Browsing by Author "Gichuki, Dennis Karugu"

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    Assessing Preparedness for Smart Farming and Technology Adoption among Kenyan Farmers
    (International Journal of Research and Innovation in Applied Science, 2024-09-16) Gichuki, Dennis Karugu; Mbuguah, Samuel Mungai; Owoche, Patrick Oduor; Oyile, Paul Oduor
    Agriculture is crucial in reducing poverty, promoting economic prosperity, and ensuring food security for the world’s growing population, which is expected to reach 9.7 billion by 2050. This sector is vital to the global economy, contributing significantly to GDP and providing jobs for a large workforce. Precision agriculture and e-commerce advances have proven beneficial, boosting crop yields and rural incomes. Sub-Saharan Africa faces similar agricultural challenges as it anticipates a population of 2.1 billion by 2050. Although the region has made strides in expanding farmland and labour, improvements in crop yields have been limited. The digital revolution offers new opportunities to tackle issues such as undernutrition by improving access to information and technology. In Kenya, with a population projected to reach 95 million by 2050, expanding food production is a pressing challenge. Significant hurdles include declining soil fertility, inadequate water management, and a lack of technical support. While technologies like Wireless Sensor Networks (WSNs) and Machine Learning (ML) have the potential to enhance agricultural productivity, their adoption is constrained by infrastructure issues, high costs, and a shortage of technical expertise. Addressing these barriers and improving farmer education is essential to fully realising these technologies' benefits.
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    Performance Evaluation of Machine Learning Algorithms in Smart Agriculture
    (International Journal of Advanced Research in Computer and Communication Engineering, 2024-08-26) Gichuki, Dennis Karugu; Owoche, Patrick Oduor; Mbuguah, Samuel Mungai
    This study explores the integration of Wireless Sensor Networks (WSN) and Machine Learning (ML) in smart farming to address critical agricultural challenges. By leveraging real-time data collection and advanced analytical tools, the research demonstrates the potential of ML algorithms—Decision Trees, Naive Bayes, Support Vector Machines (SVM), Logistic Regression, and Random Forests—in enhancing crop management, including yield prediction, soil quality assessment, and pest and disease detection. The study finds that Naive Bayes achieves the highest accuracy and balanced precision-recall metrics, while ensemble methods like Random Forests effectively reduce overfitting and improve prediction accuracy. Despite the promising results, the research identifies challenges such as data accessibility, model integration, and user interface design that must be addressed to fully realize the potential of smart farming technologies. Overall, the findings provide valuable insights into optimizing resource utilization, reducing crop losses, and promoting sustainable farming practices, thereby supporting global food security and economic stability.