The transcriptome landscapes of citrus leaf in different developmental stages.

KEY MESSAGE: The temporal expression profiles of citrus leaves explain the sink-source transition of immature leaves to mature leaves and provide knowledge regarding the differential responses of mature and immature leaves to biotic stress such as citrus canker and Asian citrus psyllid (Diaphorina citri). Citrus is an important fruit crop worldwide. Different developmental stages of citrus leaves are associated with distinct features, such as differences in susceptibilities to pathogens and insects, as well as photosynthetic capacity. Here, we investigated the mechanisms underlying these distinctions by comparing the gene expression profiles of mature and immature citrus leaves. Immature (stages V3 and V4), transition (stage V5), and mature (stage V6) Citrus sinensis leaves were chosen for RNA-seq analyses. Carbohydrate biosynthesis, photosynthesis, starch biosynthesis, and disaccharide metabolic processes were enriched among the upregulated differentially expressed genes (DEGs) in the V5 and V6 stages compared with that in the V3 and V4 stages. Glucose level was found to be higher in V5 and V6 than in V3 and V4. Among the four stages, the largest number of DEGs between contiguous stages were identified between V5 and V4, consistent with a change from sink to source, as well as with the sucrose and starch quantification data. The differential expression profiles related to cell wall synthesis, secondary metabolites such as flavonoids and terpenoids, amino acid biosynthesis, and immunity between immature and mature leaves may contribute to their different responses to Asian citrus psyllid infestation. The expression data suggested that both the constitutive and induced gene expression of immunity-related genes plays important roles in the greater resistance of mature leaves against Xanthomonas citri compared with immature leaves. The gene expression profiles in the different stages can help identify stage-specific promoters for the manipulation of the expression of citrus traits according to the stage. The temporal expression profiles explain the sink-source transition of immature leaves to mature leaves and provide knowledge regarding the differential responses to biotic stress.

Digital Gene Expression Analysis of Ponkan Mandarin (Citrus reticulata Blanco) in Response to Asia Citrus Psyllid-Vectored Huanglongbing Infection.

Citrus Huanglongbing (HLB), the most destructive citrus disease, can be transmitted by psyllids and diseased budwoods. Although the final symptoms of the two main HLB transmission ways were similar and hard to distinguish, the host responses might be different. In this study, the global gene changes in leaves of ponkan (Citrus reticulata) mandarin trees following psyllid-transmission of HLB were analyzed at the early symptomatic stage (13 weeks post inoculation, wpi) and late symptomatic stage (26 wpi) using digital gene expression (DGE) profiling. At 13 wpi, 2452 genes were downregulated while only 604 genes were upregulated in HLB infected ponkan leaves but no pathway enrichment was identified. Gene function analysis showed impairment in defense at the early stage of infection. At late stage of 26 wpi, however, differentially expressed genes (DEGs) involved in carbohydrate metabolism, plant defense, hormone signaling, secondary metabolism, transcription regulation were overwhelmingly upregulated, indicating that the defense reactions were eventually activated. The results indicated that HLB bacterial infection significantly influenced ponkan gene expression, and a delayed response of the host to the fast growing bacteria might be responsible for its failure in fighting against the bacteria.

YOLO-SCL: a lightweight detection model for citrus psyllid based on spatial channel interaction.

Efficient and accurate detection and providing early warning for citrus psyllids is crucial as they are the primary vector of citrus huanglongbing. In this study, we created a dataset comprising images of citrus psyllids in natural environments and proposed a lightweight detection model based on the spatial channel interaction. First, the YOLO-SCL model was based on the YOLOv5s architecture, which uses an efficient channel attention module to perform local channel attention on the inputs in the recursive gated convolutional modules to achieve a combination of global spatial and local channel interactions, improving the model's ability to express the features of the critical regions of small targets. Second, the lightweight design of the 21st layer C3 module in the neck network of the YOLO-SCL model and the small target feature information were retained to the maximum extent by deleting the two convolutional layers, whereas the number of parameters was reduced to improve the detection accuracy of the model. Third, with the detection accuracy of the YOLO-SCL model as the objective function, the black widow optimization algorithm was used to optimize the hyperparameters of the YOLO-SCL model, and the iterative mechanism of swarm intelligence was used to further improve the model performance. The experimental results showed that the YOLO-SCL model achieved a mAP@0.5 of 97.07% for citrus psyllids, which was 1.18% higher than that achieved using conventional YOLOv5s model. Meanwhile, the number of parameters and computation amount of the YOLO-SCL model are 6.92 M and 15.5 GFlops, respectively, which are 14.25% and 2.52% lower than those of the conventional YOLOv5s model. In addition, after using the black widow optimization algorithm to optimize the hyperparameters, the mAP@0.5 of the YOLO-SCL model for citrus psyllid improved to 97.18%, making it more suitable for the natural environments in which citrus psyllids are to be detected. The experimental results showed that the YOLO-SCL model has good detection accuracy for citrus psyllids, and the model was ported to the Jetson AGX Xavier edge computing platform, with an average processing time of 38.8 ms for a single-frame image and a power consumption of 16.85 W. This study provides a new technological solution for the safety of citrus production.

Detection Method of Citrus Psyllids With Field High-Definition Camera Based on Improved Cascade Region-Based Convolution Neural Networks.

Citrus psyllid is the only insect vector of citrus Huanglongbing (HLB), which is the most destructive disease in the citrus industry. There is no effective treatment for HLB, so detecting citrus psyllids as soon as possible is the key prevention measure for citrus HLB. It is time-consuming and laborious to search for citrus psyllids through artificial patrol, which is inconvenient for the management of citrus orchards. With the development of artificial intelligence technology, a computer vision method instead of the artificial patrol can be adopted for orchard management to reduce the cost and time. The citrus psyllid is small in shape and gray in color, similar to the stem, stump, and withered part of the leaves, leading to difficulty for the traditional target detection algorithm to achieve a good recognition effect. In this work, in order to make the model have good generalization ability under outdoor light condition, a high-definition camera to collect data set of citrus psyllids and citrus fruit flies under natural light condition was used, a method to increase the number of small target pests in citrus based on semantic segmentation algorithm was proposed, and the cascade region-based convolution neural networks (R-CNN) (convolutional neural network) algorithm was improved to enhance the recognition effect of small target pests using multiscale training, combining CBAM attention mechanism with high-resolution feature retention network high-resoultion network (HRNet) as feature extraction network, adding sawtooth atrous spatial pyramid pooling (ASPP) structure to fully extract high-resolution features from different scales, and adding feature pyramid networks (FPN) structure for feature fusion at different scales. To mine difficult samples more deeply, an online hard sample mining strategy was adopted in the process of model sampling. The results show that the improved cascade R-CNN algorithm after training has an average recognition accuracy of 88.78% for citrus psyllids. Compared with VGG16, ResNet50, and other common networks, the improved small target recognition algorithm obtains the highest recognition performance. Experimental results also show that the improved cascade R-CNN algorithm not only performs well in citrus psylla identification but also in other small targets such as citrus fruit flies, which makes it possible and feasible to detect small target pests with a field high-definition camera.

Metabolic Profiling of Hybrids Generated from Pummelo and Citrus latipes in Relation to Their Attraction to Diaphorina citri, the Vector of Huanglongbing.

The citrus industry at present is severely affected by huanglongbing disease (HLB). HLB is caused by the supposed bacterial pathogen "Candidatus Liberibacter asiaticus" and is transmitted by the insect vector, the Asian citrus psyllid, Diaphorina citri Kuwayama. Developing new citrus hybrids to improve HLB management is much needed. In this study, we investigated the metabolomic profiles of three new hybrids produced from the cross of C2-5-12 Pummelo (Citrus maxima (L.) Osbeck) × pollen from Citrus latipes. The hybrids were selected based on leaf morphology and seedling vigor. The selected hybrids exhibited compact and upright tree architecture as seen in C. latipes. Hybrids were verified by simple sequence repeat markers, and were subjected to metabolomic analysis using gas chromatography-mass spectrometry. The volatile organic compounds (VOCs) and polar metabolites profiling also showed that the new hybrids were different from their parents. Interestingly, the levels of stored VOCs in hybrid II were higher than those observed in its parents and other hybrids. The level of most VOCs released by hybrid II was also higher than that released from its parents. Additionally, the preference assay showed that hybrid II was more attractive to D. citri than its parents and other hybrids. The leaf morphology, compact and upright architecture of hybrid II, and its attraction to D. citri suggest that it could be used as a windbreak and trap tree for D. citri (double duty), once its tolerance to HLB disease is confirmed. Our results showed that metabolomic analysis could be successfully used to understand the biochemical mechanisms controlling the interaction of D. citri with its host plants.