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A microbial fungicide developed from Bacillus subtilis NCD-2 has been registered for suppressing verticillium wilt in crops in China. Spores are the main ingredient of this fungicide and play a crucial role in suppressing plant disease. Therefore, increasing the number of spores of strain NCD-2 during fermentation is important for reducing the cost of the fungicide. In this study, five kinds of carbon sources were found to promote the metabolism of strain NCD-2 revealed via Biolog Phenotype MicroArray (PM) technology. L-arabinose showed the strongest ability to promote the growth and sporulation of strain NCD-2. L-arabinose increased the bacterial concentration and the sporulation efficiency of strain NCD-2 by 2.04 times and 1.99 times compared with D-glucose, respectively. Moreover, L-arabinose significantly decreased the autolysis of strain NCD-2. Genes associated with arabinose metabolism, sporulation, spore resistance to heat, and spore coat formation were significantly up-regulated, and genes associated with sporulation-delaying protein were significantly down-regulated under L-arabinose treatment. The deletion of msmX, which is involved in arabinose transport in the Bacillus genus, decreased growth and sporulation by 53.71% and 86.46% compared with wild-type strain NCD-2, respectively. Complementing the mutant strain by importing an intact msmX gene restored the strain's growth and sporulation. [ABSTRACT FROM AUTHOR]

Plant disease control has long been a critical issue in agricultural production and relies heavily on the identification of plant diseases, but traditional disease identification requires extensive experience. Most of the existing deep learning-based plant disease classification methods run on high-performance devices to meet the requirements for classification accuracy. However, agricultural applications have strict cost control and cannot be widely promoted. This paper presents a novel method for plant disease classification using a binary neural network with dual attention (DABNN), which can save computational resources and accelerate by using binary neural networks, and introduces a dual-attention mechanism to improve the accuracy of classification. To evaluate the effectiveness of our proposed approach, we conduct experiments on the PlantVillage dataset, which includes a range of diseases. The F 1 s c o r e and A c c u r a c y of our method reach 99.39% and 99.4%, respectively. Meanwhile, compared to AlexNet and VGG16, the C o m p u t a t i o n a l c o m p l e x i t y of our method is reduced by 72.3% and 98.7%, respectively. The P a r a m s s i z e of our algorithm is 5.4% of AlexNet and 2.3% of VGG16. The experimental results show that DABNN can identify various diseases effectively and accurately. [ABSTRACT FROM AUTHOR]

Fusarium wilt, caused by Fusarium oxysporum, is one of the most notorious diseases of cash crops. The use of microbial fungicides is an effective measure for controlling Fusarium wilt, and the genus Bacillus is an important resource for the development of microbial fungicides. Fusaric acid (FA) produced by F. oxysporum can inhibit the growth of Bacillus, thus affecting the control efficacy of microbial fungicides. Therefore, screening FA-tolerant biocontrol Bacillus may help to improve the biocontrol effect on Fusarium wilt. In this study, a method for screening biocontrol agents against Fusarium wilt was established based on tolerance to FA and antagonism against F. oxysporum. Three promising biocontrol bacteria, named B31, F68, and 30833, were obtained to successfully control tomato, watermelon, and cucumber Fusarium wilt. Strains B31, F68, and 30833 were identified as B. velezensis by phylogenetic analysis of the 16S rDNA, gyrB, rpoB, and rpoC gene sequences. Coculture assays revealed that strains B31, F68, and 30833 showed increased tolerance to F. oxysporum and its metabolites compared with B. velezensis strain FZB42. Further experiments confirmed that 10 µg/mL FA completely inhibited the growth of strain FZB42, while strains B31, F68, and 30833 maintained normal growth at 20 µg/mL FA and partial growth at 40 µg/mL FA. Compared with strain FZB42, strains B31, F68, and 30833 exhibited significantly greater tolerance to FA. [ABSTRACT FROM AUTHOR]