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

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+ microcomputer , 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.

Invasiveness, biology, ecology, and management of the fall armyworm, Spodoptera frugiperda

“…Other factors such as weather, crop habitat, and proximity to trees also affect trap captures (Tingle & Mitchell 1979;Koffi et al 2021a). The use of artificial intelligence as a component of FAW pheromone trapping systems was illustrated by Chiwamba et al (2019), who used automated traps to provide early warning and near real-time information.…”

IoT Solutions with Artificial Intelligence Technologies for Precision Agriculture: Definitions, Applications, Challenges, and Opportunities

“…Many studies have investigated other aspects of crop production, including disease prediction [101,105,118,123], yield prediction [124], pest control [72,125], and crop quality assessment/improvement [126]. However, there remain research gaps for future studies.…”

Novel Technologies and Their Application for Protected Area Management: A Supporting Approach in Biodiversity Monitoring

“…CNN in the context of CT research can be applied to identify any properly annotated object, from animals in PAs to agricultural pest insects [72][73][74]. Interconnectivity of hardware with cloud-based software is poised to empower realtime remote data collection in agriculture [75]. A parallel approach could be applied to state-of-the-art CT systems in PAs to provide real-time monitoring of animals or vegetation [76].…”