Plant pest surveillance: from satellites to molecules

Abstract

Plant pests and diseases impact both food security and natural ecosystems, and the impact has been accelerated in recent years due to several confounding factors. The globalisation of trade has moved pests out of natural ranges, creating damaging epidemics in new regions. Climate change has extended the range of pests and the pathogens they vector. Resistance to agrochemicals has made pathogens, pests, and weeds more difficult to control. Early detection is critical to achieve effective control, both from a biosecurity as well as an endemic pest perspective. Molecular diagnostics has revolutionised our ability to identify pests and diseases over the past two decades, but more recent technological innovations are enabling us to achieve better pest surveillance. In this review, we will explore the different technologies that are enabling this advancing capability and discuss the drivers that will shape its future deployment.

Keywords: biosecurity; diagnostics; phytopathology; surveillance.

Figure 1.. Smart phone applications facilitate wider access to diagnostic tools.

Example of the application of the PlantVillage Nuru App for identification of (A) cassava green mites and (B) Cassava mosaic disease (CMD) in cassava leaves.

Figure 2.. Miniaturised sensors enable in-field detection.

(A) A smartphone-based VOC sensor for early detection of late blight (P. infestans) and differentiation of early blight and Septoria leaf spot on tomato [65]. (B) A carbon nanotube-graphite-based wearable sensor transferred onto the surface of a live leaf [70].

Figure 3.. Multiplexing enables more data to be generated from each sample.

Schematic representation of (A) single and (B) multiplexed lateral flow devices (LFDs). LFDs are strips in a plastic cassette. Each strip is composed of four membranes (i.e. sample, conjugation, test and wick/adsorbent pad) on a plastic adhesive backing card. The conjugation pad contains detection antibodies conjugated with nanoparticles (e.g. gold nanoparticles) that are responsible for the visual read-out. The test pad is the location of the read-out. The crucial reaction on the test pad happens within spatially defined zones (i.e. test and control zones) that contain specific recognition elements (i.e. capture and secondary antibody). Single LFDs contain one test zone (i.e. one single test on one strip), while multiplexed LFDs accommodate 2+ test zones (i.e. multiple tests on one single strip). The wick pad acts as bin, prevents backflow of excess reagents, and maintains capillary flow.

Figure 4.. MinION sequencer with Flongle flowcell adapter and mini flowcell fitted. Also shows USB connection which allows the device to be run from a laptop.

Figure 5.. Cell-free synthetic gene circuits.

(A) Synthetic gene circuits can be designed to produce different outputs in response to a range of biotic and abiotic targets. In this example, the circuit produces a blue signal in response to a viral RNA target. (B) Traditionally, such circuitry has been designed and implemented in whole cells. Now, however, it is increasingly common to deploy such circuits in a cell-free manner, by using prepared cell lysates from bacteria to perform these reactions. This obviates the need for the use of genetically modified organisms outside a laboratory. (C) Recent developments have also demonstrated that these cell-free reactions are compatible with a range of physical supports, including paper and hydrogel materials, allowing cell-free reactions to be incorporated into these materials and utilise said materials as interaction surfaces. This opens the possibility of developing materials with surveillance capabilities that can have a continuous presence in the field.

Figure 6.. Integration of different technologies to support plant biosecurity and surveillance.

Centre: two main scenarios are represented; biosecurity (port and airport) and in-field surveillance (glasshouse and field). The technologies represented are (A) remote sensing; (B) smartphone-based detection; (C) plant VOCs detection; (D) LFA detection of proteins, (E) targeted and automated DNA detection and (F) High-throughput sequencing technologies.