Visual inspection of rice leaf blast resistance is time-consuming and labor-intensive with low accuracy. Therefore, this study aims to identify and detect rice leaf blast spots based on RGB imaging of rice canopy combined with mask regions with convolutional neural network (Mask-RCNN), and develop multiple classification models to quantify the number of disease spots and evaluate the association between the number of disease spots and the resistance level by analyzing the quantitative information of different categories of disease spots in RGB images of rice. First, we collected RGB images from different rice breeding lines at the seedling stage, including japonica lines, early indica lines and indica recovery lines. Preprocessing and labeling of the input images were then performed. A Mask-RCNN model for the recognition of rice leaf blast spots was developed to perform the rectangular frame detection, mask segmentation and classification. The classification result of rice leaf blast spots with the mean intersection over union (mIoU) of 0.603 was achieved. The mean average precision (mAP) of the test dataset was 0.716, when the intersection over union (IoU) threshold of 0.5 was used. Among all the classification models, Gaussian process support vector machine obtained the highest prediction accuracy of 94.30% (proportion of disease spots in each category corresponding to different resistances) on the test dataset. The above results demonstrate that RGB images of rice canopy combined with Mask-RCNN have the great potential for the accurate identification of rice leaf blast spots, and the number of detected disease spots is highly correlated with the rice leaf blast resistance level. The proposed method is promising for efficient selection of disease-resistant rice varieties in breeding.

In order to recognize citrus leaf canker disease accurately and quickly, a diagnosis method of citrus leaf canker disease based on naive Bayesian classification was proposed. The digital images of leaves with different severities of citrus leaf canker disease were used as the data source. According to the characteristics of color space, a disease spot recognition model based on naive Bayesian classification was established for rapid diagnosis of citrus leaf canker disease, and the diagnostic abilities of naive Bayesian classification, fixed threshold, adaptive threshold and support vector machine for citrus leaf canker disease were compared. The results showed that the method based on naive Bayesian classification was effective in the segmentation of citrus leaf canker disease, and the incorrect segmentation rate was only 3.58%, which was far better than the threshold methods and support vector machine. In terms of performance efficiency, the time order of the four algorithms was fixed threshold method＜adaptive threshold＜naive Bayesian＜support vector machine, all of which were within a reasonable range. Combined with the preparation time, naive Bayesian method had the best performance efficiency. Therefore, the naive Bayesian classification algorithm has a rapid and accurate application ability in the diagnosis of citrus leaf canker disease, and can provide a new way for the accurate diagnosis of fruit tree disease severities.

In order to more accurately monitor the vertical distribution of the leaf area index (LAI) of maize, we propose a conditionally constrained LAI vertical distribution inversion method based on the simulation dataset constructed by the discrete anisotropic radiative transfer (DART) model. First, the simulation effects of DART model on canopy reflectance and photosynthetically active radiation (PAR) were evaluated based on the three-layer vertical distribution scenario, and constructed the corresponding simulation dataset with PAR. Second, a single parameter inversion model for LAI and PAR was built based on the simulated dataset. Finally, using the single parameter inversion model as a priori knowledge, the inversion of the vertical distribution of maize canopy LAI based on the hyperspectral vegetation index was realized by solving the constraint problem. The results showed that the accuracy of the constraint optimization inversion model was higher than that of the single parameter inversion model. The coefficient of determination (R²) of LAI inversion results for top layer of maize increased by 0.022, root-mean-square error (RMSE) decreased by 0.016 m²/m², and normalized root-mean-square error (NRMSE) decreased by 1.3%. The R² of LAI inversion results for middle layer of maize increased by 0.08, RMSE decreased by 0.219 m²/m², and NRMSE decreased by 10.1%. The R² of LAI inversion results for bottom layer of maize increased by 0.069, RMSE decreased by 0.041 m²/m², and NRMSE decreased by 4.6%. Therefore, it can be concluded that the inversion of vertical distribution of LAI in maize canopy using the conditional constraint optimization method can effectively improve the inversion accuracy.

This study focuses on the accuracy and stability of deep learning method for maize tassel segmentation at different tasseling stages and varieties. The RGB images were collected in the experimental base of Chinese Academy of Agricultural Sciences in Xinxiang City of Henan Province from July to September in 2019, and four models, PspNet, DeepLab V3＋, SegNet and U-Net, based on the lightweight network as the feature extraction layer, were applied to compare the accuracy of different models for maize tassel segmentation. Then, the U-Net model with the best segmentation accuracy (mIoU=0.780) was selected to segment the maize tassel in different varieties at different tasseling stages. The results showed that the accuracy of U-Net model at different tasseling stages was generally better (mIoU=0.703 to 0.798), and the segmentation accuracy of maize tassel in fully emerged tassel stage was the highest (mIoU=0.798); the segmentation accuracy of maize tassel in different varieties was significantly different, but the average segmentation accuracy of maize tassel for all varieties was higher (mIoU=0.749), and the segmentation accuracy of Zhengdan 958 (ZD958) was the highest (mIoU=0.814). In summary, the U-Net model has good universality and robustness for maize tassel segmentation, which provides an effective method for tassel monitoring in maize phenotypic test in the future.

This research was based on the combined planting model of corn-soybean strip intercropping and the corns under different nitrogen levels were used as the test materials. The reflectance spectrum and chlorophyll content of leaves and canopies of corns were measured at the jointing stage, tasseling stage and filling stage. Red edge position (REP) was extracted by continuous wavelet transform (CWT) and other algorithms [maximum first derivative method (FD), four-point interpolation method (FPI) and linear extrapolation method (LEM)]. The quantitative relationships between REP and chlorophyll contents were systematically analyzed to compare the accuracy and stability of the REP extracted by each red edge algorithm on the two scales of leaf and canopy. The results showed that, based on the REP-CWT, the estimation accuracy of chlorophyll content was higher on leaf and canopy scales, and the stability was the strongest, which indicated that REP-CWT was feasible in extracting the REP of corn reflectance spectrum. The quantitative estimation models of corn leaf chlorophyll content and canopy chlorophyll content base on REP-LEM and REP-FPI, respectively, were the best. This study provides a new method for extracting the REP of corn reflectance spectrum, and then constructs the best quantitative estimation model of corn chlorophyll content on different observation scales (leaf and canopy), and offers an effective way to monitor the nitrogen nutrition status of corn.

Iron is one of the most abundant elements in the earth’s crust, ranking fourth among all crustal elements. As a trace element necessary for plant growth and development, iron plays a vital role in life processes such as photosynthesis, hormone synthesis, mitochondrial respiration and nitrogen assimilation. Plants transfer a large part of the nutrients accumulated during vegetative growth to seeds to improve the survival rate of the next generation, and iron is no exception. However, since free iron can produce active oxygen and cause damage to plants, iron is generally stored in the seed in a chelated state. When the external conditions are suitable, the seed iron will be reutilized to help the seedlings transform into an active photosynthetic state before absorbing iron from the environment, which has a very important impact on the vitality of seedlings. Seeds are also an important dietary source of iron for most of the world’s population, and iron deficiency can lead to diseases such as iron deficiency anemia which threatens human life and health. Therefore, understanding comprehensively the molecular mechanisms of storage, transportation and reutilization of seed iron is critical to increase seed iron content and iron bioavailable. This article summarizes the current research progress on the long-term storage, transportation and reutilization of iron in plant seeds, as well as the potential strategies for plant iron bioforti?cation, which provides a theoretical basis for cultivating iron-rich crops and improving the bioavailable of dietary iron.

N⁶-methyladenosine (m⁶A) is one of the most abundant messenger RNA (mRNA) modification methods in eukaryotes, and it plays an important role in RNA metabolism and function. The recent studies have revealed that m⁶A modification play roles in the life cycles of various viruses and in the host response to the viral infection. The interaction between host and virus is affected by m⁶A modification. On the one hand, m⁶A modifies viral RNA and regulate virus replication, gene expression and progeny virus production. On the other hand, changes of m⁶A modification in the cellular mRNAs can regulate viral infection. With the discovery of m⁶A regulatory proteins and the invention of m⁶A sequencing methods, a large number of reports on the viral m⁶A have emerged. However, the mechanisms of m⁶A modification in the viral infection have not been thoroughly elucidated. In this paper, we reviewed the recent advances in the different roles of m⁶A modification in the viral infection and host responses, in order to provide references for further studies on the functions and corresponding mechanisms of m⁶A during viral infection.

The effects of different volumes and proportions of lettuce cultivation substrates prepared by materials such as fungus residue of Auricularia auricula, vermiculite, perlite and organic fertilizer on the growth and quality of lettuce were compared and explored. The results showed that the water holding capacity of the substrate within fungus residue was more than two times of the control and was significantly improved. The mixture of vermiculite, perlite, organic fertilizer and fungus residue had high contents of available phosphorus, total potassium, total nitrogen and good water retention, which were beneficial to the growth of lettuce. The production indexes such as plant height, number of blades per plant, leaf area per plant, yield and the contents of ascorbic acid, soluble sugar and soluble protein of lettuce treated with some substrates were higher than those cultivated in soil. According to the comprehensive judgment of biological quality, the suitable formulas were V (fungus residue)∶V (organic fertilizer)∶V (vermiculite)=4∶2∶2 (T₁₃), V (fungus residue)∶V (organic fertilizer)∶ V (vermiculite)∶ V (perlite)=4∶2∶1.5∶0.5 (T₁₄), and V (fungus residue)∶V (organic fertilizer)∶V (vermiculite)∶ V (perlite)=5∶2∶1.5∶0.5 (T₁₇). The nitrate content of lettuce was positively correlated with the proportion of organic fertilizer, but negatively correlated with the proportion of fungus residue. With a reasonable and scientific ratio, the cultivation substrate based on fungus residue can replace the traditional cultivation substrate of lettuce, and improve the quality and yield of lettuce.

A field experiment was carried out on ‘Zheyou 21’ rice cultivar, and the effects of topdressing of silicon (Si) fertilizer on stress resistance and yield of rice were studied at two levels of pesticide application. The normal level of pesticide application (375 g/hm² 75% tricyclazole wettable powder, D1) and the reduced level of pesticide application (225 g/hm² 75% tricyclazole wettable powder, D2) were set up. Each level of pesticide application contained two Si fertilizer treatments, including non-topdressing of Si fertilizer (－Si) and topdressing of Si powder fertilizer (750 kg/hm², ＋Si). The results showed that compared with －Si, the ＋Si increased the breaking resistance of the second stem by 26.71%, and reduced the lodging index by 13.29%, incidence of rice ear neck blast by 15.37%, disease index by 19.09%, and increased yield by 3.33% (P＜0.05) of rice under the treatment of D1. Compared with －Si, the ＋Si increased the breaking resistance of the second stem by 33.67%, and reduced lodging index by 14.04%, incidence of rice ear neck blast by 28.98%, disease index by 23.11%, and increased yield by 11.44% (P＜0.05) of rice under the treatment of D2. In conclusion, topdressing of Si fertilizer could reduce lodging index and disease index of rice ear neck blast, leading to enhance lodging resistance and disease resistance to rice ear neck blast, and increase rice yield under the reduced level of pesticide application. In the case of no Si topdressing, rice yield was reduced due to the reduction in pesticide application, while in the case of Si topdressing, there is no significant effect on rice yield.

Spatio-temporal variation characteristics of soil pH, soil organic matter, total nitrogen, available phosphorus and available potassium contents were determined in Wenling City of Zhejiang Province in 2006 and 2017, respectively, using the analysis methods of mathematical statistics and inverse distance weighting spatial interpolation. The results showed that the soils presented a certain acidification trend with a decrease in soil pH of 5.40%. Soil organic matter (SOM) and nitrogen, phosphorus and potassium contents showed an overall increasing trend. Available phosphorus and available potassium contents increased significantly, with increased ratios of 50.34% and 26.23%, respectively. SOM and total nitrogen contents also increased to a certain extent, increasing by 13.97% and 10.76%, respectively. The results of spatial analysis showed that the soil pH decreased in the west of Wenling, while increased in the east of Wenling. Total nitrogen contents of the soils decreased in the north of Wenling, while increased in the south of Wenling. The available phosphorus and available potassium contents increased significantly. The above results indicate that fertilizer application will be implemented according to soil pH, the abundance and deficiency of nitrogen, phosphorus and potassium, and the law of fertilizer requirements for crops. The improvement of the soil quality and soil sustainable development will be focused on.

By using a ¹³C natural abundance method, this study investigated the rhizosphere priming effects (RPE) of sorghum and maize growing in two types of soil (paddy soil and lou soil) at two stages, and the contents of light fraction organic carbon (ρ₁＜1.7 g/cm³) and heavy fraction organic carbon (ρ₂＞1.7 g/cm³) of soil were also determined. The results showed that planting crops significantly enhanced the soil organic matter decomposition. And the maize induced the most CO₂-C flux derived from soil organic carbon at the trumpet stage in paddy soil, which reaching 18.49 mg/(kg?d). The maize induced stronger RPE than sorghum across all growth stages, which indicated that planting maize would bring more CO₂ emission. The content of light fraction organic carbon of soil changed significantly, while the content of heavy fraction organic carbon remained stable during RPE process. Hence, RPE may directly function on the light fraction organic carbon. This study provides the theoretical basis for controlling the RPE intensity reasonably and reducing global CO₂ fluxes.

Guinea pig is a laboratory animal model to study the illness and stimulation of infection. The previous studies showed that the sensitivity of Zmu-1∶DHP outbred guinea pig to foot-and-mouth disease virus was 100%, which was significantly different from DHP (parental strain). This study took three strains of guinea pigs (Zmu-1∶DHP outbred line, Zmu-2∶DHP outbred line and DHP strain) as the experimental groups. The RNA extracted from spleen of different strains of guinea pigs was transcribed into cDNA and amplified by polymerase chain reaction. The amplified products were sequenced by high-throughput sequencing to analyze the structure, haplotype and polymorphism of MHC classⅠgene in guinea pigs, so as to detect whether there were differences in the expression of MHC classⅠgene among different strains. The results showed that eight MHC classⅠsequences in guinea pig were found and some of these haplotypes had 23 amino acids deletion in exon 3. There were significant differences in the frequencies of haplotype expression reads among some strains (P＜0.05), which suggested that the difference of MHCclassⅠhaplotype expression might be related to the difference of immune ability among different strains. The research provides a theoretical basis for the application of guinea pig strains in disease model and vaccine development.