College of Science and Technology(CST) | Research portal

AI and IoT-powered edge device optimized for crop pest and disease detection.

Modeling and Testing of a Phasor Measurement Unit Under Normal and Abnormal Conditions Using Real-Time Simulator

Comparative study of machine learning models in predicting crop yield.

Evaluation of advanced Kalman filter on real-time agricultural soil parameters through an IoT resources-constrained device.

AI and IoT-powered edge device optimized for crop pest and disease detection.

From animal waste to energy: Exploring the effects of household livelihoods on biogas technology use in Rwanda

Evaluation of advanced Kalman filter on real-time agricultural soil parameters through an IoT resources-constrained device.

AI and IoT-powered edge device optimized for crop pest and disease detection.

From animal waste to energy: Exploring the effects of household livelihoods on biogas technology use in Rwanda

eHealth Literacy among Adult Dental Patients Attending the University of Rwanda Polyclinic

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