Public Lecture: Monitoring System to Strive Against Fall Armyworm in Crops - Case Study: Maize in Rwanda

Public Lecture

Monitoring System to Strive Against Fall Armyworm in Crops

Case Study: Maize in Rwanda

By Dr Damien Hanyurwimfura, PhD

Dr Damien Hanyurwimfura, PhD

Lecturer in Computer and Software Engineering, School of ICT

Head of PhD Studies and Research at African Center of Excellence in Internet of Things (ACEIoT)



The food security has received much attention due to hunger that usually affects some countries in Africa. Pests are very dangerous to crops and reduce the harvest of affected crop and can cause risk if no strategically decisions are taken against pests. Fall armyworm (FAW) is one the pest that has attacked the priority stable crop of Rwanda (maize) since last year 2017. This reduced the expected harvest from this crop. The presence of this pest costs the government to use drones and other special forces to strive for it, but pest will remain a problem if there is no regular, automatic way to detect the pest and strive for it at the early days of attacking the crop. This paper introduces a design of a prototype of an automated system that will be able to detect the presence of a fall armyworm in the field. The system will use the new technology of Internet of Things (IoT) where sensors are used to identify the pest location. Once the presence of the pest is detected in the farm, the system will be able to give the information of the affected crop and notify the farmer through his/her mobile phone who will immediately react accordingly. Through the authorization from the farmer, the system will pump pesticides to kill both larvae and eggs of fall armyworm on the affected crop. This automated system will save the farmer's time, as it will monitor the crops while the farmer is doing other activities. Lastly the system will help to increase the production of maize in Rwanda since the crops will be safe from the pest.


Keywords-Food security; fall armyworm; Pest detection; Internet of things; sensors, maize, pest

Accepted paper to be published in IEEE Smart World Congress 2018

Damien Hanyurwimfura received his Bachelor degree of Engineering in Computer Engineering and Information Technology from University of Rwanda (Former KIST) in 2005. He obtained his Master degree of Engineering in Computer Science and Technology and his Ph.D. degree in Computer Science and Technology from Hunan University, China in 2010 and 2015 respectively. He worked at University of Rwanda (Former KIST) since 2005 as a Tutorial Assistant before joining Hunan University for master degree studies in 2008. He is currently a Lecturer at the University of Rwanda, College of Science and Technology. In 2017, He has been appointed as the Head of PhD Studies and Research and the African Center of Excellence in Internet of Things (ACEIoT)

He participated in Postdoctoral Researcher Networking in tour 2018 Artificial Intelligence field that was held in Germany.

 His research interests include most aspects of data mining, information security, intrusion detection and vulnerability analysis, Intelligent Transportation, Internet of things etc. He has authored more than twenty articles and papers in leading, peer reviewed; high indexed international journals and conferences.


Time & Venue: Wednesday 24th October, 2018

@ Kist II Building, 3PM-4PM

ResearcherID: U-5578-2017 Scopus Author ID: 36442253000

H-index in Scopus: 4

H-Index in Google scholar: 6

Damien Hanyurwimfura

+2507 8739 4447




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