Plant stress is one of major issues that cause significant economic loss for growers. The labor-intensive conventional methods for identifying the stressed plants constrain their applications. To ...address this issue, rapid methods are in urgent needs. Developments of advanced sensing and machine learning techniques trigger revolutions for precision agriculture based on deep learning and big data. In this paper, we reviewed the latest deep learning approaches pertinent to the image analysis of crop stress diagnosis. We compiled the current sensor tools and deep learning principles involved in plant stress phenotyping. In addition, we reviewed a variety of deep learning applications/functions with plant stress imaging, including classification, object detection, and segmentation, of which are closely intertwined. Furthermore, we summarized and discussed the current challenges and future development avenues in plant phenotyping.
This study developed and field tested an automated weed mapping and variable-rate herbicide spraying (VRHS) system for row crops. Weed detection was performed through a machine vision sub-system that ...used a custom threshold segmentation method, an improved particle swarm optimum (IPSO) algorithm, capable of segmenting the field images. The VRHS system also used a lateral histogram-based algorithm for fast extraction of weed maps. This was the basis for determining real-time herbicide application rates. The central processor of the VRHS system had high logic operation capacity, compared to the conventional controller-based systems. Custom developed monitoring system allowed real-time visualization of the spraying system functionalities. Integrated system performance was then evaluated through field experiments. The IPSO successfully segmented weeds within corn crop at seedling growth stage and reduced segmentation error rates to 0.1% from 7.1% of traditional particle swarm optimization algorithm. IPSO processing speed was 0.026 s/frame. The weed detection to chemical actuation response time of integrated system was 1.562 s. Overall, VRHS system met the real-time data processing and actuation requirements for its use in practical weed management applications.
Deep learning is thought of as a promising mean to identify maize diseases. However, the drawback of deep learning is the huge sample data and low accuracy. In this paper, we proposed a multi-scale ...convolutional global pooling neural network to improve the accuracy of maize diseases identification. Firstly, on the basis of the AlexNet model, a convolutional layer and new Inception module are added to enhance the ability of AlexNet features extraction. Then, in order to avoid the over-fitting problem caused by too many parameters, we use the global pooling layer to replace the original fully-connected layer. Besides, we also adopt the transfer learning method to solve the over-fitting problem caused by insufficient sample data. The improved model can reduce over-fitting and epochs to enhance the performance of maize diseases recognition. From the considerable experimental results, we can conclude that the proposed model has better performance compared with convolutional neural network models VGGNet-16, DenseNet, ResNet-50 and AlexNet in recognition accuracy.
Moisture content is an important index to assess the grain quality and food processing conditions. A measurement system based on the traveling–standing microwave attenuation method is designed for a ...fast and nondestructive grain moisture content determination. The proposed system consists of a microwave cavity oscillator, microwave transmitting and receiving horn antennas, microwave detector, slide rail, sample container, weight sensor, temperature sensor, and controller. The traveling–standing wave caused by free space microwave multiple reflection is discussed. The moisture content calibration functions eliminated the interference of bulk density and temperature are proposed based on the attenuation of the maximum field strength of the transmission traveling–standing wave. The moisture content of rice, which ranges from 10.75% to 27.62%, is obtained with a standard error of prediction (SEP) of 0.586% and a coefficient of determination (R2) of 0.988, whereas the moisture content of corn, which ranges from 7.72% to 24.46%, is obtained with a SEP of 0.340% and R2 of 0.991. The main results might provide technical support for the development of accurate and intelligent grain quality detection equipment.
The aim of this study was to investigate the genomic epidemiology of MRSA in China to identify predominant lineages and their associated genomic and phenotypic characteristics. In this study, we ...conducted whole-genome sequencing on 565 MRSA isolates from 7 provinces and municipalities of China between 2014 and 2020. MRSA isolates were subjected to MLST, spa typing, SCCmec typing, analysis of virulence determinants and antimicrobial susceptibility testing. Among 565 MRSA isolates tested, clonal complex (CC) 59 (31.2%), CC5 (23.4%) and CC8 (13.63%) were the major lineages, and the clonal structure was dominated by ST59-t437-IV (14.9%), ST239-t030-III (6.4%) and ST5-t2460-II (6.0%), respectively. Of note, CC8, the predominant lineage in 2014-2015, was replaced by CC59 after 2016. Interestingly, the extension and unstable structure of the CC5 population was observed, with ST5-t311-II, ST764-t1084-II, ST5-t2460-II and ST764-t002-II existing complex competition. Further analysis revealed that virulence determinant profiles and antibiograms were closely associated with the clonal lineage. The CC59 MRSA was less resistant to most tested antimicrobials and carried fewer resistance determinants. But rifampicin resistance and mupirocin resistance were closely linked with CC8 and CC5, respectively. MRSA isolates conservatively carried multiple virulence genes involved in various functions. PVL encoding genes were more common in ST338, CC30, CC398, ST8 and CC22, while tsst-1 was associated with ST5. In conclusion, the community-associated CC59-ST59-t437-IV lineage was predominant in China, with diverse clonal isolates alternately circulating in various geographical locations. Our study highlights the need for MRSA surveillance in China to monitor changes in MRSA epidemiology.
Carbapenem-resistant Enterobacterales (CRE) infection is highly endemic in China; Klebsiella pneumoniae carbapenemase (KPC) 2–producing CRE is the most common, whereas KPC-3–producing CRE is rare. We ...report an outbreak of KPC-3–producing Enterobacterales infection in China. During August 2020–June 2021, 25 blaKPC-3–positive Enterobacteriale isolates were detected from 24 patients in China. Whole-genome sequencing analysis revealed that the blaKPC-3 genes were harbored by IncX8 plasmids. The outbreak involved clonal expansion of KPC-3–producing Serratia marcescens and transmission of blaKPC-3 plasmids across different species. The blaKPC-3 plasmids demonstrated high conjugation frequencies (10−3 to 10−4). A Galleria mellonella infection model showed that 2 sequence type 65 K2 K. pneumoniae strains containing blaKPC-3 plasmids were highly virulent. A ceftazidime/avibactam in vitro selection assay indicated that the KPC-3–producing strains can readily develop resistance. The spread of blaKPC-3–harboring IncX8 plasmids and these KPC-3 strains should be closely monitored in China and globally.
The rate of fluoroquinolone (FQ) resistance among carbapenem-resistant Klebsiella pneumoniae (CRKP) is high. The present study aimed to investigate the distribution of fluoroquinolone resistance ...determinants in clinical CRKP isolates associated with bloodstream infections (BSIs). A total of 149 BSI-associated clinical CRKP isolates collected from 11 Chinese teaching hospitals from 2015 to 2018 were investigated for the prevalence of fluoroquinolone resistance determinants, including plasmid-mediated quinolone resistance (PMQR) genes and spontaneous mutations in the quinolone resistance-determining regions (QRDRs) of the gyrA and parC genes. Among these 149 clinical CRKP isolates, 117 (78.5%) exhibited resistance to ciprofloxacin. The GyrA substitutions (Ser83 right arrow IIe/Phe) and (Asp87 right arrow Gly/Ala) were found among 112 (75.2%) of 149 isolates, while the substitution (Ser80 right arrow IIe) of ParC was found in 111 (74.5%) of the 149 isolates. In total, 70.5% (105/149) of the CRKP isolates had at least two mutations within gyrA as well as a third mutation in parC. No mutations in the QRDRs were found in 31 ciprofloxacin susceptible CRKP isolates. Eighty-nine (56.9%) of 149 were found to carry PMQR genes including qnrS1 (43.0%), aac(6')-Ib-cr (16.1%), qnrB4 (6.0%), qnrB2 (2.7%), and qnrB1 (1.3%). Nine isolates contained two or more PMQR genes, with one carrying four aac(6')-Ib-cr, qnr-S1, qnrB2, and qnrB4. The co-existence rate of PMQR determinants and mutations in the QRDRs of gyrA and parC reached 68.5% (61/89). Seventy-four (83.1%, 74/89) PMQR-positive isolates harbored extended-spectrum beta-lactamase (ESBL)-encoding genes. Multilocus sequence typing (MLST) analysis demonstrated that the ST11 was the most prevalent STs in our study. Mutations in the QRDRs of gyrA and parC were the key factors leading to the high prevalence of fluoroquinolone resistance among BSI-associated CRKP. The co-existence of PMQR genes and mutations in the QRDRs can increase the resistance level of CRKP to fluoroquinolones in clinical settings. ST11 CRKP isolates with identical QRDR substitution patterns were found throughout hospitals in China.
To solve the problem of the low efficiency of traditional lettuce freshness classification methods and sample damage, we proposed an automatic lettuce freshness classification method based on ...improved deep residuals convolutional neural network (Im-ResNet). We built an image acquisition system to obtain the freshness classification dataset of lettuce leaves. For improving the classification accuracy, we developed an image acquisition system for curating the freshness of lettuce leaves. Then, we proposed a novel method that was derived from the existing ResNet-50 (which uses ReLU activation function) known as Improved Residual Networks (Im-ResNet): the new method factored extra convolutional layer, pooling layer, fully-connected layers, and a random ReLU (RReLU) activation function. We also performed the corresponding experiments using the Im-ResNet network compared with four network architectures (AlexNet, GoogleNet, VGG16 and ResNet50). The experimental results showed that the proposed network had more significant advantages in the recognition accuracy and loss value of lettuce freshness compared with the traditional deep networks. The recognition accuracy of the validation set of the proposed model can reach to 95.60%. Different from the physical and chemical methods, our scheme can automatically and non-destructively classify the freshness of lettuce.
Currently, the most efficient method of resolving the pollution problem of weed management is by using variable spraying technology. In this study, an improved genetic ...proportional-integral-derivative control algorithm (IGA-PID) was developed for this technology. It used a trimmed mean operator to optimize the selection operator for an improved searching rate and accuracy. An adaptive crossover operator and mutation operator were constructed for a rapid convergence speed. The weed density detection was performed through an image acquisition and processing subsystem which was capable of determining the spraying quantity. The variable spraying control sub-system completed variable spraying operation. The performance of the system was evaluated by simulations and field tests, and compared with conventional methods. The simulation results indicated that the parameters of the overshoot (1.25%), steady-state error (1.21%) and the adjustment time (0.157s) of IGA-PID were the lowest when compared with the standard algorithms. Furthermore, the field validation results showed that the system with the proposed algorithm achieved the optimal performance with spraying quantity error being 2.59% and the respond time being 3.84s. Overall, the variable spraying system based on an IGA-PID meets the real-time and accuracy requirements for field applications which could be helpful for weed management in precise agriculture.
Many antimicrobial resistance genes usually located on transferable plasmids are responsible for multiple antimicrobial resistance among multidrug-resistant (MDR) Gram-negative bacteria. The aim of ...this study is to characterize a carbapenemase-producing
Enterobacter hormaechei
1575 isolate from the blood sample in a tertiary hospital in Wuhan, Hubei Province, China. Antimicrobial susceptibility test showed that 1575 was an MDR isolate. The whole genome sequencing (WGS) and comparative genomics were used to deeply analyze the molecular information of the 1575 and to explore the location and structure of antibiotic resistance genes. The three key resistance genes (
bla
SFO–1
,
bla
NDM–1
, and
mcr-9
) were verified by PCR, and the amplicons were subsequently sequenced. Moreover, the conjugation assay was also performed to determine the transferability of those resistance genes. Plasmid files were determined by the S1 nuclease pulsed-field gel electrophoresis (S1-PFGE). WGS revealed that p1575-1 plasmid was a conjugative plasmid that possessed the rare coexistence of
bla
SFO–1
,
bla
NDM–1
, and
mcr-9
genes and complete conjugative systems. And p1575-1 belonged to the plasmid incompatibility group IncHI2 and multilocus sequence typing ST102. Meanwhile, the pMLST type of p1575-1 was IncHI2-ST1. Conjugation assay proved that the MDR p1575-1 plasmid could be transferred to other recipients. S1-PFGE confirmed the location of plasmid with molecular weight of 342,447 bp. All these three resistant genes were flanked by various mobile elements, indicating that the
bla
SFO–1
,
bla
NDM–1
, and
mcr-9
could be transferred not only by the p1575-1 plasmid but also by these mobile elements. Taken together, we report for the first time the coexistence of
bla
SFO–1
,
bla
NDM–1
, and
mcr-9
on a transferable plasmid in a MDR clinical isolate
E. hormaechei
, which indicates the possibility of horizontal transfer of antibiotic resistance genes.
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