Aims:  Biocontrol is an emerging trend aimed at reducing chemical input while increasing plant fitness, productivity and resistance to diseases in sustainable agriculture. An antagonist, pY11T‐3‐1, was herein characterized for potential applications against soil‐borne plant diseases. Methods and Results:  In vitro antagonistic assays, the antagonist pY11T‐3‐1 was demonstrated able to obviously reduce the occurrence of the soft rot disease on Pinellia ternata, potato, pepper, tomato, cucumber and eggplant tubers or fruits, with higher prevention (90%) on P. ternata. It showed a broad antagonistic spectrum against 23 tested bacterial and fungal phytopathogens, which were distributed in 14 genus and 17 species. However, it inhibited only two of the seven bacterial nonpathogens. Phenotypic characterizations showed that the antagonist pY11T‐3‐1 was similar to Pseudomonas aeruginosa. Its major fatty acids were 18:1 w7c (22·17%), 16:0 (20·21%), 12:0 2OH (12·45%), 16:1w7c/15 iso2OH (10·95%) and 10:0 3OH (10·79%), which is a different profile from that of Ps. aeruginosa. The 16S rRNA and gyr B gene sequences shared 100 and 99% similarity with Ps. aeruginosa, respectively. The phylogenetic trees showed that it was clustered with Ps. aeruginosa. Conclusions:  The antagonist pY11T‐3‐1 was characterized as Ps. aeruginosa with a unique fatty acid profile. Significance and Impact of the Study:  With broad antagonistic spectrum and host selectivity, the antagonist pY11T‐3‐1 may provide a more environmental and economical alternative to the control of soil‐borne disease on P. ternata, which needs further investigation.

As one of the ancient cultivated crops in China, millet has the characteristics of high nutritional value, drought resistance and barrenness. It also plays an important role in ensuring the supply of food in our country. At present, most of the millet breeding work uses manual extraction of phenotypic information, which is laborintensive and inefficient. Therefore, the development of an automated, efficient and accurate millet phenotype detection method has practical significance for the extraction of the millet genome. In this study, a combination of sparse reconstruction based on Structure from Motion (SfM) and Patch-based Multi-View Stereo (PMVS) was used to select three different varieties of millet. A total of 81 samples of 9 samples in each period were reconstructed to obtain a 3D model of millet. The combination of conditional filtering and statistical filtering is used to remove the noise points generated during the photographing process, and finally the obtained point cloud data is used to measure the agronomic traits of millet such as plant height and leaf area. The results show that the interval angle of 5° is the best reconstruction angle of millet. The coefficient of determination R2 of point cloud measurement results and manual measurement data regression analysis is higher than 0.94, indicating that the method used for 3D reconstruction has high applicability to different millet in different periods and high-throughput measurement of millet by the method in this paper is feasible. This study provides a theoretical basis for a millet phenotypic information measurement device