In wheat breeding, spike number is a key indicator for evaluating wheat yield, and the timely and accurate acquisition of wheat spike number is of great practical significance for yield prediction. In actual production; the method of using an artificial field survey to count wheat spikes is time-consuming and labor-intensive. Therefore, this paper proposes a method based on YOLOv5s with an improved attention mechanism, which can accurately detect the number of small-scale wheat spikes and better solve the problems of occlusion and cross-overlapping of the wheat spikes. This method introduces an efficient channel attention module (ECA) in the C3 module of the backbone structure of the YOLOv5s network model; at the same time, the global attention mechanism module (GAM) is inserted between the neck structure and the head structure; the attention mechanism can be more Effectively extract feature information and suppress useless information. The result shows that the accuracy of the improved YOLOv5s model reached 71.61% in the task of wheat spike number, which was 4.95% higher than that of the standard YOLOv5s model and had higher counting accuracy. The improved YOLOv5s and YOLOv5m have similar parameters, while RMSE and MEA are reduced by 7.62 and 6.47, respectively, and the performance is better than YOLOv5l. Therefore, the improved YOLOv5s method improves its applicability in complex field environments and provides a technical reference for the automatic identification of wheat spike numbers and yield estimation. Labeled images, source code, and trained models are available at: https://github.com/228384274/improved-yolov5.

Drought is one of the main adverse factors affecting the growth and development of wheat. The molecular regulation pathway of Strigolactone (SLs or SL),which induces drought resistance in wheat, needs to be further clarified. In this study, SL and Tis (Strigolactone inhibitor) were sprayed on leaves to clarify the changes in wheat drought resistance and their effect on antioxidant enzyme activity, photosynthesis and other metabolic processes. However, 20 kinds of DOF transcription factors were identified by transcriptome metabolome association analysis, and they were highly enriched on chromosome 2. Moreover, the proline, glycosides, indoleacetic acid, betaine, etc., in wheat are the key factors affecting the change in the drought resistance of wheat. The study initially revealed the mechanism of the involvement of DOF in the SL regulation pathway and revealed its impact on different metabolites of wheat, thus providing a theoretical reference for the subsequent molecular verification and breeding of excellent drought-resistant varieties.

Wheat is one of the main crops in northern China, and drought has been a major problem in wheat cultivation. As a new type of plant hormone, Strigolactone (SLs), its effect on drought resistance of wheat needs to be studied extensively. In this study, the effects of SLs on the growth and development of wheat from florescence stage to maturity stage were determined by plant physiological and enzyme-linked immunoassay techniques. The results showed that, with the progress of reproductive development, leaf spraying exogenous SLs not only delayed the peak of ABA content, promoted the increase of IAA and GA content in the early stage, changed the metabolic balance of hormone in leaves, and thus changed the physiological metabolic process. Especially under the condition of high density, the effects of SLs were more obvious. And, with the change of the blade metabolic balance, its net photosynthetic rate and the level of carbon dioxide fixation increased, and SOD and POD activity enhanced, MDA content decreased, indicating that aging rate of leaves was slowed down and matter accumulation ability enhanced, so as to improve the filling rate and increase the weight of the mature grain, eventually promoted the yield of wheat. Through the above analysis, it can be seen that exogenous SLs can effectively improve the yield of wheat under high density condition, which provides reliable theoretical and technical support for its practical application in the field.

In order to quickly and accurately obtain density and height information of winter wheat varieties, it is of great practical significance for the growth monitoring of new wheat varieties. In actual production, the plant density and height are mainly obtained by manual measurement, which is inefficient, time-consuming and laborious. Therefore, the winter wheat were extracted coverage based on unmanned aerial vehicles (UAV) images at seedling stage, the relationship between coverage and plant density were investigated. Moreover, the high-definition digital images of winter wheat varieties at 4 growth stages including jointing, booting, flowering and grain filling stages were obtained. The digital orthophoto model (DOM) and digital surface model (DSM) of winter wheat varieties was generated in combination with the ground control points. The estimation model of plant height at the four growing stages were established. Based on the ground measured plant height (H) of new wheat varieties, the plant height of new wheat varieties extracted by DSM was verified. The results showed that the coverage of new wheat varieties extracted from the UAV images at seedling stage was highly correlated with the measured plant density, and the coefficient of determination (R²) was 0.82. The new wheat varieties H extracted by DSM was significantly correlated with the measured H, and the fittedR²and root mean square error (RMSE) of the predicted plant height and the measured value were 0.96 and 6.32 cm, respectively. It indicated that the use of UAV images to predict the plant density and plant height of new wheat varieties has good applicability, and can provide technical reference for the monitoring of wheat phenotypic information in the future.