomoeba 7.49%, Giardia 2.58%, Nematodirus 5.68%, Trichuris 2.33%, Toxocara 1.55%, Eimeria 9.82%, and Taenia 2.33%. Risk factors (farmers' knowledge and farming practice) were significantly associated with the prevalence of gastrointestinal parasites. To manage gastrointestinal parasites and improve cattle farming veterinary services such as regular mass deworming, frequent diagnosis for infection and training farmers on control and prevention of infection are recommended.Collection Collection Collection