Types of Maize Virus Diseases and Progress in Virus Identification Techniques in China

There are a total of more than 40 reported maize viral diseases worldwide. Five of them have reportedly occurred in China. They are maize rough dwarf disease, maize dwarf mosaic disease, maize streak dwarf disease, maize crimson leaf disease, maize wallaby ear disease and corn lethal necrosis disease. This paper reviewed their occurrence and distribution as well as virus identification techniques in order to provide a basis for virus identification and diagnosis in corn production.

The influence of potassium to mineral fertilizers on the maize health

Field experiments （2009-2011） were conducted at the Department of Agronomy at Poznar~ University of Life Sciences on the fields of the Research Institute in Swadzim. We evaluated the health of maize plants of two types, depending on the variations in mineral fertilization. The conducted research recorded the occurrence of pests such as oscinella frit （Oscinella frit L.） and the European corn borer （Pyrausta nubilalis Hbn.）. Diseases recorded during the research included two patho- genes： Fusarium （Fusarium ssp.） and corn smut （Ustilago maydis Corda）. It was shown that the meteorological conditions during the maize vegetation had a significant influence on the occurrence of pests. Adding potassium to mineral fertilizers increased the maize resistance to Fusarium. Cultivation of ＂stay-green＂ cultivar shall be considered as an element of in- tegrated maize protection. The occurrence of oscineUa flit was correlated with the occurrence of Fusarium as well as the occurrence of the European corn borer for both examined cultivars.

Pathogenicity of Klebsiella pneumonia(KpC4)infecting maize and mice

Recently, a new bacterial top rot disease of maize has frequently appeared in many areas of Yunnan Province, China. The pathogen of the disease was identified as Klebsiella pneumoniae （KpC4）, which is well known to cause pulmonary and urinary diseases in humans and animals and occasionally exists as a harmless endophyte in plants. To evaluate the viru- lence of the maize pathogen to maize and mice, we inoculated maize and mice with routine inoculation and intraperitoneal injection respectively according to Koch＇s postulates. The results showed that KpC4 and the clinical strain K. pneumoniae 138 （Kp138） were all highly pathogenic to maize and mice and the strain re-isolated from diseased mice also caused typical top rot symptoms on maize by artificial inoculation. It is highlighting that a seemingly dedicated human/animal pathogen could cause plant disease. This is the first report of K. pneumoniae, an opportunistic pathogen of human/animal, could infect maize and mice. The findings serve as an alert to plant, medical and veterinarian scientists regarding a potentially dangerous bacterial pathogen infecting both plants and animals/humans. The maize plants in the field could serve as a reservoir for K. pneumoniae which might infect animals and probably humans when conditions are favorable. The new findings not only are significant in the developing control strategy for the new disease in Yunnan, but also serve as a starting point for further studies on the mechanism of pathogenesis and epidemiology of K. pneumoniae.

Recent progress in maize lethal necrosis disease:From pathogens to integrated pest management

Maize(Zea mays),as a staple food and an important industrial raw material,has been widely cultivated for centuries especially by smallholder farmers.Maize lethal necrosis disease(MLND)is a serious disease infecting maize,which caused devastating damage in the African region recently.MLND is induced by co-infection of maize chlorotic mottle virus and one of several cereal-infecting viruses in the Potyviridae family,with the symptoms ranging from chlorotic mottle to plant death at different infection stages.Integrated pest management for MLND needs strengthening detection,focusing on prevention and effective control.Early detection system of MLND has been successfully established by serological methods,nucleic acid-based methods,next-generation sequencing,etc.The practices,such as using certified seeds,sanitary measures,crop rotation,tolerant or resistant varieties etc.,have been considered as the effective,economical and eco-friendly way to prevent and control MLND.

Disease Grading Criterion and Assessment of Yield Loss Caused by Maize Rough Dwarf Disease

[Objective] The paper was to study the disease grading criterion and assess the yield loss caused by maize rough dwarf disease. [Method] The ear lengths and yields of each healthy and infected plant of 5 cultivars were measured during 2009 and 2010. The severity grading criterion was deduced according to the ear length ratios. [Result]When the ratios were 0.92-1.00, 0.67-0.91, 0.41-0.66, 0.10-0.40 and 0, its corresponding disease grading criterions were 0, 1, 3, 5 and 7, respectively. The severity grading criterion was closely correlated to the yield loss. By analyzing the data of disease indexes and yield loss rates of 27 cultivars with DPS （Data Processing System）, the regression equations were established respectively. According to the comparison with each other, the Weibull Model was proved to have the highest fitting degree. Validating with the disease indexes of 27 cultivars in 2010, the equation supported the feasibility of the equation to predict the yield loss caused by maize rough dwarf disease. [Conclusion] The paper provided theoretical basis for further study on maize rough dwarf disease.

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.

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.