School of Agriculture and Biotechnology
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Item Evaluation of drought tolerance in mutant Kenyan bread wheat (Triticum aestivum L.) using in vitro techniques(INNSPUB, 2016-08-31) Githinji, Gerald Gikonyo; Kinyua, Miriam; Kiplagat, Oliver; Birithia, RaelWheat (Triticum aestivum L) is widely cultivated as a small-grain cereal. In Kenya, it is ranked second after maize in its contribution towards food security. Biotic stress conditions such as drought cause extensive losses to agricultural production worldwide. In Kenya, arid and semiarid lands represent 83% of total land area, which experience frequent crop failure due to drought stress. Developing drought-tolerant wheat genotypes has been the focus of many wheat improvement programs. Few drought tolerant varieties are available for commercial production in Kenya. Hence, there is need to develop more drought tolerant wheat varieties. The objective of this study was to screen for drought resistance in two mutant wheat lines in vitro using Polyethylene Glycol (PEG). Four wheat germplasm were tested for drought tolerance using -3.0, -9.0 and -15.0 PEG-6000 concentrations and the data was recorded on various seedling parameters including root length, shoot length and root length /shoot length ratio. The experiment was carried out in three replicates using completely randomized design. Data was subjected to analysis of variance (ANOVA) using GENSTAT 12th edition. Correlation was done by Pearson Correlation Coefficients to determine significant associations among the different variables. Results indicated that there was a significant difference (p=0.05) between Mutant 1 and Mutant 2 having longer roots, shoots and a root to shoot ratio compared to Chozi and Duma in the different PEG concentrations used. Hence, the two mutant lines are possible candidates for varieties that can be grown in ASALs regions in Kenya.Item Identification of Maize Leaf Diseases Based On AlexNet and ResNet50 Convolutional Neural Networks(2023-08-30) Micheni, Maurice; Birithia, Rael; Mugambi, Cyrus; Too, Boaz; Kinyua, Margaret KMaize crop protection is crucial for global food security, requiring accurate disease identification. In Kenya, farmers rely on subjective visual analysis of symptomatic leaves, which is time-consuming and prone to errors. Computer vision technologies, like deep learning and machine learning, offer promising solutions for disease identification. This study applies Convolutional Neural Networks (CNNs), specifically AlexNet and ResNet-50, to automatically learn image features and enhance speed and accuracy in maize leaf disease identification. A dataset of 3200 digital maize leaf disease images from Embu County is used for training and testing. AlexNet achieved the highest average accuracy of 98.3%, followed by ResNet-50 at 96.6%. The machine learning, support vector machine (SVM) exhibited the lowest average accuracy of 85.5%. These findings highlight the significance of utilizing AlexNet and ResNet-50 in maize leaf disease identification and classification.