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To reveal the causes of photosynthetic characteristics of Yulania denudata with variegated leaf, the green leaves, variegated leaves and yellow leaves of Y. denudata with variegated leaf were used as experimental materials. The anatomical structure and photosynthetic characteristics in three types of leaves were compared from the aspects of photosynthetic pigment content, leaf anatomical structure and light response curve. The results were as follows: (1) The yellow leaf spots of Y. denudata with variegated leaf were chlorophyll-type leaf spots formed by the decrease of chlorophyll content due to the abnormal structure of chloroplasts. (2) The structure of thylakoids in the yellow region of Y. denudata with variegated leaf was abnormal, and ATP synthesis was blocked, which hindered the progress of photosynthesis. (3) The anatomical structure showed that the palisade tissue of the green leaf was developed, the chloroplast integrity and pigment content were higher than those of the variegated leaf and the yellow leaf, and the net photosynthetic rate was higher. (4) There were significant differences in the maximum net photosynthetic rate and light saturation point between the green leaf and the variegated leaf, and the green leaf could withstand a wider range of bright light than the variegated leaf. This study further elucidates that the chloroplast structure of Y. denudata with variegated leaf affects its photosynthetic characteristics, and provides a reference for the breeding of excellent germplasm of Y. denudata with variegated leaf. [ABSTRACT FROM AUTHOR]

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[Objective] The purpose of the study is to provide a scientific basis to control leaf spot disease on Salvia miltiorrhiza which happends in Zhongjiang county, Sichuan province, by identifying pathogenic fungus and screening of fungicides. [Method] The pathogen causing leaf spot disease on S. miltiorrhiza was isolated and purified by tissue separation and single spore culture, respectively. The pathogenicity of the isolate was determined according to Koch's rule and morphological characteristics were observed. The rDNA-ITS sequence primer ITSl/ ITS4, ACT (actin) and GDPH (glyceraldehyde-3-phosphate dehydrogenase) genes were used for molecule biology identification. Five fungicides were used to determine the inhibition effect on that pathogen, and the toxicity regression equation of them was also established. [Result] Combined with pathogenicity, colonymorphological characteristics and rDNA-ITS sequence analysis results, the pathogen was identified as Colletotrichum fructicola. The five fungicides including 80% Mancozeb WP, 80% Ethylicin EC, 250 g/L Azoxystrobin SC, 50% Kresoxim-methyl WG, and 500 g/L Fluazinam SC, had a certain inhibitory effect on mycelium growth, and the EC50 value of 80% Ethylicin EC was 19. 18 µg/mL, which showed a strong inhibitory effect. [Conclusion] The pathogen causing leaf spot disease on S. miltiorrhiza to happen in Zhongjiang county was C. fructicola. 80% Ethylicin EC had the strongest antibacterial activity and could be used for the prevention and control of the leaf spot disease. The study provides a theoretical basis for the treatment of leaf spot on S. miltiorrhiza. [ABSTRACT FROM AUTHOR]

To determine the toxicity of 7 fungicides including prochloraz, difenoconazole, propiconazole, boscalid, myclobutanil, isopyrazam, and thiophanate-methyl against conidia of quinoa leaf spot pathogen(Alternaria alternate), and their morphological toxicology against conidia, the toxicity of 7 fungicides against growth of A. alternata mycelium was determined by mycelial growth rate method, and the toxicity and the effects of 7 fungicides on germination toxicity and germination morphology of conidia of A. alternata were determined and observed by hanging drop method. The results showed that the 7 fungicides had different toxicities against mycelial growth and conidial germination of A. alternata, and the toxicity against mycelial growth was as follows: prochloraz > difenoconazole > propiconazole > boscalid > myclobutanil > isopyrazam > thiophanate-methyl. The EC50 values ranged from 0.19 μg/mL to 245.45 μg/mL. Among them, prochloraz, difenoconazole, and propiconazole had the best antibacterial effect on mycelial growth of A. alternata at the highest concentration(2.5, 2.5, and 10.0 μg/mL, respectively), the inhibition rates were 78.9%, 81.4%, and 79.7%, respectively. The EC50 values were 0.19, 0.26, and 0.47 μg/ mL, respectively. The toxicity of the 7 fungicides against A. alternata conidia was as follows: isopyrazam > boscalid > propiconazole > myclobutanil > prochloraz > difenoconazole > thiophanate-methyl. Their EC50 values ranged from 0.21 μg/mL to 524.69 μg/mL. Among them, isopyrazam and boscalid had the best inhibition effect on A. alternata conidial germination, with inhibition rates of 76.9% and 84.0% at the highest concentration(1.0 μg/mL and 5.0 μg/mL, respectively). Their EC50 values were 0.21 μg/mL and 0.29 μg/mL, respectively. Morphological toxicology study showed that isopyrazam and boscalid could cause malformed germination of A. alternata conidia, which was characterized by enlargement of the germ tube tip and increased branching of the conidia. In conclusion, isopyrazam and boscalid could be used as protective agents to control the alternaria leaf spot of quinoa, and prochloraz, difenoconazole, and propiconazole could be used as therapeutic agents to control the alternaria leaf spot of quinoa. [ABSTRACT FROM AUTHOR]

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An accurate and rapid detection is one of the most important steps for the scientific prevention and control of cucumber leaf diseases. In this study, A DCNSE-YOLOv7 deep learning algorithm was proposed for the detection performance of the early blighted leaves, particularly for the high accuracy of the fine features on the cucumber leaf spots. Firstly, the convolution (Conv2D) of the last feature layer was changed to the deformable convolution (DCNv2) in the backbone feature extraction network, in order to extract the small features from the disease. Secondly, SENet attention mechanism modules were added at the output of the three feature layers in the backbone feature extraction network. The effective extraction was enhanced for the similar diseases in early stages of onset. Meanwhile, K-means++ clustering algorithm was used to re-cluster anchor boxes, in order to avoid the blind learning on the size and location of the target during training. Finally, the CIOU loss function of YOLOv7 was replaced with the Focal-EIOU to accelerate the convergence of the model. The test was conducted in Windows environment using Pytorch deep learning framework for network training. The network training parameters were set as follows: image input size 640×640, batch size 24, multi-threading set to 2, initial learning rate 0.01, and each training iteration (Epoch) was 1 000 times. A weight parameter was saved for each 10 Epoch. Thus, a total of 100 training weight parameters were obtained, and then the best weight parameter was applied into the test data to evaluate the final performance of the model. Three experiments were designed in total, including mainstream model performance, different attention mechanism, and ablation experiments. The results showed that the DCNSE-YOLOv7 algorithm was effectively detected the cucumber leaf diseases in the mainstream model performance, with an average precision mean of 94.25%. Specifically, the average precision of DCNSE-YOLOv7 increased by 2.72, 2.87, 0.28, 12.04, and 7.02 percentage points, respectively, compared with the mainstream object detection models, YOLOv5l, YOLOv7, Faster-RCNN, SSD, and YOLOv7-tiny. The accuracy of DCNSEYOLOv7 was 96.02%, and the detection speed was 52.04 frames per second. Furthermore, the SENet attention mechanism in the DCNSE-YOLOv7 algorithm performed the better detection accuracy than the CBAM and ECA ones. Accuracy and recall rates of 1 000 iterations of DCNSE-YOLOv7 and YOLOv7 models were plotted into the P-E and R-E curves, respectively. Among them, an initial non-zero ordinate was observed in the R-E curve of the DCNSE-YOLOv7 model. There were no pretrained weights during the first training, due to the modification of the main feature extraction network. The trained weights were then used to train the model with YOLOv7 for 1 000 iterations after 300 iterations of training. The accuracy of the model was stabilized after the 500th iteration, indicating the better fitting network. There was also the significant oscillation trend in the recall rate of the model at the 600th iteration. Finally, the ablation experiment showed all positive performances of DCNv2, SENet attention mechanism, K-means++ anchors, and Focal-EIOU loss function. At the same time, the different models were selected to detect the cucumber leaf diseases, in order to verify the performance of the model in the cucumber growth environment. The visualization also show that the DCNSE-YOLOv7 model shared the lower error and leakage detection rates than before. More importantly, the DCNSE-YOLOv7 can be expected to accurately distinguish the target spot disease and the powdery mildew disease with the similar yellow dot features in the early stage. In conclusion, the DCNSE-YOLOv7 model with the high detection accuracy and robustness can provide the effective technical support for the accurate detection of cucumber leaf diseases. [ABSTRACT FROM AUTHOR]

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In order to further understand types of peanut diseases in field, the regularity of main diseases and influencing factors in northern Henan province, accurately prevent and control the field diseases during peanut growth period, in this study, peanut diseases in field were investigated in the field plots including the spring sowing continuous cropping(winter fallow) and peanut wheat rotation fields in Nanzhaoshi village, Datun township, Puyangcity from 2019 to 2021. The occurrence regularity of the main diseases was analyzed, and the influencing factors were studied. The investigation found that the diseases at the early stage of peanut growth were mainly stem rot, early leaf spot and scorch spot and so on, the diseases in at the late stage of growth were mainly early leaf spot, late leaf spot, web blotch, southern blight, and so on. Among them, leaf spot disease was a serious disease that occurred every year during the growth of peanut in the tested field. The correlation analysis results showed that the correlations between the disease index of leaf spot and the average relative humidity of ten days as well as average value of average temperature of ten days were 0.373, -0.311, respectively, reached the extremely significant and significant levels, respectively, indicating that the temperature and the relative humidity had significant and extremely significant impacts on the occurrence of leaf spot. From the results of general linear models univariate analysis, the effects of tillage methods, years, and their interaction on the occurrence of leaf spot disease were not significant, the continuous cropping effect and accumulation of pathogenic bacteria in different years were not obvious. With the advance of growth period, the incidence of leaf spot disease increased, which may be related to the peanut leaf spot disease mainly caused by late leaf spot disease in northern China, as well as the accumulation and repeated infection of leaf spot pathogens in the fields in the year. This study clarified the types, occurrence regularity, and influencing factors of peanut field diseases in northern Henan, and provided a theoretical basis for targeted disease prevention and control. [ABSTRACT FROM AUTHOR]

In order to effectively control the tobacco Corynespora leaf spot, the biocontrol strain YC2140 screened in the previous work of our laboratory was identified by Biolog automatic microbialidentification systemlog automatic microbial identification system 16S rRNA and gyrB sequence molecular identification. Five fungicides commonly used in production and YC2140 were selected for pot and field control experiments. Results showed that the YC2140 strain was identified as Pseudomonas fluorescens by molecular method and Biolog identification system. The pot experiment results showed that under the recommended dosage (449.78 mL/hm² or 449.78 g/hm2 ), the control efficacy on Corynespora leaf spot from high to low was fluazinam 500 g/L SC (the control efficacy was 78.77%), prochloraz 450 g/L EW (77.85%), tebuconazole 430 g/L SC (67.12%), 1 × 108 cfu/mL YC2140 fermentation broth (61.64%) and boscalid 50% WG (46.12%). The field trial results under the same dosage showed that the control efficacy from high to low was prochloraz 450 g/L EW (43.33%), fluazinam 500 g/L SC (40.97%), tebuconazole 430 g/L SC (27.94%), 1 × 108 cfu/mL YC2140 fermentation broth (26.15%), 70% mancozeb WP (dosage 2998.50 g/hm², control efficacy 21.99%). The results of this study can provide reference for the screening of fungicides for the control of tobacco Corynespora leaf spot disease. [ABSTRACT FROM AUTHOR]

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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.)

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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]

【Objective】Since 2017, an anthracnose was extensively occurred on P. cyrtonema in the Hunan province of China. The present paper aimed to clarify the pathogen and the sensitivity of the pathogen to fungicides.【Method】The pathogen fungal stains were isolated from symptomatic leaves and identified by both morphological and molecular tools using both the ITS, ACT, CHS-1 and GPDH gene regions. Pathogenicity was confirmed with inoculation experiments that fulfilled the Koch's postulates. On this basis, the indoor toxicity of 5 fungicides to the pathogen was determined by mycelial growth rate method.【Result】By tissue isolation, 16 morphologically identical strains were isolated from the diseased diseased leaves of four plantation in Hunan province, and the colony morphology and culture characteristics of the strains were observed to confirm that they were Colletotrichum sp.. Molecular identification of CLHJY4-3 determined that the pathogen was C. spaethianum. The pathogenicity was confirmed by inoculation and the Koch's postulates was verified. The average growth rate of the hyphae of this pathogen was 1.1 cm/day. Conidia were 18.4 to 25.0 μm long and 3.9 to 5.3 μm wide. The seta were dark brown, with 2-3 septs, 79-107 μm long. The results of indoor toxicity determination showed that among the five agents, prochloraz had the strongest inhibitory effect on hyphal growth, EC50 was 0.031 μg/mL, followed by pyraclostrobin and tebuconazole, with the EC50 of 0.049 and 0.252 μg/mL, respectively. The inhibitory effects of thiophanate-methyl and carbendazim were relatively poor, with the EC50 of 2.911 and 1.763 μg/mL, respectively. 【Conclusion】C. spaethianum was the pathogen of leaf spot of P. cyrtonema in Hunan province. Moreover, prochloraz and pyraclostrobin were screened as better fungicides against the pathogen fungus. [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.)

Bioactivities of eight fungicides (boscalid, difenoconazole, propiconazole, flusilazole, carbendazim, prochloraz, dimethachlon, mancozeb) to the pathogen of a tobacco leaf spot, Didymella segeticola, were determined using bioassay method at the mycelial growth stage. In vivo protective and curative effects of these fungicides against D. segeticolla on detached tabbaco leaves were also analyzed. The results showed that eight fungicides exhibited different inhibitory activities against mycelial growth of the pathogen, and also showed some protective and curative efficacies against the leaf spot. The most effecient one against mycelial growth of the pathogen was boscalid, with an EC50 value of (0.047 0 ± 0.012 0) mg/L; followed by difenoconazole [(0.079 0 ± 0.005 0) mg/L], prochloraz [(0.29 ± 0.08) mg/L], propiconazole [(0.69 ± 0.12) mg/L], dimethachlon [(1.08 ± 0.33) mg/L], carbendazim [(1.22 ± 0.29) mg/L], flusilazole [(1.38 ± 0.07) mg/L]; and most least effecient one was mancozeb with an EC50 value of (22.80 ± 10.51) mg/L. Flusilazole, difenoconazole, propiconazole, boscalid and dimethachlon all had better protective activity for the control of this disease with >82% efficacy at 25 mg/L dosage treatment than 100 mg/L mancozeb showed 70.51% efficacy. Both boscalid and flusilazole showed better curative activities, with efficacies of >80% at 25 mg/L dosage treatment, followed by prochloraz, difenoconazole, carbendazim and dimethachlon; mancozeb showed relatively poor activity with 63.31% efficacy at 100 mg/L dosage treatment. The results provide scientific basis and reference for the screening of fungicides to control tobacco leaf spot. [ABSTRACT FROM AUTHOR]

In order to improve the inhibition activity and light stability of the protein extract TraT2A from the fermentation broth of Trichoderma atroviride T2 strain, the ultraviolet protective agents (ascorbic acid, humic acid) and the auxiliary agent (IE-08, SP-4821A and SP-4821B) were screened, the inhibition condition of TraT2A for Alternaria alternata were optimized through the colony growth rate test, and the field control efficacy on lily leaf spot disease was determined. The results showed that TraT2A had a good inhibitory effect on A.alternata at 200.00 mg/mL and the inhibitory rate was 72.12%. With the prolongation of the UV illumination time, the inhibition activity of TraT2A gradually decreases. The addition of 5.00 mg/mL humic acid had an excellent UV protection effect on TraT2A and its inhibition rate (69.01%) increased by 39.64% compared with that without a UV protection agent (29.37%). The addition of 0.17 mg/mL adjuvant of SP-4821A increased the inhibition activity of TraT2A most significantly and the inhibition rate (95.13%) increased by 25.97% compared with the TraT2A control (69.16%). According to the optimized formula, 200 g TraT2A (the mass concentration was 200.00 mg/mL), 5 g humic acid (the mass concentration was 5.00 mg/mL), 0.17 g SP-4821A (the mass concentration was 0.17 mg/mL) and 794.83 mL sterilized water was mixed and the field control efficacy of the mixture was tested on lily leaf spot disease, the result showed that its field control efficacy was 80.37%. The research results provide a theoretical basis for the further development of TraT2A preparations for the control of lily diseases. [ABSTRACT FROM AUTHOR]

【Objective】 This paper aimed to study the pathogen and biological characteristics of three leaf diseases of Amomum tsaoko Crevost et Lemarie in Yunnan province, and to provide theoretical basis for the control of leaf spot of A.tsaoko. 【Method】Three fungal diseases were discovered from A.tsaoko plantation in Nujiang Prefecture, Yunnan province. Three strains, including CG-N01, CG-N02 and CG-N03 were obtained by single spore culture, their pathogenicity was determined, spore morphology was observed according to morphological characteristics, and pathogen was identified by phylogenetic tree constructed by multi-gene sequence analysis (rDNA-ITS, tub2, LSU and tef-1 α), and biological characteristics were determined. 【Result】 The morphological characteristics of CG-N01, CG-N02 and CG-N03 were in accordance with the typical characters of Pyrus costina, Phoma matteuccola and Colletotrichum mondsii, respectively. The rDNA-ITS, LSU and tef-1 α sequences of CG-N01 were 489, 901 and 475 bp, respectively ( the login numbers were MN596209, MN689811 and MN696687, respectively) ; The rDNA-ITS, LSU and tef-1 α sequences of the strain CG-N02 were 517, 997 and 529 bp respectively (the login numbers were MN596212, MN650644 and MN696686, respectively) ; The rDNA-ITS, tub2 and tef-1 α sequences of strain CG-N03 were 560, 926 and 738 bp, respectively ( the login numbers were MN596210, MN650645 and MN696688, respectively). Purified strains of CG-N01, CG-N02 and CG-N03 had high homology with P. costina, P. matteuccola and C. simmondsii, respectively. Biological determination showed that strain CG-N01 was advantageous to mycelium growth and conidia formation under the conditions of 28 ℃, alternating light and dark, and pH 6.0. Strains CG-N02 and CG-N03 were favorable for sporulation at 25 ℃, alternating light and darkness, and pH 8.0. 【Conclusion】 This study first reported that C. simmondsii can infect and cause Colletotrichum gloeosporioides, the biological characteristics of the pathogens of leaf spot, leaf blast and anthracnose were determined to provide scientific basis and theory support for the diversity study and effective control of the disease. [ABSTRACT FROM AUTHOR]

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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.)

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.)

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.)

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.)

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.)

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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.)

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.)

[Objectives] Anthracnose is one of the main factors for the early defoliation of pear trees, which results in tree weakness and yield reduction. In order to realize the automatic monitoring of pear leaf anthracnose spot and take timely control measures, this study proposed a recognition algorithm of pear leaf tiny anthracnose spot. [Methods] Fine grained color moments (FG-CM) was proposed to describe the color features of disease. LBP (local binary pattern) and HOG (histogram of oriented gradient) were used to extract texture and shape features of disease. The three features were fused and fed into random forest (RF) to generate optimal fusion feature. Finally, it was sent to RF classifier for training and the multi-resolution combined with deduplication algorithm was used for recognition. [Results] The results showed that the multi feature fusion method was better than single feature and pairwise combination, and multi-resolution method was better than single resolution method. RF algorithm was superior to BP (back propagation), SVM(support vector machines), R-CNN (region-based convolutional neural networks), and Faster R-CNN, SSD (single shot multibox detector) algorithm in the recognition of pear leaf anthracnose. The recognition accuracy was 97.88%, and the recall was 97.95%. It was verified that FG-CM was better than traditional color moment and was the most important factor in the recognition. [Conclusions] The algorithm based on multi-resolution and multi-class feature fusion could accurately and quickly identify the tiny anthracnose on pear leaves, which is of great significance for rapid diagnosis of pear anthracnose damage and timely prevention and control. [ABSTRACT FROM AUTHOR]

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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.)

To clarify the species composition, distribution and pathogenicity differentiation of coffee anthracnose pathogens in my country, so as to lay the foundation for effective control and disease-resistant breeding of coffee anthracnose. [Method] Using morphology combined with ApMat gene sequence alignment and phylogenetic evolution analysis methods, 74 strains of anthracnose pathogens isolated from leaves, branches, and berries of typical anthracnose in coffee growing areas in my country were identified and used in vitro The leaf inoculation method is used to determine the pathogenicity of the obtained strains and analyze the difference in pathogenicity of the strains. 【Result】Through morphological and ApMat gene sequence analysis, 55 strains were identified as a gliosporium complex population and classified into three species: Colletotrichum siamense, Colletotrichum nupharicola, and Colletotrichum theobromicola. The pathogenicity test showed that there were differences in the pathogenicity of different strains. Colletotrichum theobromicola had the strongest pathogenicity, followed by Colletotrichum siamense, and finally Colletotrichum nupharicola. Among them, most of the anthracnose strains from Hainan Baisha Coffee Planting Base and Yunnan Ruili Coffee Germplasm Resource Nursery of the Ministry of Agriculture and Rural Affairs are above the medium level. [Conclusion] All 55 strains of coffee anthracnose pathogens in my country's main coffee growing areas can cause disease, but the pathogenicity is clearly differentiated and is related to geographic origin. [ABSTRACT FROM AUTHOR]

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[ Objectives ] The purpose of this study was to screen resistant cultivars for black spot disease resistance breeding. [ Methods] The tissue block separation method was used to separate and purify the pathogens of black spot disease. The morphological identification of pathogenic conidia was observed by microscope, and then molecular level identification was used to fungal 18S rDNAITS sequence. Based on the identification results of pathogens of black spot disease, 38 cultivars chrysanthemum for tea were inoculated and identified. The characteristics of leaf epidermal trichomes and stomata structure were observed by scanning electron microscope. [ Results] The strains of Fl6 and F20 were isolated from black spot disease leaves of ' Fubaiju', and both two strains were Alternaria alternate. The pathogenicity test showed that both Fl6 and F20 could cause black spot disease of chrysanthemum for tea, and both of them had strong pathogenicity and no significant difference in pathogenicity. The identification results of 38 cultivars showed that there were 11 resistant cultivars ( accounting for 28. 95%), 22 moderately susceptible cultivars ( accounting for 57. 89%), and 5 susceptible cultivars( accounting for 13.16%). By further comparing the differences in leaf structure characteristics of typical resistant and susceptible cultivars, the results showed that the epidermal trichomes density of resistant cultivars was generally greater than susceptible cultivars and the stomatal density was less than that of susceptible cultivars. [ Conclusions] This study screened out some resistant cultivars to black spot disease and found that the epidermal trichomes and stomatal density were related to their resistance. It was also the first time to find that Altermaria alternata could cause chrysanthemum leaf spot disease. [ABSTRACT FROM AUTHOR]

Objective] In order to investigate the accumulation of reactive oxygen species (ROS) in tobacco (Nicotiana tabacum L. ) at dif ferent malurity stages. ( Method ] The tissue staining was performed by nitrotetrazolium chloride (NBT) chemical tissue staining method. And superoxide anion (O2), hydrogen peroxide (H2O2), malondialdehyde ( MDA ) content were measured in leaves with different maturity. respectively, The relative expression of related genes was determined by real-time quantitative PCR (qRT-PCR). [ Result ] The results showed that after the leaves were dyed with NBT, as the maturity of the leaves increased, the blue spots on the leaves increased. The content of H2O2, O2 Ane MDA in the blades also increased with the increase of maturity and reached the maximum in overripe tobacco. With the increase of maturity, the relative expression of NPSAT gradually decreased, while the expression of CYP82E showed a gradually increasing trend. Conclusion with the increase of maturity of tobacco leaves, reactive oxygen species in leaf tissue gradually accumulated, which cased certain damage to tobacco leaves [ABSTRACT FROM AUTHOR]

Corynespora leaf spot caused by Corynespora cassiicola has become an important disease affecting cucumber. To provide the basis of biological research for exploring different resistance mechanisms of C. cassiicola to boscalid, the biological characteristics and environmental fitness of C. cassiicola with different types of resistance to boscalid were determined in this study. A total of 24 strains of C. cassiicola collected from different regions with different resistant types to boscalid were randomly selected. Seven types of resistant mutants cross-resistance to several different succinate dehydrogenase inhibitors (SDHIs) were analyzed and whether the mutants could inherite stably after cultivated nine generations on PDA plates without any fungicides selection pressure were determined. Meanwhile, the biological characteristics of resistant mutants under different carbon and nitrogen sources, temperature and other conditions were also determined to evaluate the fitness of resistant mutants under different environmental conditions. The results indicated that only SdhB-H278Y and SdhB-H278R mutants showed negative cross-resistance between boscalid and fluopyram, while drug resistance in all strains of the different mutation types could be inherited stably. Pathogenicity of different mutation types strains were discrepant, and the SdhD-D95E mutants had the strongest pathogenicity. The most favorable carbon source for the growth of all resistant mutants was maltose, and no discrepancy was showed in the effects of different nitrogen sources. The most optimal growth temperature range of each mutant strain was 25-30 ℃, while SdhD-D95E mutants grew faster than other mutants at temperatures greater than 30 ℃. In the study of heat resistance, the resistant mutants were not able to survive after treated at 65 ℃ for 45 min. At the same time, the mutant could grow normally under the condition of 60 ℃, while the sensitive strain could not. The mycelium growth rate of SdhDD95E mutants were faster than that of other mutant strains, while SdhB-H278Y mutants were slower than that of other mutant strains under treatment with various concentrations of NaCl. Glucose was important to the growth of SdhB-H278R mutants. The results showed that the biological characteristics and fitness of C. cassiicola with different boscalid-resistance were discrepant. The adaptability of SdhD-D95E mutants was improved, indicating that the mutant had stronger competitiveness in the field and was easy to form a dominant population. [ABSTRACT FROM AUTHOR]

Identification of plant disease has been becoming a significant issue on the pattern recognition and image processing in intelligent agriculture. However, the conventional single feature cannot clarify the typical characteristics of crop diseases, due to the various parameters of growth environment, including the soil temperature and humidity, pH value, air temperature and humidity, all are closely related to the plant disease. Recently, the vectorization of multi-structured data learning, and the optimal combination of features can provide a new way to effectively improve the accuracy of disease diagnosis. Taking 50 samples and four types of cucumber diseases as examples, including powdery mildew, fusarium wilt, keratoderma, and sclerotinia sclerotiorum, this study aims to establish an optimal combination model for multi structure of plant disease, using integrated learning of in situ environmental parameters via intelligent Internet of Things. The specific collected data can be 4332 of powdery mildew, 4213 of keratoderma, 234 of anthracnose, and 2895 of fusarium wilt. Each kind of disease was corresponding to several descriptions of expert knowledge, which were combined with the number of expert knowledge description for each disease. Based on the perception model of multimedia sensor networks, a multi-path packet transmission method was proposed to ensure the reliability of multi-structure data transmission. The collected multi-source heterogeneous data was grouped and transmitted along multiple paths, in order to reduce data loss and transmission time, while improve the accuracy of data acquisition. The heterogeneous modal information was then mapped on the shared subspace, while the similarity of heterogeneous data was directly measured under a framework. Furthermore, the relationship of unknown heterogeneous data was derived using the integrated index system of multi structure parameters. A learning vector quantization neural network algorithm was used for the multi-fusion category by fusing the structural environmental parameters of crop growth, and unstructured image features. Three types of data features in the input layer were fused to establish semantic association, including the real-time environment Internet of things data, crop disease image data and expert knowledge text data. A method of multi-structure parameter ensemble learning was used to diagnose disease types, where the recognition rate of samples was 79.4% to 93.6%. Specifically, the recognition rate of powdery mildew was relatively high, due to its obvious image features and clear relationship between environment and disease. The identification rate of fusarium wilt was lower than that of other diseases, because of the similar disease characteristics and anthracnose image features, with emphasis on the expert knowledge description of leaf spot. In order to verify the robustness of the proposed algorithm, a convolutional neural network and simple image recognition technology based on deep transfer learning were selected to carry out experimental analysis on the above-mentioned four typical cucumber diseases. The experimental results show that in the convolution neural network image recognition method, the recognition rate was similar to that of the proposed method, but the recognition time of background was higher than that of the method, due possibly to reduce the dimension of disease image data. Normally, the deep transfer learning method requires many image data inputs into the network for learning, whereas, the actual number of disease images cannot be enough to meet the requirements of deep learning. That is why the recognition rate can be reduced due to the insufficient samples. The intelligent diagnosis technology of cucumber diseases was established based on multi-structural parameters ensemble learning, and thereby to serve as a sound basis for the correlation analysis between image features and environment parameters. Combined with the environmental and expert knowledge resources, the subspace mapping was used to deal with the heterogeneity of different modal data, further to ensure the accuracy of identification based on less identification time. [ABSTRACT FROM AUTHOR]

Balsam pear is a type of vegetable that is popular with humans, which is rich in crude fiber, vitamin C, calcium, iron, and other nutrients, therefore, it is highly significant for human health, and the cultivated area of balsam pear increases annually, however, it is more susceptible to diseases that affect the quantity and quality of the crop yield. Recently, it was reported that the Balsam pear is exposed to many diseases namely, powdery mildew, gray leaf spot, gummy stem blight, Phyllosticta leaf spot, etc. The aim of the study was an automatic identification method for Balsam pear leaf diseases based on improved Faster R-CNN. 1 204 pictures of balsam pear leaves were photographed under sunlight which included the healthy balsam pear leaf as control and infected leaves with powdery mildew; gray leaf spot; gummy stem blight, and Phyllosticta leaf spot. The data were accumulation and rotating and flipping randomly. Finally, 10 627 pictures were obtained as the experimental groups. The design of the experiment was carried out as 9 564 pictures used as a training group, 1 435 pictures of the training group were used as a detection group. and 1 063 pictures were used as the experimental groups. The residual-structure convolutional neural network, ResNet-50, was used as the feature extraction network of Faster R-CNN. Two classic networks, ZF-Net and VGG-16, were compared to extract features that showed significant results, proving whether the proposed approach performed well or not. The pre-trained ImageNet model for transfer learning was joined to the network, which saved computing and time costs. Feature maps obtained from the feature extraction network were input to the Region proposal network for proposals, which included a series of anchors, scores, classification loss, and bounding-box regression loss. Finally, feature maps and proposals were sent to R-CNN for the ultimate accurate location. The obtained results indicated that the performance of the original model was not good because balsam pear leaf diseases were complicated in color, texture, and shape. The feature extraction networks were likely to miss some diseases of the balsam pear leaf diseases because their forms were tiny, and some took up a few pixels that made it difficult to distinguish diseases. To solve the difficulty of detecting the small targets of balsam pear leaf diseases, Faster R-CNN was modified to increase the different sizes of bounding boxes, along with the introduction of the feature pyramid networks to ResNet-50. Feature pyramid networks helped to extract feature maps from every block in ResNet-50, which contained an accurate location and strong semantic information. Feature pyramid networks then handed it over to the Region proposal network to get more accurate proposals to make object recognition. As a result, the experiment showed that the performance of the trained model using the original ResNet-50 was better than that used ZF-Net or VGG-16 as a feature extraction network, and the mean average precision was 78.85%. The average precision of healthy balsam pear leaves, powdery mildew, gray leaf spot, gummy stem blight, and Phyllosticta leaf spot were 88.24%, 75.73%, 51.75%, 72.00% and 72.15%, respectively. The mean average precision for the feature pyramid networks’ final model was 0.863 9, which is 7.54% higher than before. The average precision of healthy balsam pear leaves, powdery mildew, gray leaf spot, gummy stem blight, and Phyllosticta leaf spot were 89.24%, 81.48%, 83.31%, 88.62%, and 89.24%, respectively, and the average precision of gray leaf spot increased the stain by more than 16%. Detection time reached 0.322 s/frame, ensuring real-time detection. This method was characterized by good durability and high accuracy to detect diseases of balsam pear leaf diseases in a complex natural environment and had important research significance for the prevention of melon and fruit diseases. [ABSTRACT FROM AUTHOR]

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Copyright of Journal of Agro-Environment Science is the property of Journal of Agro-Environment Science 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.)

Copyright of Forest Research is the property of Forest Research 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.)

Neopestalotiopsis leaf spot is a new disease on strawberry. The pathogens of the new strawberry leaf spot disease from Changping district, Beijing, Zhucheng city, Shandong Province, Baoshan city, Yunnan Province, Huizhou city, Guangdong Province and other regions were isolated and identified. The pathogen was identified by morphological analysis and ITS phylogenetic analysis. Sensitivities of 8 isolates to 8 commonly used fungicides and SYP-14288 were determined by indoor inoculation. The results showed that the pathogen was Neopestalotiopsis clavispora. SYP-14288 had a strong inhibitory effect on the mycelia growth of 8 isolates of N. clavispora, with an average EC50 value of (0.004 ± 0.002) μg/mL, which could be used as the first choice for the control of strawberry leaf spot disease. The mean of EC50 values were 0.006-0.803 μg/mL for prochloraz, azoxystrobin, tebuconazole, epoxiconazole, difenoconazole, and myclobutanil, respectively. Chlorothalonil exhibited lower toxicity. while mancozeb had the lowest inhibitory activity. Those results are important for the rational application of fungicides in the chemical control of Neopestalotiopsis leaf spot, a new disease of strawberry. [ABSTRACT FROM AUTHOR]

[Objective] The study aimed to clarify the changes of nutritional components of mulberry leaf spot and the effects on the health of silkworm. [Method] The healthy mulberry leaves and the diseased mulberry leaves were collected in the outbreak season of mulberry brown spot disease. The contents of nutritional components were determined, and the effect of mulberry leaf spot on silkworm was analyzed by sericulture test. [Result] Results showed that the contents of crude protein, crude fiber, total sugar, solubility sugar, amino acides of disease mulberry leaves were significantly lower than that of health mulberry, while crude ash and mineral elements (except P) increased remarkably. Fhe biological experiment of silkworm rearing showed that the affected leaves had a great influence on the uniform life rate, larva weight, larval stage and digestive absorption rate of the insect pupa in home, but had no obvious effect on cocoon quality. [Conclusion] Mulberry leaves infected with mulberry brown spot disease have a great impact on the nutritional components of mulberry leaves and the health of bombyx mori. Therefore, in silkworm areas where brown spot disease is common, it is necessary to plan harvesting leaf reasonably and avoid using diseased leaves when brown spot disease is serious. [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.)

Peanut is one of the most important oil crops in the world. Up to now, 81 Arachis species are identified and arranged in 9 infrageneric taxonomic sections according to its morphological characteristics, geographical distribution and cross-compatibility. Cultivated peanut has a very narrow genetic base, which is a fundamental limitation to crop improvement using only cultivated germplasm. Compared with cultivars, wild peanut species have high genetic diversity, can adapt to a series of complex environments and are important sources for resistance to bi-otic and abiotic stress. An increasing number of studies showed that many wild species of peanut were highly resistant to root -knot nematode, late leaf spot and rust. In recent years, new tools for genetic and genomic analysis, provided better efficiency in using peanut resources in crop improvement. This paper reviewed the wild species of peanuts and introduced the wild peanut with disease resistance to assist the genetic improvement of peanut. [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.)

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.)

Copyright of Journal of Agricultural Science & Technology (1008-0864) is the property of Journal of Agricultural Science & Technology 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.)

Pathogen of oil palm leaf spot disease was isolated and purified, and referred to as strain YZ-4. According to its morphological characteristics and the sequence of internal transcribed spacer (ITS), the strain YZ-4 was identified as Pestalotiopsis microspore. Results of biological characteristics showed that the optimum temperature and pH for YZ-4 mycelia growth were 28-30°C and 6.0-8.0 respectively, while the highest rate of conidium germination occurred at 25-30°C, and pH 6.0-9.0. The most efficient utilized carbon sources by mycelium were D-maltose and soluble starch. When soluble starch, D-maltose, D-galactose and glucose were used separately as carbon sources, YZ-4 conidial germination rates were 94.33%, 88.33%, 90.00%, and 89.67% respectively. These rates were not significantly different. Peptone was the most efficient nitrogen source. As peptone and potassium nitrate were separately used by YZ-4 as nitrogen sources, the corresponding conidial germination rates were 80.33% and 74.00% respectively. [ABSTRACT FROM AUTHOR]