Anatomical Study on Maize Filaments Infected by Fusarium proliferatum

Conidia of Fusarium proliferatum infected epidermal cells of stigma, hairs and filaments on maize when sprayed on maize filament. Hyphae of F. proliferatum extended along with the parenehyma and vascular cells in the filaments, and infected toward ovary. Both the parenchyma cells and the epidermal cells were wrinkled, and the filament was deformed. The result showed that F. proliferatum could infect filaments directly and cause maize ear rot through filament channel.

Dynamic detection method for falling ears of maize harvester based on improved YOLO-V4

Traditional maize ear harvesters mainly rely on manual identification of fallen maize ears,which cannot realize real-time detection of ear falling.The improved You Only Look Once-V4(YOLO-V4)algorithm is combined with the channel pruning algorithm to detect the dropped ears of maize harvesters.K-means clustering algorithm is used to obtain a prior box matching the size of the dropped ears,which improves the Intersection Over Union(IOU).Compare the effect of different activation functions on the accuracy of the YOLO-V4 model,and use the Mish activation function as the activation function of this model.Improve the calculation of the regression positioning loss function,and use the CEIOU loss function to balance the accuracy of each category.Use improved Adam optimization function and multi-stage learning optimization technology to improve the accuracy of the YOLO-V4 model.The channel pruning algorithm is used to compress the model and distillation technology is used in the fine-tuning of the model.The final model size was only 10.77%before compression,and the test set mean Average Precision(mAP)was 93.14%.The detection speed was 112 fps,which can meet the need for real-time detection of maize harvester ears in the field.This study can provide technical reference for the detection of the ear loss rate of intelligent maize harvesters.

A field-deployable method for single and multiplex detection of DNA or RNA from pathogens using Cas12 and Cas13

For some Cas nucleases,trans-cleavage activity triggered by CRISPR/Cas-mediated cis-cleavage upon target nucleic acid recognition has been explored for diagnostic detection.Portable single and multiplex nucleic acid-based detection is needed for crop pathogen management in agriculture.Here,we harnessed and characterized RfxCas13d as an additional CRISPR/Cas nucleic acid detection tool.We systematically characterized AsCas12a,LbCas12a,LwaCas13a,and RfxCas13d combined with isothermal amplification to develop a CRISPR/Cas nucleic acid-based tool for single or multiplex pathogen detection.Our data indicated that sufficient detection sensitivity was achieved with just a few copies of DNA/RNA targets as input.Using this tool,we successfully detected DNA from Fusarium graminearum and Fusarium verticillioides and RNA from rice black-streaked dwarf virus in crude extracts prepared in the field.Our method,from sample preparation to result readout,could be rapidly and easily deployed in the field.This system could be extended to other crop pathogens,including those that currently lack a detection method and have metabolite profiles that make detection challenging.This nucleic acid detection system could also be used for single-nucleotide polymorphism genotyping,transgene detection,and qualitative detection of gene expression in the field.