Background The receptor-like kinase (RLK) gene families in plants contains a large number of members. They are membrane proteins with an extracellular receptor domain and participate in biotic and abiotic stress responses. Results In this study, we identified RLKs in 15 representative plant genomes, including wheat, and classified them into 64 subfamilies by using four types of phylogenetic trees and HMM models. Conserved exon‒intron structures with conserved exon phases in the kinase domain were found in many RLK subfamilies from Physcomitrella patens to Triticum aestivum. Domain distributions of RLKs were also diagrammed. Collinearity events and tandem gene clusters suggested that polyploidization and tandem duplication events contributed to the member expansions of T. aestivum RLKs. Global expression pattern analysis was performed by using public transcriptome data. These analyses were involved in T. aestivum, Aegilops tauschii and Brachypodium distachyon RLKs under biotic and abiotic stresses. We also selected 9 RLKs to validate the transcriptome prediction by using qRT‒PCR under drought treatment and with Fusarium graminearum infection. The expression trends of these 9 wheat RLKs from public transcriptome data were consistent with the results of qRT‒PCR, indicating that they might be stress response genes under drought or F. graminearum treatments. Conclusion In this study, we identified, classified, evolved, and expressed RLKs in wheat and related plants. Thus, our results will provide insights into the evolutionary history and molecular mechanisms of wheat RLKs.
In order to effectively solve the problems which affect the stable and healthy development of garlic industry, such as the uncertainty of the planting scale and production data, the influence factors of price fluctuation is difficult to be accurately analyzed, the difficult to predict the trend of price change, the uncertainty of the market concentration, and the difficulty of the short-term price prediction etc. the big data platform of the garlic industry chain has been developed. Combined with a variety of data acquisition technology, the information collection of influencing factors for garlic industry chain is realized. Based on the construction of the big data technology platform, the real-time synchronous acquisition, efficient storage and analysis of the planting, market, storage, processing, export and logistics information in five provinces and seven counties are realized. The application of the big data platform for garlic industry chain has realized the accurate acquisition of garlic planting area, the price and trend of market circulation and the information of export information, analyzed the fluctuation regulation of garlic price, and also realized the short-term precision prediction of garlic price.
The maturity level of tomato is a key factor of tomato picking, which directly determines the transportation distance, storage time, and market freshness of postharvest tomato. In view of the lack of studies on tomato maturity classification under nature greenhouse environment, this paper proposes a SE-YOLOv3-MobileNetV1 network to classify four kinds of tomato maturity. The proposed maturity classification model is improved in terms of speed and accuracy: (1) Speed: Depthwise separable convolution is used. (2) Accuracy: Mosaic data augmentation, K-means clustering algorithm, and the Squeeze-and-Excitation attention mechanism module are used. To verify the detection performance, the proposed model is compared with the current mainstream models, such as YOLOv3, YOLOv3-MobileNetV1, and YOLOv5 in terms of accuracy and speed. The SE-YOLOv3-MobileNetV1 model is able to distinguish tomatoes in four kinds of maturity, the mean average precision value of tomato reaches 97.5%. The detection speed of the proposed model is 278.6 and 236.8 ms faster than the YOLOv3 and YOLOv5 model. In addition, the proposed model is considerably lighter than YOLOv3 and YOLOv5, which meets the need of embedded development, and provides a reference for tomato maturity classification of tomato harvesting robot.
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