Efficient identification of apple leaf diseases (ALDs) can reduce the use of pesticides and increase the quality of apple fruit, which is of significance to smart agriculture. However, existing research into identifying ALDs lacks models/methods that satisfy efficient identification in the wild environment, hindering the application of smart agriculture in the apple industry. Therefore, this paper explores an ACCURATE, LIGHTWEIGHT, and ROBUST convolutional neural network (CNN) called EfficientNet-MG, improving the conventional EfficientNet network by the multistage feature fusion (MSFF) method and gaussian error linear unit (GELU) activation function. The shallow and deep convolutional layers usually contain detailed and semantic information, respectively, but conventional EfficientNets do not fully utilize the different stage convolutional layers. Thus, MSFF was adopted to improve the semantic representation capacity of the last layer of features, and GELU was used to adapt to complicated tasks. Further, a comprehensive ALD dataset called AppleLeaf9 was constructed for the wild environment. The experimental results show that EfficientNet-MG achieves a higher accuracy (99.11%) and fewer parameters (8.42 M) than the five classical CNN models, thus proving that EfficientNet-MG achieves more competitive results on ALD identification.
The NAC (NAM, ATAF, and CUC) gene family is one of the largest plant-specific transcription factor families. Its members have various biological functions that play important roles in regulating plant growth and development and in responding to biotic and abiotic stresses. However, their functions in woody plants are not fully understood. In this study, we isolated an NAC family member, the CpNAC1 promoter and gene, from wintersweet. CpNAC1 was localized to the nucleus and showed transcriptional activation activity. qRT-PCR analyses revealed that the gene was expressed in almost all tissues tested, with the highest levels found in mature leaves and flower buds. Moreover, its expression was induced by various abiotic stresses and ABA treatment. Its expression patterns were further confirmed in CpNAC1pro:GUS (β-glucuronidase) plants. Among all the transgenic lines, CpNAC1pro-D2 showed high GUS histochemical staining and activity in different tissues of Arabidopsis. Furthermore, its GUS activity significantly increased in response to various abiotic stresses and ABA treatment. This may be related to the stress-related cis-elements, such as ABRE and MYB, which clustered in the CpNAC1pro-D2 segment, suggesting that CpNAC1pro-D2 is the core segment that responds to abiotic stresses and ABA. In addition, CpNAC1-overexpressed Arabidopsis plants had weaker osmosis tolerance than the wild-type plants, demonstrating that CpNAC1 may negatively regulate the drought stress response in transgenic Arabidopsis. Our results provide a foundation for further analyses of NAC family genes in wintersweet, and they broaden our knowledge of the roles that NAC family genes may play in woody plants.
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