Research Article Open Access

Automated Fall Armyworm (Spodoptera frugiperda, J.E. Smith) Pheromone Trap Based on Machine Learning

Simon H. Chiwamba1, Jackson Phiri1, Philip O.Y. Nkunika1, Claytone Sikasote1, Monde M. Kabemba1 and Miyanda N. Moonga1
  • 1 University of Zambia, Zambia
Journal of Computer Science
Volume 15 No. 12, 2019, 1759-1779

DOI: https://doi.org/10.3844/jcssp.2019.1759.1779

Submitted On: 17 September 2019 Published On: 19 December 2019

How to Cite: Chiwamba, S. H., Phiri, J., Nkunika, P. O., Sikasote, C., Kabemba, M. M. & Moonga, M. N. (2019). Automated Fall Armyworm (Spodoptera frugiperda, J.E. Smith) Pheromone Trap Based on Machine Learning. Journal of Computer Science, 15(12), 1759-1779. https://doi.org/10.3844/jcssp.2019.1759.1779

Abstract

Maize is the main food crop that meets the nutritional needs of both humans and livestock in the sub-Saharan African region. Maize crop has in the recent past been threatened by the fall armyworm (Spodoptera frugiperda, J.E Smith) which has caused considerable maize yield losses in the region. Controlling this pest requires knowledge on the time, location and extent of infestation. In addition, the insect pest’s abundance and environmental conditions should be predicted as early as possible for integrated pest management to be effective. Consequently, a fall armyworm pheromone trap was deployed as a monitoring tool in the present study. The trap inspection is currently carried out manually every week. The purpose of this paper is to bring automation to the trap. We modify the trap and integrate Internet of Things technologies which include a Raspberry Pi 3 Model B+ micro-computer, Atmel 8-bit AVR microcontroller, 3G cellular modem and various sensors powered with an off-grid solar photovoltaic system to capture real-time fall armyworm moth images, environmental conditions and provide real-time indications of the pest occurrences. The environmental conditions include Geographical Positioning System coordinates, temperature, humidity, wind speed and direction. The captured images together with environmental conditions are uploaded to the cloud server where the image is classified instantly using Google’s pre-trained InceptionV3 Machine Learning model. Intended users view captured data including prediction accuracy via a web application. Once this smart technology is adopted, the labour-intensive task of monitoring will reduce while stakeholders shall be provided with a near real-time insight into the FAW situation in the field therefore enabling pro-activeness in their management of such a devastating pest.

  • 652 Views
  • 493 Downloads
  • 0 Citations

Download

Keywords

  • Internet of Things
  • Integrated Pest Management
  • Fall Armyworm
  • Raspberry Pi
  • Machine Learning