Detection of Emerald Ash Borer Infestations in Living Green Ash by Noninvasive Electronic-Nose Analysis of Wood Volatiles

Seismic sensor-based management of the red palm weevil Rhynchophorus ferrugineus in date palm plantations

BACKGROUND

The red palm weevil (RPW), Rhynchophorus ferrugineus, is one of the gravest threats to palm trees. The challenge in monitoring RPW primarily arises from the inconspicuous presence of larvae within the stem, which is often devoid of noticeable symptoms. This study looks at the use of seismic sensors in RPW management in commercial date palm plantations. It explores whether the data garnered from the sensor domain, and its translation into the health status of date palms, can reliably inform precise decision-making.

RESULTS

Sensor and damage index values, as gauged by the Agrint IoTree seismic sensor, vividly mirrored RPW colonization activity. They also accurately portrayed the impact of three distinct insecticides: imidacloprid, phosphine, and entomopathogenic nematodes. The seismic values and damage index of healthy untreated palms strongly supported the decision to pursue tree recovery. Furthermore, this facilitated the computation of recovery pace discrepancies across the tested treatments, measured as the number of days required for tree restoration.

CONCLUSIONS

Our findings underscore the practicality of employing seismic sensors, as exemplified by the IoTree system and its network services, to both monitor and assess palm tree health. Furthermore, it validates their efficacy in evaluating the efficiency of management strategies adopted against RPW, all grounded in a wealth of sensor-derived data. © 2023 Society of Chemical Industry.

Emerald Ash Borer Management and Research: Decades of Damage and Still Expanding

Since the discovery of the ash tree (Fraxinus spp.) killer emerald ash borer (EAB; Agrilus planipennis) in the United States in 2002 and Moscow, Russia in 2003, substantial detection and management efforts have been applied to contain and monitor its spread and mitigate impacts. Despite these efforts, the pest continues to spread within North America. It has spread to European Russia and Ukraine and is causing sporadic outbreaks in its native range in China. The dynamics of EAB's range expansion events appear to be linked to the lack of resistant ash trees in invaded ranges, facilitated by the abundance of native or planted North American susceptible ash species. We review recently gained knowledge of the range expansion of EAB; its ecological, economic, and social impacts; and past management efforts with their successes and limitations. We also highlight advances in biological control, mechanisms of ash resistance, and new detection and management approaches under development, with the aim of guiding more effective management.

Forest Insect Biosecurity: Processes, Patterns, Predictions, Pitfalls

The economic and environmental threats posed by non-native forest insects are ever increasing with the continuing globalization of trade and travel; thus, the need for mitigation through effective biosecurity is greater than ever. However, despite decades of research and implementation of preborder, border, and postborder preventative measures, insect invasions continue to occur, with no evidence of saturation, and are even predicted to accelerate. In this article, we review biosecurity measures used to mitigate the arrival, establishment, spread, and impacts of non-native forest insects and possible impediments to the successful implementation of these measures. Biosecurity successes are likely under-recognized because they are difficult to detect and quantify, whereas failures are more evident in the continued establishment of additional non-native species. There are limitations in existing biosecurity systems at global and country scales (for example, inspecting all imports is impossible, no phytosanitary measures are perfect, knownunknowns cannot be regulated against, and noncompliance is an ongoing problem). Biosecurity should be a shared responsibility across countries, governments, stakeholders, and individuals.

Sensing of Airborne Infochemicals for Green Pest Management: What Is the Challenge?

One of the biggest global challenges for our societies is to provide natural resources to the rapidly expanding population while maintaining sustainable and ecologically friendly products. The increasing public concern about toxic insecticides has resulted in the rapid development of alternative techniques based on natural infochemicals (ICs). ICs (e.g., pheromones, allelochemicals, volatile organic compounds) are secondary metabolites produced by plants and animals and used as information vectors governing their interactions. Such chemical language is the primary focus of chemical ecology, where behavior-modifying chemicals are used as tools for green pest management. The success of ecological programs highly depends on several factors, including the amount of ICs that enclose the crop, the range of their diffusion, and the uniformity of their application, which makes precise detection and quantification of ICs essential for efficient and profitable pest control. However, the sensing of such molecules remains challenging, and the number of devices able to detect ICs in air is so far limited. In this review, we will present the advances in sensing of ICs including biochemical sensors mimicking the olfactory system, chemical sensors, and sensor arrays (e-noses). We will also present several mathematical models used in integrated pest management to describe how ICs diffuse in the ambient air and how the structure of the odor plume affects the pest dynamics.

Noninvasive detection of COPD and Lung Cancer through breath analysis using MOS Sensor array based e-nose

INTRODUCTION

This paper describes the research work done toward the development of a breath analyzing electronic nose (e-nose), and the results obtained from testing patients with lung cancer, patients with chronic obstructive pulmonary disease (COPD), and healthy controls. Pulmonary diseases like COPD and lung cancer are detected with MOS sensor array-based e-noses. The e-nose device with the sensor array, data acquisition system, and pattern recognition can detect the variations of volatile organic compounds (VOC) present in the expelled breath of patients and healthy controls.

MATERIALS AND METHODS

This work presents the e-nose equipment design, study subjects selection, breath sampling procedures, and various data analysis tools. The developed e-nose system is tested in 40 patients with lung cancer, 48 patients with COPD, and 90 healthy controls.

RESULTS

In differentiating lung cancer and COPD from controls, support vector machine (SVM) with 3-fold cross-validation outperformed all other classifiers with an accuracy of 92.3% in cross-validation. In external validation, the same discrimination was achieved by k-nearest neighbors (k-NN) with 75.0% accuracy.

CONCLUSION

The reported results show that the VOC analysis with an e-nose system holds exceptional possibilities in noninvasive disease diagnosis applications.

Identification of the Red-Necked Longhorn Beetle Aromia bungii (Faldermann, 1835) (Coleoptera: Cerambycidae) with Real-Time PCR on Frass

Aromia bungii (Faldermann, 1835) (Coleoptera: Cerambycidae), the red-necked longhorn beetle is native to eastern Asia, where it is an important wood-borer of fruit and ornamental species of the genus Prunus. A. bungii is a quarantine pest in the European Union, following its accidental introduction and establishment in Germany and Italy, and is currently included in the list of priority pests. To confirm its infestations in outbreak areas, adult or larval specimens are needed to perform morphological or molecular analyses. The presence of A. bungii larvae inside the attacked trees makes the collection of specimens particularly difficult. Thus, we present two diagnostic protocols based on frass analysis with real-time PCR (probe and SYBR Green). The results obtained show that a non-invasive approach for detecting the presence of this harmful invasive pest can be a reliable and accurate alternative diagnostic tool in phytosanitary surveys, as well as to outline a sustainable pest management strategy.

Noninvasive Early Disease Diagnosis by Electronic-Nose and Related VOC-Detection Devices

This editorial provides an overview and summary of recent research articles published in Biosensors journal, volumes 9 (2019) and 10 (2020), within the Special Issue "Noninvasive Early Disease Diagnosis", which focused on recent sensors, biosensors, and clinical instruments developed for the noninvasive early detection and diagnosis of human, animal, and plant diseases or invasive pests. The six research articles included in this Special Issue provide examples of some of the latest electronic-nose (e-nose) and related volatile organic compound (VOC)-detection technologies, which are being tested and developed to improve the effectiveness and efficiency of innovative diagnostic methodologies for the early detection of particular diseases and pest infestations in living hosts, prior to symptom development.

Examining the Utility of Visible Near-Infrared and Optical Remote Sensing for the Early Detection of Rapid ‘Ōhi‘a Death

The early detection of plant pathogens at the landscape scale holds great promise for better managing forest ecosystem threats. In Hawai‘i, two recently described fungal species are responsible for increasingly widespread mortality in ‘ōhi‘a Metrosideros polymorpha, a foundational tree species in Hawaiian native forests. In this study, we share work from repeat laboratory and field measurements to determine if visible near-infrared and optical remote sensing can detect pre-symptomatic trees infected with these pathogens. After generating a dense time series of laboratory spectral reflectance data and red green blue (RGB) images for inoculated ‘ōhi‘a seedlings, seedlings subjected to extreme drought, and control plants, we found few obvious spectral indicators that could be used for reliable pre-symptomatic detection in the inoculated seedlings, which quickly experienced complete and total wilting following stress onset. In the field, we found similar results when we collected repeat multispectral and RGB imagery over inoculated mature trees (sudden onset of symptoms with little advance warning). We found selected vegetation indices to be reliable indicators for detecting non-specific stress in ‘ōhi‘a trees, but never providing more than five days prior warning relative to visual detection in the laboratory trials. Finally, we generated a sequence of linear support vector machine classification models from the laboratory data at time steps ranging from pre-treatment to late-stage stress. Overall classification accuracies increased with stress stage maturity, but poor model performance prior to stress onset and the sudden onset of symptoms in infected trees suggest that early detection of rapid ‘ōhi‘a death over timescales helpful for land managers remains a challenge.