Identification, pathogenicity, and fungicide sensitivity of Eutiarosporella dactylidis associated with leaf blight on maize in China

Maize(Zea mays L.) is an economically vital grain crop that is cultivated worldwide. In 2011, a maize foliar disease was detected in Lingtai and Lintao counties in Gansu Province, China. The characteristic signs and symptoms of this disease include irregular chlorotic lesions on the tips and edges of infected leaves and black punctate fruiting bodies in dead leaf tissues. Given favourable environmental conditions, this disease spread to areas surrounding Gansu. In this study, infected leaves were collected from Gansu and Ningxia Hui Autonomous Region between 2018and 2020 to identify the disease-causing pathogen. Based on morphological features, pathogenicity tests, and multilocus phylogenetic analysis involving internal transcribed spacer(ITS), 18S small subunit rDNA(SSU), 28S large subunit rDNA(LSU), translation elongation factor 1-alpha(TEF), and β-tubulin(TUB) sequences, Eutiarosporella dactylidis was identified as the causative pathogen of this newly discovered leaf blight. Furthermore, an in vitro bioassay was conducted on representative strains using six fungicides, and both fludioxonil and carbendazim were found to significantly inhibit the mycelial growth of E. dactylidis. The results of this study provide a reference for the detection and management of Eutiarosporella leaf blight.

Detection of maize leaf diseases using improved MobileNet V3-small

In order to realize the intelligent identification of maize leaf diseases for accurate prevention and control,this study proposed a maize disease detection method based on improved MobileNet V3-small,using a UAV to collect maize disease images and establish a maize disease dataset in a complex context,and explored the effects of data expansion and migration learning on model recognition accuracy,recall rate,and F1-score instructive evaluative indexes,and the results show that the two approaches of data expansion and migration learning effectively improved the accuracy of the model.The structured compression of MobileNet V3-small bneck layer retains only 6 layers,the expansion multiplier of each layer was redesigned,32-fold fast downsampling was used in the first layer,and the location of the SE module was optimized.The improved model had an average accuracy of 79.52%in the test set,a recall of 77.91%,an F1-score of 78.62%,a model size of 2.36 MB,and a single image detection speed of 9.02 ms.The detection accuracy and speed of the model can meet the requirements of mobile or embedded devices.This study provides technical support for realizing the intelligent detection of maize leaf diseases.

Anti-adhesive Property of Maize Leaf Surface Related with Temperature and Humidity

The anti-adhesive surfaces have always aroused great interest of worldwide scientists and engineers. But in practical ap- plications, it often faces the threat and impact of temperature and humidity. In this work, the excellent anti-adhesive perform- ance of maize leaf under high temperature and humidity were investigated in detail. Firstly, the adhesion forces of the maize leaf surface under different temperature and humidity were measured by using Atomic Force Microscopy （AFM）. The temperature of the substrate was varied between 23 ~C to 100 ~C, and the ambient relative humidity is from 18% to 100%. It was found that the adhesion force of maize leaf decreased with the increase of temperature and humidity. The mechanism of its excellent anti-adhesive performance of maize leaf under high temperature and relative humidity was revealed. The transverse and lon- gitudinal ridges on maize leaf surface interlace with each other, forming small air pockets, which reduces the actual contact area between the object and the maize leaf. With the increase of humidity, the liquid film will be formed in the air pockets gradually and so much water vapor is produced with increase of tempera^tre. Then the air flow rate increases though the wavy top of transverse ridges, inducing the dramatic decrease of adhesion force. Inspired by this mechanism, four samples with this bionic structure were made. This functional ＂biomimetic structure＂ would have potential value in the wide medical equipments such as high frequency electric knife with anti-adhesion surface under high temperature and high humidity.

Detecting maize leaf water status by using digital RGB images

To explore the correlation between crop leaf digital RGB(Red,Green and Blue)image features and the corresponding moisture content of the leaf,a Canon digital camera was used to collect image information from detached leaves of heading-stage maize.A drying method was adopted to measure the moisture content of the leaf samples,and image processing technologies,including gray level co-occurrence matrices and grayscale histograms,was used to extract the maize leaf texture feature parameters and color feature parameters.The correlations of these feature parameters with moisture content were analyzed.It is found that the texture parameters of maize leaf RGB images,including contrast,correlation,entropy and energy,were not significantly correlated with moisture content.Thus,it was difficult to use these features to predict moisture content.Of the six groups of eigenvalues for the leaf color feature parameters,including mean,variance,energy,entropy,kurtosis and skewness,mean and kurtosis were found to be correlated with moisture content.Thus,these features could be used to predict the leaf moisture content.The correlation coefficient(R2)of the mean-moisture content relationship model was 0.7017,and the error of the moisture content prediction was within±2%.The R2 of the kurtosis-moisture content relationship model was 0.7175,and the error of the moisture content prediction was within±1.5%.The study results proved that RGB images of crop leaves could be used to measure moisture content.

Maize leaf disease identification using deep transfer convolutional neural networks

Gray leaf spot,common rust,and northern leaf blight are three common maize leaf diseases that cause great economic losses to the worldwide maize industry.Timely and accurate disease identification can reduce economic losses,pesticide usage,and ensure maize yield and food security.Deep learning methods,represented by convolutional neural networks(CNNs),provide accurate,effective,and automatic diagnosis on server platforms when enormous training data is available.Restricted by dataset scale and application scenarios,CNNs are difficult to identify small-scale data sets on mobile terminals,while the lightweight networks,designed for the mobile terminal,achieve a better balance between efficiency and accuracy.This paper proposes a two-staged deep-transfer learning method to identify maize leaf diseases in the field.During the deep learning period,8 deep and 4 lightweight CNN models were trained and compared on the Plant Village dataset,and ResNet and MobileNet achieved test accuracy of 99.48%and 98.69%respectively,which were then migrated onto the field maize leave disease dataset collected on mobile phones.By using layer-freezing and fine-tuning strategies on ResNet and MobileNet,fine-tuned MobileNet achieved the best accuracy of 99.11%.Results confirmed that disease identification performance from lightweight CNNs was not inferior to that of deep CNNs and transfer learning training efficiency was higher when lacking training samples.Besides,the smaller gaps between source and target domains,the better the identification performance for transfer learning.This study provides an application example for maize disease identification in the field using deep-transfer learning and provides a theoretical basis for intelligent maize leaf disease identification from images captured with mobile devices.

Characterization of maize leaf pyruvate orthophosphate dikinase using high throughput sequencing

In C4 photosynthesis, pyruvate orthophosphate dikinase （PPDK） catalyzes the regeneration of phospho- enolpyruvate in the carbon shuttle pathway. Although the biochemical function of PPDK in maize is well characterized, a genetic analysis of PPDK has not been reported. In this study, we use the maize transposable elements Nlutator and Ds to generate multiple mutant alleles of PPDK. Loss-of- function mutants are seedling lethal, even when plants were grown under 2% CO2, and they show very low capacity for CO2 assimilation, indicating C4 photosynthesis is essential in maize. Using RNA-seq and GC-MS technologies, we exam- ined the transcriptional and metabolic responses to a deficiency in PPDK activity. These results indicate loss of PPDK results in downregulation of gene expression ofenzymes of the C4 cycle, the Calvin cycle, and components of photochemistry. Furthermore, the loss of PPDK did not change Kranz anatomy, indicating that this metabolic defect in the C4 cycle did not impinge on the morphological differentiation of C4 characters. However, sugar metabolism and nitrogen utilization were altered in the mutants. An interaction between light intensity and genotype was also detected from transcriptome profiling, suggesting altered transcriptional and metabolic responses to environmental and endogenous signals in the PPDK mutants.

QTL mapping for leaf area in maize (Zea mays L.) under multienvironments

Leaves are the main organs of photosynthesis in green plants. Leaf area plays a vital role in dry matter accumulation and grain yield in maize （Zea mays L.）. Thus, investigating the genetic basis of leaf area will aid efforts to breed maize with high yield. In this study, a total of 150 F7 recombinant inbred lines （RILs） derived from a cross between the maize lines Xu 178 and K12 were used to evaluate three ear-leaves area （TELA） under multi-environments. Inclusive composite interval map- ping （ICIM） was used to identify quantitative trait loci （QTLs） for TELA under a single environment and estimated breeding value （EBV）. A total of eight QTLs were detected under a single environmental condition, and four QTLs were identified for EBV which also can be detected in single environment. This indicated that the EBV-detected QTLs have high genetic stability. A major QTL （qTELA_2-9） located in chromosome bin 2.04/2.05 could be detected in four environments and has a high phenotypic contribution rate （ranging from 10.79 to 16.51%） that making it a good target for molecular breeding. In addition, joint analysis was used to reveal the genetic basis of leaf area in six environments. In total, six QTLxenvironment interactions and nine epistatic interactions were identified. Our results reveal that the genetic basis of the leaf area is not only mainly determined by additive effects, but also affected by epistatic effects environmental interaction effects.

Field Control Efficacy of Several Reagents against Northern Leaf Blight of Maize(Exserohilum turcicum)

[Objective] The paper was to screen suitable agents for controlling northern leaf blight of maize in field. [Method] Field efficacy test was conducted using the agents of propiconazole, azoxystrobin, propiconazole·azoxystrobin, coumoxystrobin·tebuconazole, coumoxystrobin·tebuconazole and azoxystrobin. [Result] At 14 d post secondary spraying, the control effects of 25% propionazole EC 168.8 g/hm^2, 25% azoxystrobin SC 300 g/hm^2, 18.7% propiconazole·azoxystrobin SC 196.4 g/hm^2, 40% coumoxystrobin·tebuconazole SC 180 m L/hm^2, 40% coumoxystrobin·tebuconazole SC 180 m L/hm^2＋ 25% azoxystrobin SC 300 g/hm^2 against northern leaf blight of maize were 77.35%, 77.11%, 78.13%, 74.60% and 80.94%, re-spectively. The agents were safe and harmless to maize. [Conclusion] Considering the production factors, 18.7% propiconazole·azoxystrobin SC196.4 g/hm^2 or 40% coumoxystrobin·tebuconazole SC 180 m L/hm^2＋ 25% azoxystrobin SC 300 g/hm^2 can be sprayed in production before the incidence or in the initial incidence of northern leaf blight of maize.