Drought stress seriously affects crop growth, development, and grain production. Existing machine learning methods have achieved great progress in drought stress detection and diagnosis. However, such methods are based on a hand-crafted feature extraction process, and the accuracy has much room to improve. In this paper, we propose the use of a deep convolutional neural network (DCNN) to identify and classify maize drought stress. Field drought stress experiments were conducted in 2014. The experiment was divided into three treatments: optimum moisture, light drought, and moderate drought stress. Maize images were obtained every two hours throughout the whole day by digital cameras. In order to compare the accuracy of DCNN, a comparative experiment was conducted using traditional machine learning on the same dataset. The experimental results demonstrated an impressive performance of the proposed method. For the total dataset, the accuracy of the identification and classification of drought stress was 98.14% and 95.95%, respectively. High accuracy was also achieved on the sub-datasets of the seedling and jointing stages. The identification and classification accuracy levels of the color images were higher than those of the gray images. Furthermore, the comparison experiments on the same dataset demonstrated that DCNN achieved a better performance than the traditional machine learning method (Gradient Boosting Decision Tree GBDT). Overall, our proposed deep learning-based approach is a very promising method for field maize drought identification and classification based on digital images.

Bacillus velezensis TH-1 is a plant growth–promoting rhizobacteria with biocontrol potential that was isolated from the rhizosphere of Sophora tonkinensis Radix . Our previous results showed that strain TH-1 demonstrated effective biocontrol activity against root rot of Sophora tonkinensis Radix and bacterial wilt of ginger. Currently, only a few whole-genome sequences of biocontrol strains isolated from the rhizosphere of medicinal plants are available. We report, here, the complete genome sequence of B. velezensis TH-1. The size of TH-1 genome is 3,929,846 bp that consists of 3,900 genes with a total GC content of 46.48%. The strain TH-1 genome has 3,661 coding genes, 86 transfer RNAs, 27 ribosomal RNAs, and 16 small RNAs. Moreover, we identified nine gene clusters coding for the biosynthesis of antimicrobial compounds. The genomic information of TH-1 will provide resources for the study of biological control mechanisms and plant-microbe interactions. [Formula: see text] Copyright © 2023 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license .