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Camellia oleifera, a major tree species for producing edible oil, is originated in China. Its oil is also called ‘‘eastern olive oil’’ with high economic value due to richness in a variety of healthy fatty acids (Lin et al. 218). However, leaves are susceptible to leaf spot disease (Zhu et al. 2014). In May 2021, we found circular to irregular reddish-brown lesions, 4-11 mm in diameter, near the leaf veins or leaf edges on 30%-50% leaves of 1/3 oil tea trees in a garden of Hefei City, Anhui Province, China (East longitude 117.27, North latitude 31.86) (Figure S1 A). To isolate the causal agents, symptomatic leaves were cut from the junction of diseased and healthy tissues (5X5 mm) and treated with 70 % alcohol for 30 secs and 1 % NaClO for 5 min, and subsequently inoculated onto PDA medium for culture. After 3 days, hyphal tips were transferred to PDA. Eventually, five isolates were obtained. Then the isolates were cultured on PDA at 25°C for 7 days and the mycelia appeared yellow with a white edge and secreted a large amount of orange-red material to the PDA (Figure S1 B and C). Twenty days later, the mycelium appeared reddish-brown, and sub-circular (3-10 mm) raised white or yellow mycelium was commonly seen on the Petri dish, and black particles were occasionally seen. Meanwhile, the colonies on the PDA produced abundant conidia. Microscopy revealed that conidia were globular to pyriform, dark, verrucose, and multicellular with 14.2 to 25.3 μm (=19.34 μm, n = 30) diameter (Figure S1 D). The morphological characteristics of mycelial and conidia from these isolates are similar to that of Epicoccum layuense (Chen et al.2020). To further determine the species classification of the isolates, DNA was extracted from 7-day-old mycelia cultures and the PCR-amplified fragments were sequenced for internal transcribed spacer (ITS), beta-tubulin and 28S large subunit ribosomal RNA (LSU) gene regions ITS1/ITS4, Bt2a/Bt2b and LR0R/LR5, followed by sequencing and molecular phylogenetic analysis of the sequences analysis (White et al. 1990; Glass and Donaldson 1995; Vilgalys and Hester 1990). Sequence analysis revealed that ITS, beta-tubulin, and LSU divided these isolates into two groups. The isolates AAU-NCY1 and AAU-NCY2, representing the first group (AAU-NCY1 and AAU-NCY5) and the second group (AAU-NCY2, AAU-NCY3 and AAU-NCY4), respectively, were used for further studies. Based on BLASTn analysis, the ITS sequences of AAU-NCY1 (MZ477250) and AAU-NCY2 (MZ477251) showed 100 and 99.6% identity with E. layuense accessions MN396393 and KY742108, respectively. And, the beta-tubulin sequences (MZ552310; MZ552311) showed 99.03 and 99.35% identity with E. layuense accessions MN397247 and MN397248, respectively. Consistently, their LSU (MZ477254; MZ477255) showed 99.88 and 99.77% identity with E. layuense accessions MN328724 and MN396395, respectively. Phylogenetic trees were built by maximum likelihood method (1,000 replicates) using MEGA v.6.0 based on the concatenated sequences of ITS, beta-tubulin and LSU (Figure S2). Phylogenetic tree analysis confirmed that AAU-NCY1 and AAU-NCY2 are closely clustered with E. layuense stains (Figure S2). To test the pathogenicity, conidial suspension of AAU-NCY2 (106 spores/mL) was prepared and sterile water was used as the control. Twelve healthy leaves (six for each treatment) on C. oleifera tree were punched with sterile needle (0.8-1mm), the sterile water or spore suspension was added dropwise at the pinhole respectively (Figure S1 E and F). The experiment was repeated three times. By ten-day post inoculation, the leaves infected by the conidia gradually developed reddish-brown necrotic spots that were similar to those observed in the garden, while the control leaves remained asymptomatic (Figure S1 G and H). DNA sequences derived from the strain re-isolated from the infected leaves was identical to that of the original strain. E. layuense has been reported to cause leaf spot on C. sinensis (Chen et al. 2020), and similar pathogenic phenotypes were reported on Weigela florida (Tian et al. 2021) and Prunus x yedoensis Matsumura in Korea ( Han et al. 2021). To our knowledge, this is the first report of E. layuense causing leaf spot on C. oleifera in Hefei, China.

Abstract The present study was aimed to explore the characterization of polyhydroxy butrate extracted from the bacterial strain under optimized conditions for the production of bioplastic. Under optimized fermentation conditions, Polyhydroxy butrate (PHB) was extracted and subjected to examine their properties via Thin Layer Chromotogram (TLC), Gas Chromotogram- Mass Spectrometer (GC-MS), Fourier Transform Infrared spectrum (FTIR). The presence of a brown spot in the TLC plate indicates the presence of hydroxylgroup which is similar to the polymer group. GC-MS analysis of extracted PHB shows peaks at the retention time of 3.8, 11.6 which is corresponding to octadecanoic acid, hexadecanoic acid, butyl -2-ethylester confirms the presence of polymeric nature in the extracted PHB. The absorption bands of FTIR at 1719–1720 cm −1 indicate the presence of C=O group of PHB. The absorption peaks at wave numbers 500-1000 cm -1 , 1055 cm -1 and 1230 cm -1 denotes (OH) group, (C–O) stretch and (C=O) ester group. From these results, it was confirmed that the extracted PHB is having the potential to replace petroleum plastic.

India is an agricultural country. Paddy is the main crop here on which the livelihood of millions of people depends. Brown spot disease caused by fungus is the most predominant infection that appears as oval and round lesions on the paddy leaves. If not addressed on time, it might result in serious crop loss. Pesticide use for plant disease treatment should be limited because it raises costs and pollutes the environment. Usage of pesticide and crop loss both can be minimized if we recognize the disease in a timely manner. Our aim is to develop a simple, fast, and effective deep learning structure for early-stage brown spot disease detection by utilizing infection severity estimation using image processing techniques. The suggested approach consists of two phases. In the first phase, the brown spot infected leaf image dataset is partitioned into two sets named as early-stage brown spot and developed stage brown spot. This partition is done on the basis of calculated infection severity. Infection severity is computed as a ratio of infected pixel count to total leaf pixel count. Total leaf pixel counts are determined by segmenting the leaf region from the background image using Otsu's thresholding technique. Infected pixel counts are determined by segmenting infected regions from leaf regions using Triangle thresholding segmentation. In the second phase, a fully connected CNN architecture is built for automatic feature extraction and classification. The CNN-based classification model is trained and validated using early-stage brown spot, developed stage brown spot, and healthy leaves images of rice plants. Early-stage brown spot and developed stage brown spot images used in training and validation are the same images that are obtained in phase 1. The experimental analysis shows that the proposed fully connected CNN-based early-stage brown spot disease recognition model is an effective approach. The classification accuracy of the suggested model is found to be 99.20%. The result of the suggested method is compared with those existing CNN-based disease recognition and classification methods that have used leaf images to recognize the diseases. It is observed that the performance of our method is significantly better than compared methods.

Rice is responsible for the stable crop of 3 billion people worldwide, about half of Asian depends on it, and rice is grown in more than 100 countries. Rice diseases can lead to devastating economic loss by decreasing yield production, disturbing a stable food supply and demand chain. The most commonly used method to control rice disease is chemical control. However, misuse of chemical control can cause environmental pollution, residual toxicity, and the emergence of chemical-resistant pathogens, the deterioration of soil quality, and the destruction of biodiversity. In order to control rice diseases, research on alternative biocontrol is actively pursued including microorganism-oriented biocontrol agents. Microbial agents control plant disease through competition with and antibiotic effects and parasitism against plant pathogens. Microorganisms isolated from the rice rhizosphere are studied comprehensively as biocontrol agents against rice pathogens. Bacillus sp., Pseudomonas sp., and Trichoderma sp. were reported to control rice diseases, such as blast, sheath blight, bacterial leaf blight, brown spot, and bakanae diseases. Here we reviewed the microorganisms that are studied as biocontrol agents against rice diseases.

Agriculture, the primary sector of Indian economy. It contributes around 18 percent of overall GDP (Gross Domestic Product). More than fifty percent of Indians belong to an agricultural background. There is a necessary to rapidly increase the agriculture production in India due to the vast increasing of population. The significant crop type for most of the people in India is rice but it was one of the crops that has been mostly affected by the cause of diseases in majority of the cases. This results in reduced yield that lead to loss for farmers. The major challenges faced while cultivating the rice crops is getting infected by the diseases due to the various effects that include environmental conditions, pesticides used and natural disasters. Early detection of rice diseases will eventually help farmers to get out from disasters and help in better yield. In this paper, we are proposing a new method of ensembling the transfer learning models to detect the rice plant and classify the diseases using images. Using this model, the three most common rice crop diseases are detected such as Brown spot, Leaf smut and Bacterial leaf blight. Generally, transfer learning uses pre-trained models and gives better accuracy for the image datasets. Also, ensembling of machine learning algorithms (combining two or more ML algorithms) will help in reducing the generalization error and also makes the model more robust. Ensemble learning is becoming trendier as it reduces generalization error as well as makes the model more robust. The ensembling technique that was used in the paper is majority voting. Here we are proposing a novel model that ensembles three transfer learning models which are InceptionV3, MobileNetV2 and DenseNet121 with an accuracy of 96.42%.

Fungal diseases; blast, and brown spot in rice incur severe yield losses worldwide. Blast is caused by Magnaporthe oryzae, while Bipolaris oryzae is reported as the main causal organism of brown spot. Both diseases cause leaf lesions which are difficult to be differentiated by symptomatology until the late stages. Early detection and differentiation of the lesions would help the adoption of disease management strategies specific to the pathogen and will prevent the native impact on the quality and quantity of rice yields. This study was conducted in the Northern Province of Sri Lanka over five consecutive rice cultivating seasons to characterize the causal fungi of rice blast and brown spot diseases by morphological and molecular means and to develop a visual guide to differentiate the two diseases. Disease incidence was recorded in 114 fields from 2017 to 2019, and fungal isolates associated with lesions of both the diseases were cultured and subjected to morphological and molecular characterization. Competitive growth interaction between M. oryzae and the more common individual fungal isolates of the brown spot lesions, was evaluated. Fungal metagenomics analysis was conducted for the fungal spp. isolated from brown spot lesions. A suppression of blast accompanied by an increased incidence of brown spot disease was observed during the study period. M. oryzae was confirmed to be the causal organism of the blast while over 20 species of fungi were identified to be associated with brown spot lesions through morphological, molecular studies, and metagenomics analyses. Fungal ITS region sequencing revealed considerable genetic variation in the highly conserved region of DNA sequences of blast and brown spot fungal isolates. B. oryzae, Curvularia, and Microdochium species were commonly isolated from brown spot lesions. In vitro competitive growth interaction among the fungal isolates revealed growth suppression of M. oryzae by the fungal isolates associated with the brown spot lesions. Similarly, it can be speculated that the abundance and severity of blast in the field may have an influence on brown spot associated fungi. A simple visual guide was developed to differentiate blast and brown spot lesions. The findings would be highly useful in the timely management of these major fungal diseases affecting rice.

In the last decade, the impact of needle blight fungal pathogens on the health status of forests in northern Spain has marked a turning point in forest production systems based on Pinus radiata species. Dothistroma needle blight caused by Dothistroma septosporum and D. pini, and brown spot needle blight caused by Lecanosticta acicola, coexist in these ecosystems. There is a clear dominance of L. acicola with respect to the other two pathogens and evidence of sexual reproduction in the area. Understanding L. acicola spore dispersal dynamics within climatic determinants is necessary to establish more efficient management strategies to increase the sustainability of forest ecosystems. In this study, spore counts of 15 spore traps placed in Pinus ecosystems were recorded in 2019 and spore abundance dependency on weather data was analysed using generalised additive models. During the collection period, the model that best fit the number of trapped spores included the daily maximum temperature and daily cumulative precipitation, which was associated to higher spore counts. The presence of conidia was detected from January and maximum peaks of spore dispersal were generally observed from September to November.

Abstract The objective of this work was to optimize the extraction process and application of bacterial extracts of Bacillus sp. and Serratia sp. in leaf blast control (Magnapothe oryzae) and brown spot (Bipolaris oryzae) in rice culture. The work was divided into three stages: 1) Bacterial obtaining extracts through liquid-liquid extraction 2) Antagonistic capacity of bacterial extracts to M. oryaze and B. oryae 3) Suppression of brown spot (A1) and leaf blast (A2) in greenhouse. The bacterial isolates in present study were identified as Bacillus sp. (BRM32110) and Serratia marcescens (BRM32113). The crude extract of both isolates at different extraction times 6, 16, 24, 48 and 72 hours reduced the growth of colonies of M. oryzae and B. oryzae by up to 92% and 28%, respectively. The extracts that showed highest inhibition of colony growth were those obtained after 6 and 16h of incubation and were selected for subsequent assays. These, for both isolates were able to reduce conidia germination by up to 91% and apressorium formation of M. oryzae by up to 93%. In green house, A1 the treatment that stood out was the extract of Bacillus sp. (16h) with 6.7% of leaf area affected and in A2 the treatment S. marcescens extract (16h) stood out with only 7.6% of leaf area affected with brusone when compared to control. The use of extracts of Bacillus sp. and Serratia marcescens was efficient in reducing the severity of brown spot and leaf blast in rice crop.

Abstract Winter wheat varieties sown in the Rostov region are characterized by varying degrees of resistance to pests. The size and quality of the wheat harvest are threatened not only by weather conditions, but are reduced by various pathogens, among which yellow and dark brown spots have recently played a large role. Therefore, the aim of the research is to search for sources of resistance among varieties and samples of winter wheat to the causative agents of pyrenophora (Pyrenophora tritici-repentis) and dark brown spot (Cochliobolus sativus). Resistant varieties serve as an ideal factor inhibiting epiphytotics and improving the ecological environment in general. Disease monitoring was carried out in research and production teams of the Federal State Budgetary Scientific Institution Agrarian Research Center “Donskoy”. Laboratory research - in the laboratory of mycology and phytopathology of the All-Russian Institute for Plant Protection. The object of research is 226 varieties and samples of winter soft and durum wheat obtained from the department of selection of winter wheat of the Federal State Budgetary Scientific Institution of the ANC “Donskoy”. It was established: in laboratory conditions (VIZR), 15 varieties and samples showed high resistance to yellow spot in soft winter wheat of intensive type. In studies on resistance to dark brown spot out of 155 samples of winter soft wheat, no highly resistant ones were found.

In the present investigation studies was carried out ontheIsolation, Identification and Enzyme activity of bioagent Pseudomonas fluorescens used for controlling Brown spot disease of Rice caused by Helminthosporium oryzae(Breda de Haan).This is a fungal pathogen causing major disease that causes enormous losses in grain yield (upto 90%) particularly when leaf spotting phase assumes epiphytotic proportions.