Wireless Sensor Networks in IPM

Introduction

The global burden imposed by pests is manifold. Invading plant pests and pathogens present a constantly increasing risk for agriculture brought on by current environmental shifts (Savary et al., 2019; Spence et al., 2020). Furthermore, disease carrying pest species, i.e. vectors, represent one of the biggest global health issues today, as well as historically, and are only exasperated by the introduction of invasive vector species to new areas through different mechanisms of climate change (Campbell-Lendrum et al., 2015). Vector borne disease (VBD) include diseases such as zika, dengue, malaria, yellow fever, chikungunya, leishmaniasis and trypanosomiasis (National Academies of Sciences & Medicine, 2016). These VBDs have accounted for more human death through the last two centuries than all other causes combined (Gubler, 1998; National Academies of Sciences & Medicine, 2016). Over 500 million people are infected by Vector Borne Disease (VBD) every year. WHO estimates that, annually, over 3 billion people are at risk of contracting a VBD, of which a large proportion is diseases transmitted by mosquito vectors. More than 2.5 billion people are at risk of contracting Dengue alone, and Malaria causes over 500,000 deaths every year.

The implementation of pest-control strategies in the mid-20th century to reduce mosquitoes populations succeeded in the localized reduction of these VBDs (National Academies of Sciences & Medicine, 2016). Due to the ethical implication and environmental damage caused by most of the firs-used techniques, a shift in pest control towards more environmentally sound practices is soon observed. This movement and the collection of practices it entails is labelled as Integrated Pest Management (IPM).

Ever since the inception of integrated management in 1959 by Stern et al. (Stern et al., 1959), Integrated Pest Management (IPM) has undergone quite a change and evolved into a large concept covering different fields (Peterson et al., 2018). This evolution also brought in several ethical concerns regarding the direction the concept was going. In his Integrated Control Concept, Stern (Stern et al., 1959) speaks about using chemical and biological control to supplement each other and not look at them as alternatives. Although this core concept hasn’t changed, the implementation and technologies have changed quite a bit. Resistance against pesticides was one of the main driving factors behind the conceptualization of integrated management which is still a driving factor today. There are limitations to IPM both in the practical and the ethical sense (Peterson et al., 2018). When it comes to medical pest, completely killing the pest is usually still the preferred way when it comes to public opinion and tolerance. There are several constraints to developing tolerant crops and other hosts. These include identifying tolerance, characterizing tolerance mechanisms, and understanding the genetics underlying tolerance (Delaney & Macedo, 2000; Velusamy & Heinrichs, 1986). Tolerance can be interpreted both as a type of resistance or as the factor for economic injury levels (Pedigo & Rice, 2014; Peterson et al., 2018). The topics of sampling and economic thresholds are closely allied to the focus on management, host stress, and the proper use of tactics (Kogan, 1988, 1998).

Wireless Sensor Network technology is already widely used in a plethora of scientific and commercial applications, and this number will only increase with the expected rise of the IoT market. An emerging field for environmental WSN systems is autonomous vector surveillance. Sensor technology can be used to shoulder ecologically friendly practices within the Integrated Pest Management (IPM) approach. Proper surveillance and subsequent modelling of the impact that pest and disease have on human health and crop agriculture is a pressing issue in numerous segments. In-situ, environmental WSN data support these practices by defining areas and periods of increased risk for the pest as well as providing valuable input to pest distribution and pest population dynamics models. IoT networks can be combined with earth observation (EO) data and expert knowledge to generate information regarding the pest activity levels, current environmental suitability, risk and expected dynamics.