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Myosins constitute a class of functional proteins that are widely found in eukaryotic organisms and play important roles in the process of various life activities in the cells of living organisms. In addition, myosins derived from phytopathogenic fungi significantly also have significant impacts on the growth and development of fungi and their pathogenicity. In light of these characteristics, more and more researchers are directing their attention towards the development of pesticides targeting myosin. Designing inhibitors based on myosins from phytopathogenic fungi to develop fungicides with novel mechanisms of action will be an important tool for preventing and controlling plant diseases, thereby ensuring food security. This paper focuses on the research progress of myosins and their inhibitors in phytopathogenic fungal and mainly reviews the structural features and biochemical functions, as well as myosin inhibitor fungicides reported in recent years, which can provide a reference for the discovery of myosin target-directed lead compounds of phytopathogenic fungi and the rational design of the active molecules of new pesticides. [ABSTRACT FROM AUTHOR]

Overuse of chemical pesticides for a long time has caused problems such as environmental pollution, increased resistance of microbial pathogens and excessive pesticide residues. Therefore, the use of biological control measures to reduce or replace chemical pesticides is the focus of the current national development of plant disease control. Biocontrol microorganisms have been widely used in crop disease prevention and control because of their green characteristics. Microbial pesticides can effectively control plant diseases, promote crop growth, and achieve the purpose of reducing the use of chemical pesticides. It is an important direction to promote the green prevention and control of plant diseases. Bacillus spp. is a kind of common biocontrol bacteria in microbial pesticide research and application. B. velezensis, as one of the new microbial pesticide biocontrol bacteria, has a wide distribution in nature, and can be obtained from marine, river sediment, soil, plant roots, plant tissues, animal intestines, etc. It has great development potential in the application of microbial pesticides because of its low toxicity, no pollution, wide antibacterial spectrum, strong stress resistance and ability to secrete a variety of antibacterial substances. B. velezensis controls plant diseases by producing antimicrobial substances, nutrient competition, and triggering systemic resistance responses in plants. This paper summarized the development history of B. velezensis including discovery, identification and naming, describes the biological properties and mechanism of action of its related strains, including antagonism, bacteriolysis, induction of plant resistance, competition, promotion of plant growth, etc., and reviewed the research results of B. velezensis for the biocontrol of plant fungi, bacteria, nematodes, and viruses as well as the development of the formulation, with a view to providing references to future research and applications of B. velezensis. [ABSTRACT FROM AUTHOR]

【Objective】 Rice blast resistance genes Pi2 and Pita are broad-spectrum resistance genes to rice blast in rice areas in South China, which have very important application value for rice blast resistance breeding. Therefore, the development of a set of efficient identification methods is beneficial to genetic breeding efficiency of rice blast-resistant varieties.【Method】According to the variant GCA GGA/GTG TTA of the 787 and 788 codons of the Pi2 gene in ‘Huanghuangyouzhan’ (highly resistant to rice blast) and ‘Guangluai 4’ (highly susceptible to rice blast), and the variant G/T at base 6 640 of Pita gene in Tetp (an international rice germplasm resource with high resistance to rice blast) and Lijiang Xintuan Heigu (a local ermplasm resource with high susceptibility to rice blast), based on the competitive allele PCR (Kompetitive Allele Specific PCR, KASP) labeling technology principle, molecular markers of rice blast resistance genes were developed.【Result】 Two KASP markers (W-Pi2 and W-Pita) were developed to detect the functional sites of Pi2 and Pita. The markers were used to detect the conventional rice, aromatic rice and hybrid rice cultivated by the Rice Research Institute of Guangdong Academy of Agricultural Sciences. 19 resistant varieties were screened, including 13 varieties with a single gene tested as a diseaseresistant allele and 6 varieties with two genes tested as disease-resistant alleles. The comparison of the two markers suggested that the frequency of disease-resistance was higher in the varieties carrying Pi2 resistance gene than in the varieties carrying Pita resistance gene. The results showed that the two markers could detect the allele types of rice blast resistance genes Pi2 and Pita in breeding materials in the early stage (seed or seedling stage), and the breeding materials were not required to be planted in the disease nursery, then individual plants carrying resistant alleles could be screened and identified.【Conclusion】 The developed KASP functional molecular markers W-Pi2 and W-Pita can better distinguish parental varieties with different resistance, clearly distinguish different allele types among breeding materials, and accurately screen breeding materials. [ABSTRACT FROM AUTHOR]

Agriculture in Taiwan is facing problems such as population aging and labor shortage. Therefore, effectively managing the crops in the planting process is often impossible, easily exposing them to disease threats and leading to heavy losses. This study builds a crop disease detection system and applies it to apple scab, tomato early blight, late blight, and leaf mold. The system first collects apple and tomato crop photos and crop disease data from the Plantvillage dataset on the Kaggle platform. Secondly, according to the crop dataset, the crop type recognition and leaf detection model is trained to identify crop types and detect plant leaves. Finally, the detected leaves are inputted into the disease recognition model for disease detection, and the plants infected with the disease are found. The crop detection system intends to help farmers improve the efficiency of monitoring crop disease through aerial cameras so that farmers can treat plants early and avoid agricultural impacts caused by crop disease. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Ecology & Rural Environment is the property of Journal of Ecology & Rural Environment Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In order to judge the severity of crop diseases quickly and accurately, a novel online remote diagnosis method was proposed based on multi-task learning in this paper. The classic model MobileNetV3 was improved by introducing convolutional block attention module and feature pyramid module to boost the performance of the recognition of crops, diseases and pests, and disease levels. Besides, some data augment methods were adopted to extend the small samples. The performance of the improved model and other image recognition models in the identification of crop disease was tested, and the performance of different models with and without data enhancement processing was explored. The results showed that the mean average precision of proposed method on such 3 tasks was more than that of the original model by 1.38, 2.24 and 2.03 percentage points, respectively, and the average recall of proposed method on such 3 tasks was more than that of the original model by 2.38, 1.62 and 1.18 percentage points, respectively. The proposed method outperformed the state-of-the-art methods, such as MobileNetV3, InceptionV3 and YOLOv7. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Taiwan Agricultural Research is the property of Taiwan Agricultural Research Institute, Council of Agriculture, Executive Yuan and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The traditional detection of crop disease mainly relies on manpower and experience, and the informatization level is low. In recent years, image recognition based on transfer learning has developed rapidly and achieved good application effect in many fields. MoblieNetV2 model was used to re-train and fine-tune corn disease image data set by transfer learning method. Then, the optimized corn disease recognition model was applied to the mobile terminal device for application development. The results showed that the final test accuracy reached to 96.83% after repeated training and fine-tuning of the pre-training model. Finally, the optimized model was used to develop a corn disease recognition APP, and the corn was photographed through the mobile APP to obtain the diagnosis results. The application was simple and easy to operate, which could facilitate and quickly identify maize diseases and have important application value in the future agricultural field. [ABSTRACT FROM AUTHOR]

Various types of crop diseases have posed a serious threat to the high-quality development of crops in agricultural production. A timely identification of disease types can greatly contribute to the prevention and control of crop diseases. However, the convolutional neural networks (CNN) recognition of crop leaf diseases cannot fully meet the large-scale production in recent years, due to the numerous parameters, high computational complexity, and poor real-time performance. There is a high demand to improve the accuracy of lightweight CNN models under the relatively complex background of crop leaf images in natural environments. In this study, a lightweight re parameterized leaf diseases identification network (RLDNet) was proposed to identify crop leaf diseases. Firstly, reparameterization was introduced into the MobileNetV2. The inverted residual blocks were reparameterized to construct the reparameterized inverse residual (RIR) block for the high inference speed. A multi-branch architecture was adopted during the training phase. The diversity of the feature space was enriched to improve the representation learning of the network. The reparameterization was used to equivalently convert the multi-branch architecture into a single path structure for the inference after training. The output of the transformed inference model was the same as the original multi-branch. The parameter identity transformation was achieved to improve the inference speed of the model with high recognition accuracy. There was a decrease in the number of output channels and the stacking times of core modules. A shallow and narrow network structure was obtained to enhance the extraction of shallow features, such as the disease colors and textures, indicating the reduced number of model parameters. Secondly, a lightweight ultra lightweight subspace attention module attention mechanism (ULSAM) was added to the last two RIR blocks of the model. Leaf disease characteristics were combined to more effectively distinguish between backgrounds and prospects for better target classification. The interference was reduced in the disease areas from the complex backgrounds. Finally, the DepthShrinker pruning was utilized to learn the importance of the activation function in the model. The redundant parameters were removed to further reduce the space occupation. The lossless lightweight and high accuracy of the model were achieved using knowledge distillation. The recognition accuracy of 99.53% was achieved in the RLDNet on the PlantVillage dataset, whereas 98.49% on the self-built leaf disease dataset with a parameter size of 0.65 M. The inference time for a single-leaf disease image was 2.51 ms. RLDNet shared a performance similar to the Transformer-based models (such as MobileVit-S) and CNN-based models (such as ResNet18) on both datasets, with a significant reduction in parameter, respectively, compared with MobileVitS and ResNet18. The higher recognition accuracy and lighter weight were obtained in the RLDNet, compared with the lightweight models, such as MobileNetV3 and ShuffleNetV2. The improved model can be expected to effectively identify the leaf diseases in the complex backgrounds, with less parameter memory and faster inference speed. The findings can also be applied in the practical production and application of smart agriculture. [ABSTRACT FROM AUTHOR]

Tomato 'Kaisa' was used as test material for pot trials in the artificial climate chamber of Nanjing University of Information Science and Technology from June to August 2022. Using whiteflies as media, through stinging and sucking for virus transmission to tomato plants. The maximum/ minimum air temperature was set at 28℃/18℃ and the relative air humidity at 70% (±5 percentage points) in the artificial climate chamber. The spectral reflectance and relative chlorophyll content (SPAD value) of tomato seedling leaves at different levels of the infection were measured by the SOC710 spectrometer and the SPAD-502 chlorophyll meter. The original spectra was first-order derivative transformed to analyze the relationship between each of the hyperspectral parameters and SPAD value of tomato leaves at different infection levels. In turn, the sensitive bands and characteristic parameters of tomato were screened in order to construct the best model for the estimation of the SPAD value. The results show that different infection levels of tomato leaves had different positions of the red edges. The leftward shift in the position of the spectral curve peak occurred at 710nm, with the maximum peak wavelengths occurring at 711nm, 700 nm, 695 nm, and 690 nm for healthy, slight, moderate, and severe infested tomato leaves, respectively. The elements R531, R816 (positive), R'500, and R'721 (positive) with the maximum highly significant correlation (P<0.01) were selected as spectral feature parameters by correlation analysis of spectral parameters with SPAD value. Spectral indexes were constructed using spectral feature parameters and the elements that were highly significantly correlated with SPAD values were Dy, λb, λr, Rg, RVI, DVI, PRI, GNDVI, NDVI, mNDI and SAVI. The hyperspectral estimation model of SPAD value of tomato leaves under virus disease infection was constructed by screening mNDI, GNDVI, R816, Rg, and R'500 by stepwise regression method, and the R² of the validated model was 0.835. This study showed that the use of spectral technology was feasible for the measurement of SPAD value in tomato leaves affected by virus diseases, which provided a reference for early non-destructive diagnosis of plant diseases. [ABSTRACT FROM AUTHOR]

Copyright of Agricultural Outlook (1673-3908) is the property of Institute of Agricultural Information, Chinese Academy of Agricultural Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The beneficial microbiome assist the plant in disease defense. The root, stem and seed of Sophora tonkinensis from wild type were germinated and grew healthily, but they in cultivated type were susceptible to insect and pathogen attack. In order to explore the possibility of disease control using beneficial endophytic fungal microbiome harbored in S. tonkinensis from healthy wild type, endophytic fungi were isolated from the root, stem and seed of S. tonkinensis from healthy wild type, and then were further identified based on morphological characters and ITS sequence characters. Phylogenetic tree, α-diversity index and β-diversity index were used to analyze the phylogenetic evolution, diversity and similarity among different endophytic fungal microbiomes. The pathogen-resistant function in vitro of endophytic fungal microbiome was determined by the agar plug method and flat-stand method. The results were as follows:(1)131 strains with 23 taxa, 108 strains with 23 taxa, and 64 strains with 11 taxa, were respectively isolated and identified from the root, stem and seed of S. tonkinensis;(2)More endemic genera and all endemic species, indicated that endophytic fungal microbiomes harbored in the root, stem and seed had tissue specificity in genus and species evolutionary tree;(3)The extremely low β-diversity between root-stem, root-seed, and stem-seed indicated that species similarity was very low among different endophytic fungal microbiomes;(4)The high α-diversity showed that endophytic fungal microbiomes harbored in the root, stem and seed were abundant biodiversity;(5)More than one third of the taxa antagonized pathogens in vitro. Endophytic fungal microbiome harbored in the root/stem showed strong broad-spectrum pathogenic bacteria/fungi-resistant function in vitro, and endophytic fungal microbiome harbored in the seeds showed strong broad-spectrum pathogenic fungi-resistant function in vitro. These results indicated beneficial endophytic fungal microbiome with tissue-specificity, biodiversity structure, and strong broad-spectrum pathogen-resistant function in vitro, harbored in healthy root, stem and seed of S. tonkinensis from wild type, and might play an important role in pathogen resistance in host tissues. Such results will provide material and experimental basis for disease control in different tissues of S. tonkinensis using beneficial endophytic fungal microbiome. [ABSTRACT FROM AUTHOR]

Copyright of Acta Agriculturae Zhejiangensis is the property of Acta Agriculturae Zhejiangensis Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

To solve the problems of large number of parameters, low recognition accuracy and slow training speed on the crop disease image recognition model, a multi-scale convolutional network leaf disease image recognition model with attention module is proposed. Based on the residual network module, multi- scale convolution replaces the traditional single-scale convolution, so that the network is widened to obtain more feature information, avoid overfitting caused by network stacking too deep. In order to speed up the model training, depthwise separable convolution is used instead of tradition convolution to reduce the number of model parameters, the attention mechanism is introduced into the residual network to enhance the extraction ability of key feature information of the model, thus improving the recognition accuracy of the model. Through the comparative test of the experimental data set, the recognition accuracy of the improved network model reaches 99.48% and the parameters is only 19.06 MB, the experimental results show that the proposed method can effectively improve the recognition performance of the model and reduce the parameters, which lays a foundation for the realization of low-cost terminal operation. [ABSTRACT FROM AUTHOR]

Copyright of Acta Scientiarum Naturalium Universitatis Sunyatseni / Zhongshan Daxue Xuebao is the property of Sun-Yat-Sen University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Apple trees are susceptible to various diseases that caused by weather, environment and microorganisms. The leaves of plants are the most common parts of the disease. The small area and similar symptoms of diseases have also posed great challenges on the manual observation and experience judgment in recent years. The disease type cannot be diagnosed in time, resulting in the huge losses in apple production. Deep learning can automatically extract features in crop diseases, but it also suffers from an excessive number of parameters and high computational effort. Furthermore, various lightweight architectures have been constructed to provide the strong technical support for the deployment of crop disease identifications, such as less network parameters, less computation, simple models, and low practicability of deep learning models. However, the direct application or improvement of the existing light convolutional neural network (CNN) can fail to further optimize the fine-grained problem in "small variance between classes and large variance within classes" of apple leaf diseases. Multiple CNN frameworks or attention modules can be utilized to consider the coarse-grained global and fine-grained local features of apple leaf diseases. It is necessary for the small number of parameters to meet the requirements of smart agriculture for mobile deployment. In this study, a fine-grained knowledge distillation (FGKD) model was proposed to improve the CNN accuracy in the disease identification of apple leaf suitable for the deployment to smart agricultural mobile terminals. Firstly, contextual information and spatial-semantic relations were used to design the spatial attention (SA) and fine-grained feature extraction (FGFE) modules respectively, and they were embedded into Resnet50 and the designed light CNN as teacher and student networks. Secondly, the SA and FGFE knowledge distillation loss functions were constructed to transfer the feature extraction and fine-grained knowledge representation of the teacher to the student network, in order to enhance the local feature extraction and high-level semantic expression of apple leaf disease images. Finally, the performance of the light student network was close to that of the complex teacher network under the condition of a small number of parameters. The comparative test was carried out on the standard apple leaf disease dataset. The results show that the accuracy of the student network was 98.60% after knowledge distillation, while the number of model parameters was only 0.75 MB, and the average inference time was 25.51 ms. The automatic identification of apple leaf diseases was be rapidly and accurately realized to fully meet the needs of the model of the actual smart agriculture mobile terminals. The SA module and SA distillation function were designed to combine the contextual information and spatial attention, in order to effectively improve the extraction of local information about the disease. The spatial-semantic relationship aggregation of fine-grained features was used to enhance the extraction and expression of high-level semantic information about the disease. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Henan Agricultural Sciences is the property of Editorial Board of Journal of Henan Agricultural Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Acta Agriculturae Zhejiangensis is the property of Acta Agriculturae Zhejiangensis Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In order to clarify the causes and pathogens of wheat leaf blight and spot phenomenon in Tacheng wheat producing area of Xinjiang, more random sampling dry leaf or with irregular disease spot disease of plants were collected from Tacheng prefecture Emin county, and pathogenic bacteria were isolated and purified from diseased leaves by routine tissue separation. The pathogenicity was determined by acupuncture inoculation. And the species of pathogens were identified by morphological observation and multi-gene sequence analysis. The results showed that 76 strains of Alternaria spp were isolated and identified, which confirmed as pathogens by pathogenicity determination. Through the morphological identification, 76 strains belonged to A. alternata, A. rosae and A. infectoria, among which, A. alternata as the main pathogen with 50% frequency of separation. Based on sequences of EF-1 and Alt genes and ITS, the evolutionary tree was constructed, and the three kinds of representative strains YC3, LK19, LK3 showed 100%, 100% and 99% homology with A. alternata, A. rosea and A. infectoria, respectively, which was consistent with morphological identification results. The above results showed that, in Tacheng prefecture, Xinjiang winter wheat field of area, the phenomenon of wheat leaf blight for chain and spore wheat leaf blight was caused by Alternaria infection, and A. alternata, A. rosae and A. infectoria were the main pathogens. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Tea Science is the property of Journal of Tea Science Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Northwest A & F University - Natural Science Edition is the property of Editorial Department of Journal of Northwest A&F University (Natural Science Edition) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Chinese Journal of Oil Crop Sciences is the property of Oil Crops Research Institute of Chinese Academy of Agricultural Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Branch and leaf pruning has been one of the most important links in the process of tomato planting for reducing the disease rate and increasing economic benefits. However, the manual pruning of tomato branches and leaves cannot fully meet large-scale production in recent years, due to the time-consuming and labor-intensive task. An accurate and rapid identification of the pruning position can be greatly contributed to the automatic operation of tomato branch and leaf pruning. In this study, a Recognition method was proposed for the pruning point of the tomato lateral branch using an improved Mask R-CNN. Firstly, the backbone network of ResNet50 in the original Mask R-CNN was replaced with the MobileNetv3-Large to reduce the model complexity. Efficient Channel Attention was added to the feature map C3 and C4, in order to focus more on the features of the lateral and main branch rather than other features. Then, the tomato lateral and main branches were predicted using the improved Mask R-CNN. Three steps were selected to avoid some single branches taken as multiple masks. The lateral and main branch masks were first distinguished by the aspect ratio of the bounding boxes. The overlap and pole constraints were then analyzed for the adjacent masks that belonged to the same branch. The masks with similar constraints were finally merged and joined in the images. The pruning point of the lateral branch was only positioned at one of the two ends of the lateral branch. The lateral pruning point identification was proposed with the help of the main branch, in order to determine the coordinate of the lateral pruning point. The range near the main branch was first determined. And then the branch pruning end was determined by estimating which one of the lateral branch left and right endpoints was in the range. The center point close to the endpoint of the pruning end was finally determined as the pruning point of the lateral branch. The original and improved Mask R-CNN were also compared to verify the detection performance of the lateral and main branches. The recall rate and precision of the original Mask R-CNN were 87.9% and 93.3%, respectively, whereas, the recall rate and precision of the improved Mask R-CNN were 91.2% and 88.6%, respectively. The number of backbone network parameters in the improved Mask R-CNN was only 21.1% of that in the original one. The average segmentation time of the improved Mask R-CNN decreased by 0.038 s than before. The results showed that the backbone network of MobileNetv3-Large reduced the model parameters with the high speed in the improved Mask R-CNN. More branches were recognized, particularly when adding the Efficient Channel Attention mechanism into the feature map C3 and C4. Lateral and main branches that were divided into multiple masks were selected randomly to verify the performance of merging masks. The merging success rate of lateral branch masks was lower than that of the main branch masks, due to the more outstanding curved shape of the lateral branch. The average success rate of merging masks was 86.2%, indicating the excellent performance of merging masks. The presence of multiple pruning points was effectively reduced, where the single branch was normally taken as the multiple masks. Some images were selected randomly in the test set to verify the recognition accuracy for the pruning point of the lateral branch. The result showed that the recognition success rate on sunny days was higher than that on cloudy. The average recognition success rate was 82.9%, which fully met the requirements of lateral branch pruning. This finding can provide the technical support for the tomato branch and leaf pruning automatically. [ABSTRACT FROM AUTHOR]

Plant nematode disease is one of the major diseases to threaten agricultural safety in China. The green, high-efficiency, and low-toxic nematicide pesticides can be one of the better means to control plant nematode disease, further to prevent the large-scale spread of nematode disease. However, the manual visual inspection cannot fully meet the large-scale screening and test of the nematicidal pesticide activity, such as time-consuming, low accuracy, and heavy workload. Particularly, it is very necessary to accurately and rapidly count the number of nematodes in the solution, and then to identify the dead and living insects. In this study, a fast identification was proposed for the nematodes using a coordinate attention mechanism and efficient bounding box regression loss YOLOFN (YOLO for nematodes). Firstly, the sawdust samples of tree trunks with the pine wood nematode were collected in the epidemic area of pine wood nematode disease. The solution slides of pine wood nematode were prepared with the different concentrations, after the pine wood nematode was separated in the laboratory. Secondly, the optical microscope and single lens reflex camera were used to collect the pine wood nematode images. The offline data enhancement was combined with the mosaic online data enhancement to expand the training samples of pine wood nematode images, according to the similar characteristics of pine wood nematode images. Thirdly, the feature extraction module of the coordinate attention mechanism was embedded in the backbone network, according to the theoretical framework of YOLOv5s target detection. The position information of the nematode feature map was then integrated into the channel attention, As such, this improvement enabled the model to focus on the target category and target location at the same time. Finally, a tradeoff was made on the overlapping ratio of the nematode target, the center point distance of the nematode target, the width and height of the prediction frame in the nematode target, as well as the proportion of positive and negative samples of the nematode target. The Efficient Intersection over Union (EIoU) and Focal loss functions was utilized to optimize the localization and classification loss function. There was a minimum difference between the width and height of the real frame and the predicted frame. The weight of the easily distinguishable samples was dynamically reduced to quickly focus the beneficial training samples. The analytical ability and regression accuracy of the model were improved to overlap the nematode targets. The experimental results showed that the performance indicators of YOLOFN were improved by 0.2, 4.4, and 3.8 percentage points, in terms of accuracy, recall, and mean Average Precision (mAP). The mAP of the improved model increased by 1.1, 31.7, and 15.1 percentage points, respectively, compared with the classical detection YOLOv3, SSD, and Faster R-CNN3. There was no difference in the inference time, where the mAP was higher than 11.0, 16.3, and 15.0 percentage points, respectively, compared with the lightweight backbone depth-separable convolution-YOLOv5, Mobilenetv2-YOLOv5, GhostNet-YOLOv5. Therefore, the YOLOFN model can be expected to quickly, accurately and efficiently realize the nematode microscopic examination and statistics, fully meeting the actual needs of research and development of plant nematode pesticides. The finding can provide strong technical support to accelerate the development of new drugs for plant nematode disease control. In the future research, the systematic deployment of the model can greatly contribute to the development of green nematicide, and the protection of plant nematode diseases. The high requirements of rapid end-to-side nematode counting and mortality measurement can be fully met using the expansion of the nematode species image data set. [ABSTRACT FROM AUTHOR]

Huanglongbing (HLB) is known as one of the most important diseases in citrus production. Early detection of diseased plants can be used to prevent the disease from the spreading, in order to reduce the severity of the disease. Hyperspectral analysis can be expected in the crop disease detection in recent years, due to the rich spectral information. However, the hyperspectral equipment is too expensive to apply in large scale, particularly with a large amount of calculation for a large number of bands. An accurate band selection can be utilized to remove the redundant information for the less data storage, calculation, and transmission, in order to avoid the "dimension disaster", and equipment costs. In this study, an Exemplar Component Analysis (ECA)-based method was proposed to optimize the characteristic wavebands of HLB diseases using a ground feature spectrometer. The spectrum detector was also selected the spectral characteristic bands from the hyperspectral information of citrus plants. Three algorithms were utilized to optimize the combination of sever spectral wavebands for comparison. The optimal bands were obtained to evaluate the robustness of the four band selection. Among them, the six machine learning methods were used for the modeling and classification. In addition, a multi-spectrometer was designed using the optimal characteristic band for the detection of citrus HLB. The results show that an accuracy of more than 92% was achieved in the test set of the feature band that selected by the ECA algorithm combined with the six classifiers, indicating the excellent robustness. The self-developed multi-spectrometer was detected up to 95% accuracy for the HLB using the characteristic band. Experiments show that the multiple spectrometer was feasible to characterize the HLB for a small number of characteristic bands. A reasonable characteristic band was greatly reduced the design cost of spectrum detection in the special agricultural disease, and then improved the accuracy of disease prevention and control in orchards. As such, the HLB spectral feature bands were extracted to develop a spectral detector. The proposed ECA-based band selection can be expected to obtain the characteristic bands of citrus HLB with the high robustness. Specifically, the healthy leaves were distinguished from the HLB leaves, particularly from the high similarity to the HLB disease due to deficiency. Moreover, the ECA-based band preference was only preferred seven bands. The redundant bands were removed to reduce the data transmission and storage in the application scenario of large number of samples. The comparison of different classifiers showed that the Xgboost performed the best, in terms of the stable spectral data. The high detection accuracy was also achieved in the rest classifiers using the feature bands. A low-cost multispectral instrument was also designed to promote the unstable performance in different band selection. Anyway, the accurate HLB detection was obtained using the spectrometer data with the feature bands model, where the highest recognition accuracy was close to 95% in the case of triple classification. The promising application prospect was given in the future, compared with the rest high precision and high price professional instruments. The data analysis and spectrometer can be affordable to promote the smart agriculture, compared with the generally expensive spectrometers on sale. Low-cost spectrometers are more likely to be popularized, in order to reduce the amount of generated data for the less hardware requirements. A balance between detection accuracy and production cost can lay a strong foundation for the wide popularization of intelligent equipment. [ABSTRACT FROM AUTHOR]

Mesoporous silica particles (MSNs) can be used as carriers to prepare pesticides controlled release formulations with high drug loading, thus reducing pesticide use and protecting the ecological environment. In this study, mesoporous silica particles loaded with prochloraz (prochloraz@MSNs) were prepared using the one-pot method. Their morphology, drug loading rate, release behavior, antibacterial activity and safety to non-target organisms were systematically studied. The results show that the prepared prochloraz@MSNs had a regular spherical shape and uniform particle size, with an average particle size of about 240 nm and a drug loading rate of 40.6%. The release rate was related to pH and temperature, and acid, alkaline and high-temperature conditions facilitated pesticide release. Compared with emulsifiable concentrate (EC), prochloraz@MSNs had better control effects on Magnaporthe oryzae and Fusahum graminearum, with prolonged effective duration. Prochloraz@ MSNs also had low toxicity to zebrafish, earthworm and human BEAS-2B cells. Therefore, prochloraz@MSNs has great potential as an efficient, low toxic and safe pesticide formulation for sustainable management of plant diseases. [ABSTRACT FROM AUTHOR]

Trehalose is a non-reducing disaccharide that has a variety of biological functions in insects or fungi such as participating in energy metabolism, recovery from stress, and chitin synthesis. Trehalase (EC 3.2.1.28) has become a safe candidate target for the development of novel agrochemicals due to its important roles in regulating the metabolism and the content of trehalose, as well as its functional differences between pests and mammals. In this review, the crystal structures of trehalase and its interaction mechanism with inhibitors are summarized. Meanwhile, the research progress of trehalase inhibitors in agriculture is overviewed including validamycin, natural products salbostatin and trehazolin, deoxynojirimycin, natural alkaloids, piperine, and their corresponding synthetic analogues. In addition, the application of piperine-based compounds in the control of plant diseases or pests is highlighted. This review would provide a reference for the design and the discovery of novel piperinebased agrochemicals targeting the structure of trehalase. [ABSTRACT FROM AUTHOR]

Succinate dehydrogenase inhibitors (SDHIs), targeting succinate dehydrogenase complex, have gradually become the third largest group of fungicides in the world after quinone outside inhibitors (QoIs) and ergosterol biosynthesis inhibitors (EBIs). In recent years, the market share of SDHIs is increasing yearly, and new members are emerging, playing a vital role in the chemical control of plant diseases. However, due to the single action site of these fungicides, fungicide resistance has become a key scientific issue that restricts the development and scientific application of these fungicides. In this paper, the development, products, occurrence and development of resistance, molecular mechanism and application status of SDHIs were reviewed. Together with the latest research achievements from the author's laboratory, the research on target biology and application technology was summarized, to provide references for the innovation and application of higher activity SDHIs. [ABSTRACT FROM AUTHOR]

With the development and widespread use of modern highly active selective fungicides, fungicide resistance in different phytopathogens is becoming increasingly serious, which is one of the most concerned issues in the field of chemical protection of plant diseases. This paper clarified the definitions of terms related to fungicide resistance, overviewed the current situation of phytopathogen resistance to fungicides, and analyzed the causes of fungicide resistance from the perspectives of natural selection and induced mutation. Then, it summarized the factors influencing the prevalence of fungicide resistance, including the characteristics of pathogens, the types and mode of action of fungicides, and other factors. The content of fungicide resistance risk assessment, fungicide resistance mechanism, fungicide resistance evolution, and methods and technologies for routine and molecular detection of fungicide resistance were also reviewed. Finally, the goals and strategies for fungicide resistance management were further proposed, that is, according to the main influencing factors of the formation of fungicide-resistant pathogen populations, short-term and long-term strategies for fungicide resistance control should be designed in a targeted manner. In particular, it is necessary to further strengthen the risk assessment of new fungicdes and new control objects, develop fungicide resistance management strategies, and establish a re-evaluation mechanism. Clarifying the characteristics of fungicide resistance in plant pathogens and developing scientific and reasonable resistance management strategy will provide important reference value for the green control of plant diseases. [ABSTRACT FROM AUTHOR]

Copyright of Acta Agriculturae Zhejiangensis is the property of Acta Agriculturae Zhejiangensis Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Rice is one of the most important food crop in the world, and with the growth of global population, the demand of rice is increasing day by day. Therefore, its safety production is an extremely important global problem. Rice blast caused by Magnaporthe orzae is one of the most devastating diseases on rice plants that threatens the production safety of rice in the world. Therefore, revealing the mechanism of the pathogen-host interaction, and analyzing how pathogen breaking through host defense, are expected to provide new ideas for the exploration of novel drugs and high-efficiency targets, and also have important reference value for the application of gene editing to improve the host resistance. This review summarized the current understanding of the mechanisms by which M. oryzae recognized surface cues to form infectious structure appressoria, and subverted the host-derived immunity to colonize rice cells. We also looked ahead to the key points that needed to be addressed to provide a better understanding of the molecular pathogenesis process and the prospects of scientific control for rice blast. [ABSTRACT FROM AUTHOR]

【Objective】 The study aimed to understand epidemics and infection characteristics of potato late blight in different seasons in Yunnan province, in order to provide scientific insights for improving the early warning accuracy and sustainable green prevention and control of late blight. 【Method】 The meteorological data such as temperature, humidity and rainfall in spring, autumn and winter potato production areas of Yunnan province from 2014 to 2019 were collected, and the late blight CARAH prediction model was used to comprehensive analyze the emergence time, infection cycle, degree of damage and prevention and control strategies. 【Result】 Potato late blight in the spring production regions showed a gradual incidence pattern from northeastern to central, then northwestern of Yunnan province. The potato late blight disease occurred earlier in the spring production regions, such as Daguan, Yiliang and Zhenxiong in northeastern of Yunnan province, and the initial occurrence time of diseased plants usually appeared from late April to May, with 70-130 times infection per each growth season. However, in middle and northwestern of Yunnan province, the initial occurrence time of diseased plants usually appeared from the middle of June, with 40-70 times infection per each growth season. Moreover, in the autumn potato production regions, the initial occurrence time of diseased plants generally appeared from late August to early September, with 25-48 times infection per each growth season. Furthermore, In winter potato production regions, the initial occurrence time of diseased plants usually appeared from early January to mid-January of the following year after planting, with 4-13 times infection per each growth season. 【Conclusion】Our research firstly revealed the epidemics and infection characteristics of potato late blight in different seasons in Yunnan province. The infection degree of potato late blight in spring production regions was the most serious and gradually weakened from northeastern to central, and then to northwestern regions of Yunnan province. Comparing with different seasons, the infection degree of late blight showed that spring potato >autumn potato >winter potato. The management of late blight in Yunnan province should be focused in spring and autumn potato. There were diverse ecosystem types and different potato production regions with different planting seasons in Yunnan province. The occurrence and prevalence of late blight in different seasons and the harm of infection were complex. It was necessary to carry out monitoring, early warning and scientific prevention and control of potato late blight according to local conditions in combination with the resistance of varieties grown in different regions. [ABSTRACT FROM AUTHOR]

Apples in China accounts for more than 50% of the global production and consumption at present. However, the quantity and quality of apples have been threaten by the various diseases, such as alternaria boltch, brown spot, mosaic disease, gray spot, and rust. The CNN-based methods can be expected to recognize the crop leaf disease for the high recognition rates. But, there is still lacking on the recognition accuracy, due mainly to the lack of linkage between global and patch features of disease images in the general disease recognition models. In this study, a disease recognition model was proposed using the patch and global features interactively coupling model (GPF-IC). The main characteristics were also addressed for the small inter-class and large intra-class differences in the apple leaf disease images under natural conditions. Firstly, an attention fusion module was designed in the global feature extraction branch. The convolutionally extracted feature maps were then enhanced to fuse the information on the channels and spaces. The global features and attention activation maps were generated from the enhanced feature maps. Secondly, a cropping module was designed to crop the original image using the attention activation maps. The blocks of images were obtained with the disease information in the patch feature extraction branch, particularly with the patch features. Thirdly, the multi-head cross-attention feature coupling module was designed to realize the bi-directional cross-coupling of patch and global features. As such, the recognition accuracy was improved to enhance the representation capability of fine-grained features of disease images. Finally, three operations of data enhancement were used to evaluate the learning effect of the model for the less overfitting, due to the insufficient training data. A total of 30 540 disease images of six types of apple leaves were obtained with the sufficient number of samples and balanced distribution. The improved model was included as follows. 1) The global feature extraction branch was proposed to promote the disease recognition accuracy by 0.79 percentage points using the attention fusion module. 2) A patch feature extraction branch and a cropping module were introduced to implement the local feature extraction. The model accuracy was then improved by 1.22 percentage points than before. 3) A multi-head cross-attention coupling module was proposed to couple the features from the different branches for the feature extraction and expression capability of the model. The recognition accuracy was improved by 3.39 percentage points, which was the highest recognition accuracy of 98.23%. The experiment demonstrated that the feature extraction can effectively exclude the non-target noises to locate the most discriminative region using the global feature extraction branch. The patch feature extraction branch was efficiently acquired the patch information using the image block embedding. The feature coupling module was realized the interactive coupling of global and patch tokens for the better fine-grained feature representation using multi-headed cross-attention. The GPF-IC was achieved in the 98.23% recognition accuracy of apple leaf disease. The finding can provide a technical support for the automatic recognition of apple leaf diseases in natural scenes. [ABSTRACT FROM AUTHOR]

Plant diseases caused by phytopathogenic microorganisms seriously threaten the global food security, and the development of traditional fungicides is greatly limited by the increasing resistance of pathogens to fungicides and the environmental exposure risks caused by fungicide application. A large number of plant volatile organic compounds (VOCs) are released back into the atmosphere as signal molecules for communication and interaction between plants and the surrounding environment. Plant VOCs can protect themselves from herbivores, or attract pollinators and seed dispersers. They can also directly inhibit the growth of pathogens or activate plant defense systems. This paper reviewed the research progresses in the biosynthesis, collection, analysis, and induced release of plant VOCs, their bioactivity against phytopathogenic microorganisms, and induction of plant immune response; summarized the molecular mechanisms of the antibacterial activity and induced disease resistance of typical green leaf volatile trans-2-hexenal; discussed the limitations of plant VOCs in field applications and the prospects of future research, laying a foundation for the application of such compounds in sustainable disease control. [ABSTRACT FROM AUTHOR]

Crop yield has been one of the most prominent issues in the world in recent years. However, crop diseases have posed a great threat to crop yield. It is a high demand to timely and accurately detect crop disease types and the degree of damage. The manual recognition can rely only on skilled technicians. But, the visual fatigue of humans can easily lead to reduce the accuracy rate. The current machine learning cannot consider the correlation between the attributes in the data set, resulting in low recognition accuracy. In this study, a network model was proposed to identify the damage degree of multiple crop diseases using deep transfer learning and improved RegNet. The model contained four aspects as follows. Firstly, an online data enhancement was carried out at the input side of this model. Nine strategies were selected for the data enhancement, such as the HSV color variation, grayscale transformation, and Gaussian noise. The diversity of data samples increased while reducing the time and space for the data set collection expansion. As such, the over-fitting of the network was alleviated during this time. Secondly, the Efficient Channel Attention (ECA) mechanism was introduced into the feature extraction layer of the model for the cross-channel interaction. The model was then improved to extract more subtle features, particularly for the crop disease features. As such, a higher accuracy of recognition was achieved for the crop disease damage, which increased by 1.5 percentage points, compared with the original model at the same model size. In addition, a multi-scale feature fusion was introduced into the classification layer of the model. A spatial pyramid pooling was adopted to highly improve the accuracy of the model. The degree of crop disease damage was classified at different scales, especially for the fine-grained features. Correspondingly, the accuracy of crop disease damage recognition increased by 2.4 percentage points. Finally, deep transfer learning was used to optimize the overall performance of the model. The convergence speed was accelerated to improve the generalization ability of the model. The recognition accuracy was improved by 2.7 percentage points, compared with the strategy without deep transfer learning. The experimental results show that the improved RegNet network model achieved 94.5 percentage points accuracy on the dataset of crop disease damage level, which was 10.4 percent higher than the original one. The recognition accuracy of the improved model was improved by 2.1, 6.0, 3.7, and 1.6 percentage points, respectively, compared with the commonly-used classification network models, such as ResNet50, VGG16, InceptionV3, and ConvNeXt. Consequently, higher accuracy of recognition and smaller model size were achieved in the improved classification model, compared with the rest. The better performance of feature extraction and stronger classification ability were also obtained for the fine-grained features during this time. The finding can also provide a promising way to identify the crop disease types and the degree of damage. [ABSTRACT FROM AUTHOR]

Aiming at the problems of current crop disease deep learning identification methods are more complex, poor adaptability for deployment on edge mobile devices with limited computing resources and poor real-time accurate identification, a lightweight crop disease identification method based on improved ShuffleNet V2 is proposed. Based on ShuffleNet V2 Unit, the efficient channel attention (ECA) attention module is introduced, using H-Swish activation function inorder to reduce ShuffleNet V2 Unit numbers in each Stage module of network structure, and using a lightweight network design component with depthwise separable convolution. Finally, experiments are conducted on the PlantVillage disease dataset. Experimental results show that model parameter is 2.95 x 105, computational volume is 3.388 x 107 (FLOPs) and 6.674 x 107 (MAdd), and crop disease identification average accuracy reaches 99.24%, provides a reference for the deployment of crop diseases identification methods on resource-constrained devices such as mobile terminals. [ABSTRACT FROM AUTHOR]

A cross-platform pesticide fast searching system for crop diseases and pests control was developed for helping farmers understand and master the knowledge of pest control, facilitating farmers to obtain the key characteristics and scientific control methods of crop diseases and pests through mobile phones quickly and conveniently, and providing online plant protection technology services for farmers. The fast searching system was developed using RESTful web service architecture, HTML5 mobile web development technology, and taking the We chat platform as user access portal, which realized the functions of pesticide searching, plant diseases and pests mapping searching, and backstage data management. The detailed information of pesticides could be searched by pesticide names and crop disease or pests names for the relationships between pesticides and crop diseases or pests were established. The control technologies of 30 important pest and disease in Fujian Province, which including vegetables, fruit trees, rice, tea and tobacco, were covered in the constructed database. The preliminary application results showed that the system displayed satisfying practicability and stability, which indicating the system was suitable for popularization and application in rural grassroots. The pesticide fast searching system based on We chat platform could satisfy the demands of the popularization and application of new crop protection technologies, and providing farmers with simple and convenient online plant protection technology services. The research had great significance on improving the ability of farmers in safe application of pesticides and scientific prevention and control of crop diseases and pests, and promoting the efficiency and reduction of pesticides. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Journal of Oil Crop Sciences is the property of Oil Crops Research Institute of Chinese Academy of Agricultural Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

More than two million tons of tea can be produced each year in China. Tea production, consumption, and exportation have been dominated for decades in the world. However, some diseases in the growing period of tea plants have posed a serious impact on both tea yields and quality in recent years, even leading to serious economic loss. It is a high demand for the accurate identification and treatment of tea diseases for the higher production and quality. In traditional, various tea diseases can be generally identified by the accumulated experiences from experts, indicating the seriously labor-intensive and time-consuming. Since the conventional convolutional neural networks (CNN) have been ever applied to the plant disease identification, the accuracy and efficiency can drop dramatically for the small number of disease samples and uneven distribution. In this study, a transfer learning (called SE-DenseNet-FL model) was proposed for the tea disease identification of small samples. The Squeeze-and-Excitation Networks (SENet) module was first integrated into the DenseNet network structure. Among them, the SENet was used to compress the feature channels for the much more global receptive fields. As such, the SE-DenseNet-FL model was strengthened to propagate the important features for the second calibration. The Focal Loss function was then introduced to replace the loss function in the original DenseNet. Thus, the SE-DenseNet-FL model was guided to focus on the hard-to-classify samples during training, thereby alleviating the impact of the uneven sample distribution on the performance of the model. Finally, a pre-training model was obtained to pre-train with the PlantVillage dataset. A self-developed dataset of tea disease was used to fine-tune the parameters of the pre-training model through transfer learning, thereby alleviating the over-fitting from the small sample data. The dataset was taken from the organic tea garden base of Wantanhe Village, Wuyang Town, Hefeng obtains County, Enshi Tujia, and Miao Autonomous Prefecture, Hubei Province, China. A total of 2175 disease images were captured to involve the five kinds of diseases of three tea varieties, including Zhongcha No. 108, Echa No. 1, and Hefeng Dadou Tea. Five diseases included the white star disease, tea wheel spot disease, tea coal disease, tea round red spot disease, and tea leaf blight. Subsequently, an experimental operating system (CentOS) was adopted to verify the model, where a Pytorch framework was used to implement all the code and the CUDA10.0 parallel computing framework for the acceleration. A series of experiments were conducted in the field environment. Firstly, the model was optimized for the learning rate. The optimal learning rate of 0.001 was achieved, where the initial learning rates were set as 0.000 1, 0.001, 0.01, and 0.1. Then, the new transfer learning models were performed on the tea disease images. Correspondingly, transfer learning can be expected to effectively alleviate the over-fitting for the better generalization of the model. Finally, an experiment was also designed to compare with the original DenseNet model and three classic classification models as AlexNet, VGG16, and ResNet101. Consequently, the SE-DenseNet-FL model using transfer learning can be used to identify the tea diseases with an accuracy of 92.66% in the case of small samples and uneven sample distribution, indicating higher recognition accuracy and stronger robustness. The finding can provide a strong reference for the intelligent diagnosis of tea diseases. [ABSTRACT FROM AUTHOR]

Aiming at the problems of current crop disease segmentation and recognition models with low disease spot segmentation accuracy, insufficient dataset and too slow training speed, a multi-scale tomato leaf disease segmentation algorithm based on improved U-Net network is constructed. This model makes improvements on the basis of the U-Net network structure, reduces the image input size, uses asymmetric Inception multi-channel convolution in the encoder to replace the traditional convolution, which can realize multi- scale extraction of disease features and improve the accuracy of the model. The decoder adds attention module to pay attention to the edge of the tomato disease and reduce the upsampling noise. Moreover, this model introduces GN to accelerate its convergence, and using the improved U-Net network to pretrain on the PlantVillage dataset can improve the segmentation accuracy and speed of the model. The accuracy, recall and MIoU of the proposed method are respectively 92.9%, 91.1% and 93.6%. The experimental results show that this method can effectively improve the model's performance of tomato disease segmentation. [ABSTRACT FROM AUTHOR]

【Objective】 The purpose of the present study was to screen and develop antagonistic bacterial strains with control effect on potato early blight, in order to enrich the resources of biocontrol bacteria for the disease. 【Method】 Antagonistic strains were isolated and screened from the rhizosphere soil of the potato planting areas where early blight disease occurred seriously. Using multiple classification methods including morphological characteristics observation, physiological and biochemical characteristics detection, and 16 S rDNA and gyrB gene sequence analysis, the obtained strains were analyzed. The antagonistic strains were classified and identified. The disease prevention effect, broad-spectrum antibacterial activity and biological function of the antagonistic strains on potato tuber slices were determined to preliminarily verify the preventive effect of the strains, and a pot experiment was further set up to confirm the control effect of the tested antagonistic strains on potato early blight and its resistance to early blight, and effects of soil microbial flora. 【Result】 A strain of antagonistic bacteria WK-1 with an inhibition rate of 89% against potato early blight pathogen plates was obtained through primary screening and re-screening, which was identified as Bacillus subtilis. The control effect of this strain on isolated potato tuber slices was 88.39%, and it had certain inhibitory effect on potato scab, rice sheath blight, maize large spot, wheat scab and rice baica. In addition, it had the ability to dissolve inorganic phosphorus and iron carriers, and the potted plant control effect was 74.49%. After treatment with the antagonist WK-1, the microbial community structure in the soil root zone was improved, and the diversity of beneficial microorganisms in the soil increased. 【Conclusion】 The antagonistic bacteria WK-1 screened in the study with strong inhibitory effect on potato early blight can effectively prevent and control the occurrence of potato early blight, and provide a theoretical basis and strain resources for the biological control of the disease, it has a good prospect in the prevention and control of agricultural plant diseases, and can be promoted according to the actual needs of planting and production. [ABSTRACT FROM AUTHOR]

【Objective】The purpose of the present study was to screen and develop antagonistic bacterial strains with control effect on potato early blight, in order to enrich the resources of biocontrol bacteria for the disease.【Method】Antagonistic strains were isolated and screened from the rhizosphere soil of the potato planting areas where early blight disease occurred seriously. Using multiple classification methods including morphological characteristics observation, physiological and biochemical characteristics detection, and 16 S rDNA and gyrB gene sequence analysis, the obtained strains were analyzed. The antagonistic strains were classified and identified. The disease prevention effect, broad-spectrum antibacterial activity and biological function of the antagonistic strains on potato tuber slices were determined to preliminarily verify the preventive effect of the strains, and a pot experiment was further set up to confirm the control effect of the tested antagonistic strains on potato early blight and its resistance to early blight, and effects of soil microbial flora. 【Result】 A strain of antagonistic bacteria WK-1 with an inhibition rate of 89% against potato early blight pathogen plates was obtained through primary screening and re-screening, which was identified as Bacillus subtilis. The control effect of this strain on isolated potato tuber slices was 88.39%, and it had certain inhibitory effect on potato scab, rice sheath blight, maize large spot, wheat scab and rice baica. In addition, it had the ability to dissolve inorganic phosphorus and iron carriers, and the potted plant control effect was 74.49%. After treatment with the antagonist WK-1, the microbial community structure in the soil root zone was improved, and the diversity of beneficial microorganisms in the soil increased. 【Conclusion】 The antagonistic bacteria WK-1 screened in the study with strong inhibitory effect on potato early blight can effectively prevent and control the occurrence of potato early blight, and provide a theoretical basis and strain resources for the biological control of the disease, it has a good prospect in the prevention and control of agricultural plant diseases, and can be promoted according to the actual needs of planting and production. [ABSTRACT FROM AUTHOR]

【Objective】The aim of this study is to provide basic data and theoretical basis for improving the micro-ecological environment of tobacco planting soil in Yunnan tobacco-growing areas and preventing and controlling tobacco black shank from the perspective of soil biocontrol bacteria screening. 【Method】The rhizosphere soil fungal community structure of healthy and diseased tobacco plants was analyzed by Illumina MiSeq high-throughput sequencing technology. 【Result】The number of fungal OTUs in the rhizosphere soil of healthy tobacco plants (ZCTY) and the rhizosphere soil of diseased tobacco plants (HJTY) were 1809 and 1479, respectively, and the number of OTUs and unique OTUs of ZCTY were 1.22 and 1.62 times that of HJTY, respectively., and the amount, richness and diversity of fungal species in HJTY were significantly reduced, and the ACE, Chao1 and Shannon indices were reduced by 31.87%, 30.47% and 0.68%, respectively, compared with ZCTY; at the same time, the dominant species of Soilcoccozyma, Fusarium ( Fusarium), Spiromastix and Penicillium were significantly different between the two groups. Compared with ZCTY, the relative abundances of Soilcoccozyma, Fusarium and Penicillium in HJTY increased by 177.06%, 54.32% and 144.27%, respectively, while the beneficial bacteria genera such as Chaetomium were significantly reduced, and Spiromastix was a unique genus of HJTY; the rhizosphere soil fungal community of tobacco plants was related to soil physical and chemical factors (pH, organic matter, alkaline hydrolyzable nitrogen, available phosphorus, available potassium) ) changes have a certain correlation, in which Oil Chrysanthemum is significantly negatively correlated with organic matter and alkali-hydrolyzable nitrogen, Fusarium is significantly positively correlated with alkali-hydrolyzable nitrogen, Soilcoccozyma is significantly negatively correlated with pH, ​​and significantly correlated with available potassium Positive correlation, Plectosphaerella was significantly negatively correlated with alkaline hydrolyzable nitrogen and available phosphorus, while Penicillium was significantly positively correlated with alkaline hydrolyzable nitrogen and available phosphorus, and Exophiala was significantly positively correlated with organic matter. 【Conclusion】The reduction of fungal diversity, richness and community structure in the rhizosphere soil of tobacco plants is one of the important indicators of black shank disease in tobacco plants. Among them, Fusarium, Mortierella, Penicillium Genus and other related pathogenic bacteria and saprophytic bacteria increased significantly, while Chaetomium and other beneficial bacteria genera decreased significantly, and the rhizosphere soil fungal community was affected by both biotic and abiotic factors in the soil micro-ecological environment. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Taiwan Agricultural Research is the property of Taiwan Agricultural Research Institute, Council of Agriculture, Executive Yuan and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Mycosystema is the property of Mycosystema Editorial Board and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Agricultural named entity recognition is a fundamental task for information extraction in the agricultural domain. Aiming at the problems of local context features、unable to solve the polysemy of the word、low recognition rate of rare entities in the process of entity recognition, the model combined with character level features and dictionary feature was proposed to automatically identify entities in the text，the character level features were obtained from the BERT(Bidirectional Encoder Representations from Transformers)model. Firstly, the BERT pre-trained language model was used to integrate the left and right contextual information to obtain the character level features, enhance the semantic representation of words, in order to alleviate the problem of polysemy; Secondly, we built an agricultural dictionary and introduced external dictionary information through the feature extraction strategy to improve the recognition accuracy of the model for rare or unknown entities. Among them, two feature extraction strategies were designed to capture the dictionary features, included N-gram feature template algorithm and bi-direction maximum matching algorithm. Then, the character level features and dictionary features were fused as the input of the next neural network layer. Finally, the fused feature information were encoded by the BiLSTM (Bi-directional Long-short Term Memory) neural network layer, obtained the sequence feature matrix, and the optimal text label sequence was obtained by CRF (Conditional Random Field). Based on the knowledge of domain experts, a labeling strategy of named entities in the agricultural field was proposed to solve the problem of fuzzy boundaries of agricultural named entities, in order to ensure the integrity of the entities. The experiments were carried out on the corpus of agricultural, which contained 5 295 labeled corpora and 5 categories of agricultural entities. The results showed that better overall performance was achieved in the corpus, where the recognition precision, recall, and F1-score were 94.84%, 95.23%, and 95.03%, respectively. In terms of specific categories, due to the obvious boundary characteristics of crop diseases and pesticide, the model achieved higher recognition precision than the remaining three entities of agricultural, such as machinery, pests, and crop variety. Experimental comparison showed that for the effectiveness of the dictionary feature extraction strategy, the performance of the model based on the bi-direction maximum matching algorithm was better than the N-gram feature template algorithm. When the number of templates was 10, the performance of the model based on N-gram feature template was the best with the recognition precision of93.95%and F1-score of 94.03%. The bi-directional maximum matching algorithm using feature embedding can obtain more potential information, which was better than one-hot encoding. The precision and F1-score of the model were improved by 0.49 and 0.91 percentage points, respectively. Compared with the models based on BiLSTM-CRF, BERT-BiLSTM-CRF, the precision of the BERT-Dic-BiLSTM-CRF model proposed in this paper had obvious performance advantages with the highest recognition precision of 94.84%. Compared with the BERT-BiLSTM-CRF model, for the recognition performance of rare or unknown entities, the recognition precision of the BERT-Dic-BiLSTM-CRF model was improved by 5.93 and 6.44 percentage points, respectively. Further verifying that the integration of dictionary features into the model can improve the recognition accuracy of the model for such entities. [ABSTRACT FROM AUTHOR]

[ Objective] This paper aimed to overcome the easy breakdown of conventional disease resistant plants. [ Method] A new kind of transgenic rice plant5 was generated based on Host Induced Gene Silencing ( HIGS) technology. [Results] SEC11, a key subunit of signal peptide complex in Magnaporthe oryzae, was selected for generating the HIGS expression vector by gateway technology. HIGS vector was transferred into the Japonica rice cultivar Nipponbare by agrobacterium mediated transformation to obtain transgenic rice plants. The spray assay with conidia of rice blast indicated that the HIGS transgenic rice plants were improved in rice blast resistance. [Conclusion] This study lays the foundation for molecular breading of rice blast resistant varieties with HIGS technology. [ABSTRACT FROM AUTHOR]

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Environmental information perception has been one of the most important technologies in agricultural automatic navigation tasks, such as plant fertilization, crop disease detection, automatic harvesting, and cultivation. Among them, the complex environment of a field road is characterized by the fuzzy road edge, uneven road surface, and irregular shape. It is necessary to accurately and rapidly identify the passable areas and obstacles when the agricultural machinery makes path planning and decision control. In this study, a lightweight semantic segmentation model was proposed to recognize the unstructured roads in fields using a channel attention mechanism combined with the multi-scale features fusion. Some environmental objects were also classified into 12 categories, including building, person, vehicles, sky, waters, plants, road, soil, pole, sign, coverings, and background, according to the static and dynamic properties. Furthermore, a mobile architecture named MobileNetV2 was adopted to obtain the image feature information, in order to reduce the model parameters for a higher reasoning speed. Specifically, an inverted residual structure with lightweight depth-wise convolutions was utilized to filter the features in the intermediate expansion layer. In addition, the last two stages of the backbone network were combined with the hybrid dilated convolution (HDC), aiming to increase the receptive fields and maintain the resolution of the feature map. The hybrid dilated convolution with the dilation rate of 1, 2, and 3 was used to effectively expand the receptive fields, thereby alleviating the “gridding problem” caused by the standard dilated convolution. A channel attention block (CAB) was also introduced to change the weight of each stage feature, in order to enhance the class consistency. The channel attention block was used to strengthen both the higher and lower level features of each stage for a better prediction. In addition, some errors of semantic segmentation were partially or completely attributed to the contextual relationship. A pyramid pooling module was empirically adopted to fuse three scale feature maps for the global contextual prior. There was the global context information in the first image level, where the feature vector was produced by a global average pooling. The pooled representation was then generated for different locations, where the rest pyramid levels separated the feature maps into different sub-regions. As such, the output of different levels in the pyramid module contained the feature maps with varied sizes, followed by up sampling and concatenation to form the final output. The results showed that the objects in the complex roads were effectively segmented with pixel accuracy (PA) and mean pixel accuracy (MPA) of 94.85% and 90.38%, respectively. Furthermore, the single category pixel accuracy of some objects was more than 90%, such as road, plants, building, waters, sky, and soil, indicating a higher accuracy, strong robustness, and excellent generalization. An evaluation was also made to verify the efficiency and superiority of the model, where the mean intersection over union (MIoU), segmentation speed, and parameter scale were adopted as the indexes. The FCN-8S, SegNet, DeeplabV3+ and BiseNet networks were also developed on the same training and test datasets. It was found that the MIoU of the model was 85.51%, indicating a higher accuracy than others. The parameter quantity of the model was 2.41×106, smaller than FCN-8S, SegNet, DeeplabV3+, and BiseNet. In terms of an image with a resolution of 512×512 pixels, the reasoning speed of the model reached 8.19 frames per second, indicating an excellent balance between speed and accuracy. Consequently, the lightweight semantic segmentation model was achieved to accurately and rapidly segment the multiple road scenes in the field environment. The finding can provide a strong technical reference for the safe and reliable operation of intelligent agricultural machinery on unstructured roads. Environmental information perception has been one of the most important technologies in agricultural automatic navigation tasks, such as plant fertilization, crop disease detection, automatic harvesting, and cultivation. Among them, the complex environment of a field road is characterized by the fuzzy road edge, uneven road surface, and irregular shape. It is necessary to accurately and rapidly identify the passable areas and obstacles when the agricultural machinery makes path planning and decision control. In this study, a lightweight semantic segmentation model was proposed to recognize the unstructured roads in fields using a channel attention mechanism combined with the multi-scale features fusion. Some environmental objects were also classified into 12 categories, including building, person, vehicles, sky, waters, plants, road, soil, pole, sign, coverings, and background, according to the static and dynamic properties. Furthermore, a mobile architecture named MobileNetV2 was adopted to obtain the image feature information, in order to reduce the model parameters for a higher reasoning speed. Specifically, an inverted residual structure with lightweight depth-wise convolutions was utilized to filter the features in the intermediate expansion layer. In addition, the last two stages of the backbone network were combined with the hybrid dilated convolution (HDC), aiming to increase the receptive fields and maintain the resolution of the feature map. The hybrid dilated convolution with the dilation rate of 1, 2, and 3 was used to effectively expand the receptive fields, thereby alleviating the “gridding problem” caused by the standard dilated convolution. A channel attention block (CAB) was also introduced to change the weight of each stage feature, in order to enhance the class consistency. The channel attention block was used to strengthen both the higher and lower level features of each stage for a better prediction. In addition, some errors of semantic segmentation were partially or completely attributed to the contextual relationship. A pyramid pooling module was empirically adopted to fuse three scale feature maps for the global contextual prior. There was the global context information in the first image level, where the feature vector was produced by a global average pooling. The pooled representation was then generated for different locations, where the rest pyramid levels separated the feature maps into different sub-regions. As such, the output of different levels in the pyramid module contained the feature maps with varied sizes, followed by up sampling and concatenation to form the final output. The results showed that the objects in the complex roads were effectively segmented with pixel accuracy (PA) and mean pixel accuracy (MPA) of 94.85% and 90.38%, respectively. Furthermore, the single category pixel accuracy of some objects was more than 90%, such as road, plants, building, waters, sky, and soil, indicating a higher accuracy, strong robustness, and excellent generalization. An evaluation was also made to verify the efficiency and superiority of the model, where the mean intersection over union (MIoU), segmentation speed, and parameter scale were adopted as the indexes. The FCN-8S, SegNet, DeeplabV3+ and BiseNet networks were also developed on the same training and test datasets. It was found that the MIoU of the model was 85.51%, indicating a higher accuracy than others. The parameter quantity of the model was 2.41×106, smaller than FCN-8S, SegNet, DeeplabV3+, and BiseNet. In terms of an image with a resolution of 512×512 pixels, the reasoning speed of the model reached 8.19 frames per second, indicating an excellent balance between speed and accuracy. Consequently, the lightweight semantic segmentation model was achieved to accurately and rapidly segment the multiple road scenes in the field environment. The finding can provide a strong technical reference for the safe and reliable operation of intelligent agricultural machinery on unstructured roads. [ABSTRACT FROM AUTHOR]

Crop pests and diseases are emerging threats to global food security in recent years. Manual diagnosis has also been a serious constraint to recognizing the crop diseases in modern agriculture. The latest Convolutional Neural Network (CNN) models have opened up a new way to control diseases with the development of deep learning. However, a complex real environment in the field has posed a great challenge on the general model for disease recognition, due to the single background of the leaf disease images taken in the laboratory. In this study, an improved MobileNet-V2 was proposed to recognize the diseases of crop leaves in the fields, thereby optimizing the parameters for higher accuracy under the complex background. The specific procedures were as followed. Firstly, an image dataset was collected in the field for the disease classification, including 11 kinds of diseased leaves and 4 kinds of healthy leaves of four crops. A series of enhancement operations were then performed on the disease images, including random brightness, and noise. Secondly, a coordinate attention mechanism was added in the 3-18 layers of the basic MobileNet-V2 model. The Region of Interest (ROI) was effectively positioned to concentrate on the disease regions in the pixel coordinate system, thereby to better identify the background and foreground information of the targets. Since the areas of disease spots were different, it was easy to miss some details of the diseases only when using the high-level features. Thus, a feature pyramid module was added to the model using a multi-scale feature fusion. As such, the low-level features were combined with the high-level features, providing for more targets information and better recognition. The specific sampling was operated from the 7×7 to 14×14 feature map, where the same size was fused. Finally, the unnecessary classification layer was removed to optimize the parameter memory of the improved model, where the operation of group convolution was adopted. Compared with the original, the classification accuracy of the improved model was enhanced by 2.91 percentage points, with a little increase in the parameter memory, indicating superior performance. The times of up-sampling were significantly reduced to deal with the feature overlap, where all aspects of indicators were improved than before. Additionally, the improved model was used to better distinguish the similar target features and different lesion areas in detail. In contrast, the recognition accuracy was 0.65 percentage points higher than the EfficientNet-b0a CNN model, indicating a fewer half number of parameters. The improved model also presented much fewer parameters suitable for the mobile terminal, compared with the classical ResNet-50 CNN architecture. Consequently, the improved model can be widely expected to better identify the crop leaf diseases under a complex background, indicating more stable convergence with less parameter memory. This finding can provide strong theoretical support to reliably transplant the new CNN model into the mobile terminal for the disease classification. [ABSTRACT FROM AUTHOR]