Soon Hwa Kwon

The tobacco cutworm, Spodoptera litura, is a globally important agricultural pest that relies on a female-produced sex pheromone blend for mate location. Here, we investigated the electrophysiological and behavioral responses of S. litura to its sex pheromone components and structurally related compounds using gas chromatography–electroantennogrm detection (GC-EAD) and field trapping assays. Most of the 17 compounds tested elicited antennal responses in both sexes, although response magnitude and sensitivity differed markedly among compounds and between males and females. Male responses were generally stronger, but females also detected several pheromone-related compounds with high sensitivity, demonstrating previously unrecognized pheromone autodetection in S. litura. In field assays, four compounds, Z7-12:Ac, Z9-14:Ac, Z9-14:OH, and Z9E12-14:Ac, strongly inhibited male attraction to the conspecific pheromone. Other structurally related compounds either had weaker or inconsistent inhibitory effects. Our findings suggest that broad sensitivity to structurally related and heterospecific pheromone compounds may contribute to discrimination among chemically similar odor signals and thereby facilitate species recognition and reproductive isolation in Spodoptera. The potent behavioral antagonists identified here also provide promising candidates for the development of pheromone-based, environmentally sustainable approaches to S. litura management.

The effects of climate change and shifting consumer preferences for tropical/subtropical mango fruits have accelerated their greenhouse cultivation in South Korea, which has consequently exacerbated the risk of unexpected or exotic insect pest outbreaks. This study used the pest risk analysis (PRA) of greenhouse-cultivated mangoes provided by the Animal & Plant Quarantine Agency in Korea to evaluate the potential of ethyl formate (EF) fumigation as a new pest management strategy against the yellow tea thrips (Scirtothrips dorsalis), which is considered a surrogate pest in the thrips group according to the PRA. The efficacy and phytotoxicity of EF were evaluated in greenhouse-cultivated mango tree (Irwin variety) and post-harvest mango fruit scenarios. EF efficacy ranged from 6.25 to 6.89 g∙h/m³ for lethal concentration time (LCt)₅₀ and from 17.10 to 18.18 g∙h/m³ for LCt₉₉, indicating similar efficacy across both scenarios. Application of 10 g/m³ EF for 4 h at 23 °C could effectively control S. dorsalis (100% mortality) without causing phytotoxic damage to the greenhouse-cultivated mango trees, while post-harvest mango fruit fumigation with 15 g/m³ EF for 4 h at 10 °C showed potential for complete disinfestation of S. dorsalis without compromising fruit quality.

This study proposes a deep-learning-based system for detecting and classifying Scirtothrips dorsalis Hood, a highly invasive insect pest that causes significant economic losses to fruit crops worldwide. The system uses yellow sticky traps and a deep learning model to detect the presence of thrips in real time, allowing farmers to take prompt action to prevent the spread of the pest. To achieve this, several deep learning models are evaluated, including YOLOv5, Faster R-CNN, SSD MobileNetV2, and EfficientDet-D0. EfficientDet-D0 was integrated into the proposed smartphone application for mobility and usage in the absence of Internet coverage because of its smaller model size, fast inference time, and reasonable performance on the relevant dataset. This model was tested on two datasets, in which thrips and non-thrips insects were captured under different lighting conditions. The system installation took up 13.5 MB of the device’s internal memory and achieved an inference time of 76 ms with an accuracy of 93.3%. Additionally, this study investigated the impact of lighting conditions on the performance of the model, which led to the development of a transmittance lighting setup to improve the accuracy of the detection system. The proposed system is a cost-effective and efficient alternative to traditional detection methods and provides significant benefits to fruit farmers and the related ecosystem.

Citrus is the most extensively produced fruit tree crop in the world and is grown in over 130 countries. Fungal diseases in citrus can cause significant losses in yield and quality. An accurate diagnosis is critical for determining the best management practices and preventing future losses. In this study, a Recombinase polymerase amplification (RPA)-clustered regularly interspaced short palindromic repeats (CRISPR)/associated (Cas) system was established with the integration of a lateral flow assay (LFA) readout system for diagnosis of citrus scab. This detection can be completed within 1 h, is highly sensitive and prevents cross-reactions with other common fungal citrus diseases. Furthermore, the detection system is compatible with crude DNA extracted from infected plant tissue. This RPA-CRISPR/Cas12a-LFA system provides a sensitive, rapid, and cost-effective method with promising and significant practical value for point-of-care diagnosis of citrus scab. To our knowledge, this is the first report to establish an RPA- and CRISPR-based method with LFA for fungal diseases in plants.