MSIT Theses and Dissertations
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Find here Theses and Dissertations from for the award of Master of Science in Information Technology (MSIT). These works have been scanned and passed through the OCR. We do not hold liablity for correctness of content.
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Browsing MSIT Theses and Dissertations by Subject "Agriculture"
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- ItemA Block Chain Technology to enhance food traceability and safety: case study of agriculture industry in Kenya(Strathmore University, 2020) Lenjula, Letitoiya LesiitFood safety has been one of the growing concerns and challenges in African countries. We had reports of sub standards food stuff, including chemically made eggs being sold to un-suspecting citizens. This is a global challenge, however, it is worth mentioning that it has been a serious issue in Africa due to rampant corruption where the oversight institution are easily compromised by unscrupulous business entities mainly because of lack of tracking system that enable the public know who made decisions or certain approvals. In South Africa, Listeria Outbreak caused 203 fatalities by the time the outbreak was declared over in September 2018. Closer home, Kenya has seen a fair share of food scandal starting with the infamous Aflatoxin-contaminated maize in Kenya resulted in 317 cases of hepatic failure and 125 deaths. There is lack of a system that would ensure companies and individuals involved in production of agricultural produce keep highest level of ethics; through a transparent supply chain management system that not only give the policy makers and oversight organization openness but also neutrality, reliability and thus security of the produce from the farm to table. This projects main objective is to develop a system that will guarantee food quality and safety from supply chain perspective by applying block chain technology in agricultural supply-chain management, from farm to table. Since its conception in 2008, block chain has developed over the last decade into one of today’s biggest technologies with a massive potential to impact virtually every industry from financial to manufacturing to educational institutions. Block chain provided the answer to digital trust because it records important information in a public space and doesn’t allow anyone to remove it; it’s transparent, time-stamped and decentralized. The system developed enhances food safety and integrity through higher traceability thus helping everybody stakeholder in the supply chain quickly trace outbreaks back to specific sources, which could mitigate food fraud or food crises. The stakeholder are not only able to get quality and safer products but also forces the dishonest business entities out of business therefore making the market safe. The system offers many other benefits tandem to the block chain attributes such as, providing a secure way to perform transactions among untrusted parties; decentralizing ledger that helps in connecting inputs, suppliers, producers and buyers.
- ItemA Computer vision-based model for crop yield prediction using remote sensing data(Strathmore University, 2021) Kiragu, Daniel MburuArguably, crop yield data forms the most important measure of crop productivity in agriculture. With adequate crop yield data, local and international bodies can develop effective agricultural policy leading up to sustainable food supplies and elevated food security. However, timely acquisition of crop yield data can be a cumbersome task as existing crop yield prediction approaches face numerous challenges. In this study, these challenges are identified as high cost and high dimensionality of data required for the prediction activities as well as limited scaling of the resultant prediction models. In efforts of overcoming these challenges, this study leveraged an alternative source of data to design and develop a cheap, accurate and scalable deep learning model using convolutional neural networks. Satellite imagery datasets were used as the primary and only source of data for training the model. This benefited the study in two major ways. Firstly, off, the approach automatically took care of the high dimensionality problem as demonstrated in the GEMS data. Second, satellite imagery data is readily available globally, a factor that greatly reduced the costs needed to collect real-time data for the study. Validation of the developed model was done using 10% of the overall dataset acquired. Reliability of the model in performing crop yield predictions was captured using an MSE loss function for each epoch trained. Cumulatively, the model achieved an MSE loss score of 3.6.
- ItemA generic mobile agriculture architecture: a case study of dairy farming(Strathmore University, 2011) Gichamba, Amos NjihiaThe high penetration of mobile phones in the Kenyan market has given lead to the opportunity to use mobile devices for economic activities such as agriculture. With Kenya geared towards the accomplishment of Vision 2030, agriculture has been identified as one of the supporting component of the social and economic pillars of Vision 2030. This means that the success of agriculture in Kenya will accelerate the realization of Vision 2030. One of the key agricultural sectors in Kenya is dairy farming . Even though this sector contributes highly to the country's GDP, it is faced by immense challenges including poor market information, lack of collaboration platforms for the various stakeholders, lack of easy access of information and manual processes in produce data collection and access. The aim of this paper was to determine the extent of usage of M-Agriculture applications in Kenya, to determine the design requirements for an M-Agriculture architecture and to propose an M-Agriculture architecture. The paper also highlighted on the development of an M-Agriculture application suite. The findings of the study suggest that there are present implementations of mobile agriculture in developed countries while developing countries like Kenya have only a few cases . However, the study established that the usage of m-agriculture applications has great potential in enhancing business processes among various stakeholders of the dairy industry. The result of the study is the design and implementation of a mobile agriculture architecture that is applicable in dairy farming. This research provides a comprehensive approach on how to develop systems using mobile technology that integrate with existing systems. Future researchers can use this architecture as a starting point in developing mobile agriculture frameworks and models , while system developers in the area of mobile agriculture can be able to use this architecture to develop mobile systems in agriculture.
- ItemPortal to address agricultural information needs of banana farmers in Embu County(Strathmore University, 2013) Ireri, Evelyn WanjiruInformation and Communication Technologies (ICTs) are important tools to use in agriculture today. This is more so because ICTs can make significant contribution in increasing efficiency, productivity and sustainabiIity of smallscale farmers. The greatest challenge is that despite many initiatives Agricultural knowledge and information has not been tapped and content availed to farmers in an all inclusive format. Currently the available websites only provide information which is of very little value to the farmer since it does not cover the whole agricultural value chain. The design of this research was action in that it tried to understand the banana farmers' agricultural information needs and challenges that farmers face while using ICTs, interviews were conducted to banana farmers in Embu county. This information was intended to aid the researcher in developing an appropriate tool (portal) which could contain all the information a farmer is interested in throughout the agricultural value chain. The developed portal contains information from production, to value addition and marketing which is the final stage of the agricultural value chain. However, the research recommends that the agricultural extension offices should be fully equipped with computers and internet to facilitate the extension officers to be able to train the farmers on how to acquire this information through the internet using their phones or available information hubs. Findings also suggest the need for policy makers and even private sector to invest in making access to ICT tools by farmers easier.
- ItemVision-based model for maize leaf disease identification : a case study in Nyeri County(Strathmore University, 2016) Maina, Christine NjeriBiotic stress which includes pest and diseases affect crop productivity due to either death of affected crops or reduced yield per crop. Abiotic stress such as water and temperature also contribute to lower yields. Maize is Kenya’s staple food with most households having limited choices of other foodstuffs thus increasing their reliance on maize. Diseases affecting maize in Kenya include: Maize Grey Leaf Spot disease, Maize stem borer, Maize Lethal Necrosis Disease, Ear Rot, Stem Borers, and Maize Streak Virus. Currently, the human visual examination is the most commonly used method for classifying diseases. The method gives room for a lot of errors as the diagnosis is based on the experience of the farmer or the extension worker. The method also takes a great deal of effort and time to identify crop diseases based on the visually observable characteristics. Different experts diagnose the same disease as a different disease due to their varied experiences leading to erroneous identification of diseases. Introduction of artificial intelligence in various aspects of agriculture has gained momentum in today’s world. Artificial intelligence has seen its application in predicting soil organic matter based on remote sensing data as well as in prediction of crop yield based on factors of production and in identification of crop diseases. The research sought to propose use of an artificial intelligence model for identification of maize leaf diseases. In the proposed model, images of maize leaves were acquired and extracted color features used to identify the specific disease. Artificial Neural Network was used to identify the disease by implementing a back propagation learning algorithm. The data obtained was segmented into training and test data for the model. The algorithm was preferred due to its strengths in adaptive learning, its fast processing speed and the accuracy of its output. The performance evaluation of the model was based on the accuracy of the classification, the precision, recall ratio and the F- Measure. The model was proven to be significantly accurate with an accuracy of 78.94 % while the precision obtained was 0.778. The recall ratio from the neural network was 1 and an F-measure of 0.875.