Accurate diagnosis of apple leaf diseases is crucial for improving the quality of apple production and promoting the development of the apple industry.  However, apple leaf diseases do not differ significantly from image texture and structural information.  The difficulties in disease feature extraction in complex backgrounds slow the related research progress.  To address the problems, this paper proposes an improved multi-scale inverse bottleneck residual network model based on a triplet parallel attention mechanism, which is built upon ResNet-50, while improving and combining the inception module and ResNext inverse bottleneck blocks, to recognize seven types of apple leaf (including six diseases of alternaria leaf spot, brown spot, grey spot, mosaic, rust, scab, and one healthy).  First, the 3×3 convolutions in some of the residual modules are replaced by multi-scale residual convolutions, the convolution kernels of different sizes contained in each branch of the multi-scale convolution are applied to extract feature maps of different sizes, and the outputs of these branches are multi-scale fused by summing to enrich the output features of the images.  Second, the global layer-wise dynamic coordinated inverse bottleneck structure is used to reduce the network feature loss.  The inverse bottleneck structure makes the image information less lossy when transforming from different dimensional feature spaces.  The fusion of multi-scale and layer-wise dynamic coordinated inverse bottlenecks makes the model effectively balances computational efficiency and feature representation capability, and more robust with a combination of horizontal and vertical features in the fine identification of apple leaf diseases.  Finally, after each improved module, a triplet parallel attention module is integrated with cross-dimensional interactions among channels through rotations and residual transformations, which improves the parallel search efficiency of important features and the recognition rate of the network with relatively small computational costs while the dimensional dependencies are improved.  To verify the validity of the model in this paper, we uniformly enhance apple leaf disease images screened from the public data sets of Plant Village, Baidu Flying Paddle, and the Internet.  The final processed image count is 14,000.  The ablation study, pre-processing comparison, and method comparison are conducted on the processed datasets.  The experimental results demonstrate that the proposed method reaches 98.73% accuracy on the adopted datasets, which is 1.82% higher than the classical ResNet-50 model, and 0.29% better than the apple leaf disease datasets before preprocessing.  It also achieves competitive results in apple leaf disease identification compared to some state-of-the-art methods.

Jojoba (Simmondsia chinensis) is mainly distributed in desert, and the molecular mechanisms of jojoba in response to abiotic stress still remain elusive. In this paper, we cloned and characterized a SOD gene from jojoba named as ScMnSOD, and introduced into Arabidopsis to investigate its functions of responding to drought stress. The transgenic Arabidopsis showed an improvement in drought tolerance. Moreover, under a water deficit condition, the accumulation of reactive oxygen species (ROS) was remarkably decreased in the transgenic lines compared to the WT. Furthermore, the ScMnSOD promoter was cloned to the 5´-upstream of GUS coding region in a binary vector, and introduced into Arabidopsis. And results showed that ScMnSOD expression can be induced by drought, salt, ABA, and low temperature. In conclusion, ScMnSOD plays an important role in drought tolerance which is, at least partially, attributed to its role in ROS detoxification.