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[学术文献 ] Odorant receptor 75 is essential for attractive response to plant volatile p-anisaldehyde in Western flower thrips 进入全文
Pesticide Biochemistry and Physiology 期刊
The Western flower thrip (WFT), Frankliniella occidentalis, is a major pest of many vegetable crops and also a vector for the tomato spotted wilt virus, causing devastating damage worldwide. Odorant receptors (ORs) play an important role in host plant searching, however, specific functions of those ORs in WFT remain unclear. In this study, the attractive activity of four plant volatiles ((S)-(−)-verbenone, p-anisaldehyde, methyl isonicotinate, and benzaldehyde) to WFT was confirmed using a Y-tube olfactometer. Then, the specific receptor, OR75, was screened out as the candidate OR for these odorants, as its expression was significantly upregulated upon exposure to these odorants. Further in vitro functional assays with Xenopus oocyte expression system confirmed sensitivity of OR75 to p-anisaldehyde and three other odorants (β-ionone, undecanal and cinnamaldehyde). Of the three odorants, β-ionone was also attractive to WFT. Further, in vivo RNA interference experiments showed that the dsOR75 treated thrips lost their attractive response to p-anisaldehyde, but retained response to β-ionone. Finally, 3-D structures prediction and molecular docking showed that OR75 formed a hydrogen bond with p-anisaldehyde at His150 residue, while no hydrogen bond formed with β-ionone, undecanal or cinnamaldehyde. Taken together, OR75 plays a crucial role in perception of p-anisaldehyde, which helps us understand the host-seeking mechanisms of WFT, and provides a basis for development of olfactory based pest control strategies. This is the first report of an OR playing roles in sensing p-anisaldehyde in thrips.
[学术文献 ] Tea Disease Recognition Based on Image Segmentation and Data Augmentation 进入全文
IEEE Access 期刊
Accurate identification of tea leaf diseases is crucial for intelligent tea cultivation and monitoring. However, the complex environment of tea plantations—affected by weather variations and uneven lighting—poses significant challenges for building effective disease recognition models using raw field-captured images. To address this, we propose a method that combines two-stage image segmentation with an improved conditional generative adversarial network (IC-GAN). The two-stage segmentation approach, integrating graph cuts and support vector machines (SVM), effectively isolates disease regions from complex backgrounds. The IC-GAN augments the dataset by generating high-quality synthetic disease images for model training. Finally, an Inception Embedded Pooling Convolutional Neural Network (IDCNN) is developed for disease recognition. Experimental results demonstrate that the segmentation method improves recognition accuracy from 53.36% to 75.63%, while the IC-GAN increases the dataset size. The IDCNN achieves 97.66% accuracy, 97.36% recall, and a 96.98% F1 score across three types of tea diseases. Comparative evaluations on two additional datasets further confirm the method’s robustness and accuracy, offering a practical solution to reduce tea production losses and improve quality.
[会议论文 ] Biohybrid volatile organic compound sensing system 进入全文
IEEE National Aerospace and Electronics Conference
A biohybrid sensor is reported to integrate live insect antennae with micro electrode arrays. High resolution recording of voltage responses generated by olfactory sensory neurons (OSNs) were obtained in response to a panel of four volatile organic compounds (VOCs) at two concentrations, 20 parts per billion (ppb) and 20 parts per million (ppm). Biohybrid sensor lifetime was sustained by a novel micro fluidic platform with sensor responses acquired at 24 hours, 48 hours, 7 days, and 14 days post resection of antenna from the host. VOC identity was classified by providing OSN firing rate histograms as input into a multilayer perceptron artificial neural network (MLP ANN). Biohybrid sensor response was found to be affected by anatomical location and VOC identity and thus influenced classification accuracies. Significant classification accuracies were achieved at the 24-hour and 14-day timepoints. Toluene at the 14-day timepoint elicited a unique response resulting in 100% classification at the distal anatomical location. We believe this works provides a framework for utilizing biohybrid sensing systems for VOC detection and identification.
[学术文献 ] Performance enhancement of kernelized SVM with deep learning features for tea leaf disease prediction 进入全文
Multimedia Tools and Applications 期刊
Due to very limited number of tea leaf images, classification is very difficult. Very frequently overfitting of model occurs. To cope up with this, we applied images augmentation process, that increased dataset nearly fourteen times. But still this number of datasets is not adequate for DL based classification. So, we used here deep learning for feature extraction and machine learning based classifier for classification. In this work, we have proposed a hybrid technique that combines deep learning-based features of augmented dataset with machine learning based classifier for getting better classification result. In proposed work, VGG-16 is used for colour feature extraction from the tea leaf dataset. Based on this feature, model is built and several machine learning-based classifiers like KNN, XGB, Random Forest, and kernelized SVM are employed for classification task. Our proposed model achieved highest classification accuracy with Sigmoid and Linear kernel based SVM and VGG-16 features. The accuracy of proposed model is 96.67%. We compared our proposed work with existing work on tea leaf dataset and found that our model is performing comparatively better.
[学术文献 ] A Novel Ethyl Formate Fumigation Strategy for Managing Yellow Tea Thrips (Scirtothrips dorsalis) in Greenhouse Cultivated Mangoes and Post-Harvest Fruits 进入全文
Insects 期刊
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.
[相关专利 ] A VOLATILE PESTICIDE COMPOSITION AND A METHOD OF PRODUCTION THEREOF 进入全文
印度专利
本发明涉及挥发性农药组合物100及其制备方法。挥发性农药组合物100的生产方法包括提取半挥发性农药101、汽化剂103和赋形剂105。还包括从印楝种子粉末、印楝叶、印楝树枝和印楝树皮中提取半挥发性农药。该方法可能包括蒸发剂103，其提供足够的表面积以促进半挥发性农药101的挥发。基于赋形剂105、半挥发性农药101和汽化剂103，生产挥发性农药组合物100的均匀混合物的方法适用于大田作物，以控制田间的害虫和幼虫，而不会对人类、环境和授粉的昆虫造成损害。
