Vegetation Indices (VIs) obtained from remote sensing based canopies are quite simple and effective algorithms for quantitative and qualitative evaluations of vegetation cover, vigor, and growth ...dynamics, among other applications. These indices have been widely implemented within RS applications using different airborne and satellite platforms with recent advances using Unmanned Aerial Vehicles (UAV). Up to date, there is no unified mathematical expression that defines all VIs due to the complexity of different light spectra combinations, instrumentation, platforms, and resolutions used. Therefore, customized algorithms have been developed and tested against a variety of applications according to specific mathematical expressions that combine visible light radiation, mainly green spectra region, from vegetation, and nonvisible spectra to obtain proxy quantifications of the vegetation surface. In the real-world applications, optimization VIs are usually tailored to the specific application requirements coupled with appropriate validation tools and methodologies in the ground. The present study introduces the spectral characteristics of vegetation and summarizes the development of VIs and the advantages and disadvantages from different indices developed. This paper reviews more than 100 VIs, discussing their specific applicability and representativeness according to the vegetation of interest, environment, and implementation precision. Predictably, research, and development of VIs, which are based on hyperspectral and UAV platforms, would have a wide applicability in different areas.
•A machine learning model was developed to estimate stem water potential from UAV.•An additional model was developed to detect three levels of water stress.•Models were developed at pixel-by-pixel ...and plant by plant scales.•Spatial distribution maps were constructed for a grapevine region for irrigation.•High accuracy models developed could assist irrigation and grapevine management.
Remote sensing can provide a fast and reliable alternative for traditional in situ water status measurement in vineyards. Several vegetation indices (VIs) derived from aerial multispectral imagery were tested to estimate midday stem water potential (Ψstem) of grapevines. The experimental trial was carried out in a vineyard in the Shangri-La region, located in Yunnan province in China. Statistical methods and machine learning algorithms were used to evaluate the correlations between Ψstem and VIs. Results by simple regression between VIs individually and Ψstem showed no significant relationships, with coefficient of determination (R2) for linear fitting smaller than 0.3 for almost all the indices studied, except for the Optimal Soil Adjusted Vegetation Index (OSAVI); R2 = 0.42 with statistical significance (p ≤ 0.001). However, results from a model obtained by fitting using Artificial Neural Network (ANN), using all VIs calculated as inputs and real Ψstem from plants within the study site (n = 90) as targets (Model 1), showed high correlation between the estimated water potential through ANN (Ψstem ANN) and the actual measured Ψstem. Training, validation and testing data sets presented individual correlations of R = 0.8, 0.72 and 0.62 respectively. The models obtained from the study site were then applied to a wider area from the vineyard studied and compared to further Ψstem measured obtained from different sites (n = 23) showing high correlation values between Ψstem ANN and real Ψstem (R2 = 0.83; slope = 1; p ≤ 0.001). Finally, a pattern recognition ANN model (Model 2) was developed for irrigation scheduling purposes using the same Ψstem measured in the study site as inputs and with the following thresholds as outputs: Ψstem below −1.2 MPa considered as severe water stress (SS), Ψstem between −0.8 to −1.2 MPa as moderate stress (MS) and Ψstem over −0.8 MPa with no water stress (NS). This model can be applied to analyze on a plant by plant basis to identify sectors of stress within the vineyard for optimal irrigation management and to identify spatial variability within the vineyards.
Wheat stripe rust is one of the main wheat diseases worldwide, which has significantly adverse effects on wheat yield and quality, posing serious threats on food security. Disease severity grading ...plays a paramount role in stripe rust disease management including breeding disease-resistant wheat varieties. Manual inspection is time-consuming, labor-intensive and prone to human errors, therefore, there is a clearly urgent need to develop more effective and efficient disease grading strategy by using automated approaches. However, the differences between wheat leaves of different levels of stripe rust infection are usually tiny and subtle, and, as a result, ordinary deep learning networks fail to achieve satisfying performance. By formulating this challenge as a fine-grained image classification problem, this study proposes a novel deep learning network C-DenseNet which embeds Convolutional Block Attention Module (CBAM) in the densely connected convolutional network (DenseNet). The performance of C-DenseNet and its variants is demonstrated
via
a newly collected wheat stripe rust grading dataset (WSRgrading dataset) at Northwest A&F University, Shaanxi Province, China, which contains a total of 5,242 wheat leaf images with 6 levels of stripe rust infection. The dataset was collected by using various mobile devices in the natural field condition. Comparative experiments show that C-DenseNet with a test accuracy of 97.99% outperforms the classical DenseNet (92.53%) and ResNet (73.43%). GradCAM++ network visualization also shows that C-DenseNet is able to pay more attention to the key areas in making the decision. It is concluded that C-DenseNet with an attention mechanism is suitable for wheat stripe rust disease grading in field conditions.
CRISPR/Cas9-based gene knockout in animal cells, particularly in teleosts, has proven to be very efficient with regards to mutation rates, but the precise insertion of exogenous DNA or gene knock-in ...via the homology-directed repair (HDR) pathway has seldom been achieved outside of the model organisms. Here, we succeeded in integrating with high efficiency an exogenous alligator cathelicidin gene into a targeted non-coding region of channel catfish (Ictalurus punctatus) chromosome 1 using two different donor templates (synthesized linear dsDNA and cloned plasmid DNA constructs). We also tested two different promoters for driving the gene, zebrafish ubiquitin promoter and common carp β-actin promoter, harboring a 250-bp homologous region flanking both sides of the genomic target locus. Integration rates were found higher in dead fry than in live fingerlings, indicating either off-target effects or pleiotropic effects. Furthermore, low levels of mosaicism were detected in the tissues of P
individuals harboring the transgene, and high transgene expression was observed in the blood of some P
fish. This can be an indication of the localization of cathelicidin in neutrophils and macrophage granules as also observed in most antimicrobial peptides. This study marks the first use of CRISPR/Cas9 HDR for gene integration in channel catfish and may contribute to the generation of a more efficient system for precise gene integration in catfish and other aquaculture species, and the development of gene-edited, disease-resistant fish.
High throughput phenotyping (HTP) for wheat (Triticum aestivum L.) stay green (SG) is expected in field breeding as SG is a beneficial phenotype for wheat high yield and environment adaptability. The ...RGB and multispectral imaging based on the unmanned aerial vehicle (UAV) are widely popular multi-purpose HTP platforms for crops in the field. The purpose of this study was to compare the potential of UAV RGB and multispectral images (MSI) in SG phenotyping of diversified wheat germplasm. The multi-temporal images of 450 samples (406 wheat genotypes) were obtained and the color indices (CIs) from RGB and MSI and spectral indices (SIs) from MSI were extracted, respectively. The four indices (CIs in RGB, CIs in MSI, SIs in MSI, and CIs + SIs in MSI) were used to detect four SG stages, respectively, by machine learning classifiers. Then, all indices’ dynamics were analyzed and the indices that varied monotonously and significantly were chosen to calculate wheat temporal stay green rates (SGR) to quantify the SG in diverse genotypes. The correlations between indices’ SGR and wheat yield were assessed and the dynamics of some indices’ SGR with different yield correlations were tracked in three visual observed SG grades samples. In SG stage detection, classifiers best average accuracy reached 93.20–98.60% and 93.80–98.80% in train and test set, respectively, and the SIs containing red edge or near-infrared band were more effective than the CIs calculated only by visible bands. Indices’ temporal SGR could quantify SG changes on a population level, but showed some differences in the correlation with yield and in tracking visual SG grades samples. In SIs, the SGR of Normalized Difference Red-edge Index (NDRE), Red-edge Chlorophyll Index (CIRE), and Normalized Difference Vegetation Index (NDVI) in MSI showed high correlations with yield and could track visual SG grades at an earlier stage of grain filling. In CIs, the SGR of Normalized Green Red Difference Index (NGRDI), the Green Leaf Index (GLI) in RGB and MSI showed low correlations with yield and could only track visual SG grades at late grain filling stage and that of Norm Red (NormR) in RGB images failed to track visual SG grades. This study preliminarily confirms the MSI is more available and reliable than RGB in phenotyping for wheat SG. The index-based SGR in this study could act as HTP reference solutions for SG in diversified wheat genotypes.
Accurately detecting and segmenting grape cluster in the field is fundamental for precision viticulture. In this paper, a new backbone network, ResNet50-FPN-ED, was proposed to improve Mask R-CNN ...instance segmentation so that the detection and segmentation performance can be improved under complex environments, cluster shape variations, leaf shading, trunk occlusion, and grapes overlapping. An Efficient Channel Attention (ECA) mechanism was first introduced in the backbone network to correct the extracted features for better grape cluster detection. To obtain detailed feature map information, Dense Upsampling Convolution (DUC) was used in feature pyramid fusion to improve model segmentation accuracy. Moreover, model generalization performance was also improved by training the model on two different datasets. The developed algorithm was validated on a large dataset with 682 annotated images, where the experimental results indicate that the model achieves an Average Precision (AP) of 60.1% on object detection and 59.5% on instance segmentation. Particularly, on object detection task, the AP improved by 1.4% and 1.8% over the original Mask R-CNN (ResNet50-FPN) and Faster R-CNN (ResNet50-FPN). For the instance segmentation, the AP improved by 1.6% and 2.2% over the original Mask R-CNN and SOLOv2. When tested on different datasets, the improved model had high detection and segmentation accuracy and inter-varietal generalization performance in complex growth environments, which is able to provide technical support for intelligent vineyard management.
Olive flounder (
) is an important economical flatfish in Japan, Korea, and China, but its production has been greatly threatened by disease outbreaks. In this research, we aimed to explore the ...immune responsive mechanism of
against
infection by profiling the expression of circRNA, miRNA, and mRNA by RNA-seq and constructing a regulatory circular circRNA-miRNA-mRNA network. Illumina sequencing of samples from normal control (H0), 2 h (H2), 8 h (H8), and 12 h (H12) post-challenge was conducted. Differentially expressed (DE) circRNA (DE-circRNAs), miRNAs (DE-miRNAs), and mRNAs differential expression genes (DEGs) between challenge and control groups were identified, resulting in a total of 62 DE-circRNAs, 39 DE-miRNAs, and 3,011 DEGs. Based on the differentially expressed gene results, miRNA target interactions (circRNA-miRNA pairs and miRNA-mRNA pairs) were predicted by MiRanda software. Once these paired were combined, a preliminary circRNA-miRNA-mRNA network was generated with 198 circRNA-miRNA edges and 3,873 miRNA-mRNA edges, including 44 DE-circRNAs, 32 DE-miRNAs, and 1,774 DEGs. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis was performed to evaluate the function of the DEGs in this network, and we focused and identified two important intestinal immune pathways (herpes simplex infection and intestinal immune network for IgA production) that showed statistical significance between the challenge and control groups. Furthermore, three critical DEGs (nectin2, MHC II α-chain, and MHC II β-chain) were identified, mapped into the preliminary circRNA-miRNA-mRNA network, and new circRNA-miRNA-mRNA regulatory networks were constructed. In conclusion, we, for the first time, identified circRNA-miRNA-mRNA network from
in the pathogenesis of
and provided valuable resources for further analyses of the molecular mechanisms and signaling networks.
Disease and pest detection of grape foliage is essential for grape yield and quality. RGB image (RGBI), multispectral image (MSI), and thermal infrared image (TIRI) are widely used in the health ...detection of plants. In this study, we collected three types of grape foliage images with six common classes (anthracnose, downy mildew, leafhopper, mites, viral disease, and healthy) in the field. ShuffleNet V2 was used to build up detection models. According to the accuracy of RGBI, MSI, TIRI, and multi-source data concatenation (MDC) models, and a multi-source data fusion (MDF) decision-making method was proposed for improving the detection performance for grape foliage, aiming to enhance the decision-making for RGBI of grape foliage by fusing the MSI and TIRI. The results showed that 40% of the incorrect detection outputs were rectified using the MDF decision-making method. The overall accuracy of MDF model was 96.05%, which had improvements of 2.64%, 13.65%, and 27.79%, compared with the RGBI, MSI, and TIRI models using label smoothing, respectively. In addition, the MDF model was based on the lightweight network with 3.785 M total parameters and 0.362 G multiply-accumulate operations, which could be highly portable and easy to be applied.
, an important aquaculture species, has been widely cultured in East Asian countries. With the increase in the cultivation scale, various diseases have become major threats to the industry. Evidence ...has shown that non-coding RNAs (ncRNAs) have remarkable functions in the interactions between pathogens and their hosts. However, little is known about the mechanisms of circular RNAs (circRNAs) and coding RNAs in the process of preventing pathogen infection in the intestine in teleosts. In this study, we aimed to uncover the global landscape of mRNAs, circRNAs, and microRNAs (miRNAs) in response to
infection at different time points (0, 2, 6, 12, and 24 h) and to construct regulatory networks for exploring the immune regulatory mechanism in the intestine of
In total, 1,794 mRNAs, 87 circRNAs, and 79 miRNAs were differentially expressed. The differentially expressed RNAs were quantitatively validated using qRT-PCR. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis showed that most of the differentially expressed mRNA genes and the target genes of ncRNAs were related to immune signaling pathways, such as the NF-κB signal pathway, pathogen recognition receptors related to signaling pathways (Toll-like receptors and Nod-like receptors), and the chemokine signaling pathway. Based on these differentially expressed genes, 624 circRNA-miRNA pairs and 2,694 miRNA-mRNA pairs were predicted using the miRanda software. Integrated analyses generated 25 circRNA-miRNA-mRNA interaction networks. In a novel_circ_0004195/novel-530/
interaction network, novel_530 was upregulated, while its two targets, novel_circ_0004195 and
, were downregulated after
infection. In addition, two circRNA-miRNA-mRNA networks related to apoptosis (novel_circ_0003210/novel_152/apoptosis-stimulating of p53 protein 1) and interleukin (novel_circ_0001907/novel_127/interleukin-1 receptor type 2) were also identified in our study. We thus speculated that the downstream NF-κB signaling pathway, p53 signaling pathway, and apoptosis pathway might play vital roles in the immune response in the intestine of
This study revealed a landscape of RNAs in the intestine of
during
infection and provided clues for further study on the immune mechanisms and signaling networks based on the
in
.
TLRs (Toll-like receptors) are very important pathogen pattern recognition receptors, which control the host immune responses against pathogens through recognition of molecular patterns specific to ...microorganisms. In this regard, investigation of the turbot TLRs could help to understand the immune responses for pathogen recognition. Here, transcripts of two TLR5 (TLR5a and TLR5b) were captured, and their protein structures were also predicted. Meanwhile, we characterized their expression patterns with emphasis on mucosal barriers following different bacterial infection. The phylogenetic analysis revealed the turbot TLR5 genes showed the closest relationship to Paralichthys olivaceus. These two TLR5 genes were ubiquitously expressed in healthy tissues although expression levels varied among the tested tissues. In addition, the two copies of turbot TLR5 showed different expression patterns after bacterial infections. After Vibrio anguillarum infection, TLR5a was generally up-regulated in intestine and skin while down-regulated in gill, while TLR5b showed a general down-regulation in mucosal tissues. After Streptococcus iniae infection, the TLR5a was down-regulated at 2 h while generally up-regulated after 4 h in mucosal tissues. Interestingly, the TLR5b was up-regulated in intestine while down-regulated in skin and gill after Streptococcus iniae infection. These findings suggested a possible irreplaceable role of TLR5 in the immune responses to the infections of a broad range of pathogens that include Gram-negative and Gram-positive bacteria. Future studies should apply the bacteriological and immune-histochemical techniques to study the main sites on the mucosal tissue for bacteria entry and identify the ligand specificity of the turbot TLRs after challenge.
•TLR5 genes are homologous to their counterparts in other vertebrates.•TLR5 genes were ubiquitously expressed in turbot tissues.•TLR5 genes showed different expression patterns following different bacterial challenge.
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