https://www.selleckchem.com/products/erastin.html Background In view of the existence of light shadow, branches occlusion, and leaves overlapping conditions in the real natural environment, problems such as slow detection speed, low detection accuracy, high missed detection rate, and poor robustness in plant diseases and pests detection technology arise. Results Based on YOLOv3-tiny network architecture, to reduce layer-by-layer loss of information during network transmission, and to learn from the idea of inverse-residual block, this study proposes a YOLOv3-tiny-IRB algorithm to optimize its feature extraction network, improve the gradient disappearance phenomenon during network deepening, avoid feature information loss, and realize network multilayer feature multiplexing and fusion. The network is trained by the methods of expanding datasets and multiscale strategies to obtain the optimal weight model. Conclusion The experimental results show that when the method is tested on the self-built tomato diseases and pests dataset, and while ensuring the detection speed (206 frame rate per second), the mean Average precision (mAP) under three conditions (a) deep separation, (b) debris occlusion, and (c) leaves overlapping are 98.3, 92.1, and 90.2%, respectively. Compared with the current mainstream object detection methods, the proposed method improves the detection accuracy of tomato diseases and pests under conditions of occlusion and overlapping in real natural environment.Drought stress causes various negative impacts on plant growth and crop production. R2R3-MYB transcription factors (TFs) play crucial roles in the response to abiotic stress. However, their functions in Betula platyphylla haven't been fully investigated. In this study, a R2R3 MYB transcription factor gene, BpMYB123, was identified from Betula platyphylla and reveals its significant role in drought stress. Overexpression of BpMYB123 enhances tolerance to drought stress in contrast to repression of Bp