Background: Bacterial endophytes live inside the plant tissues and are known to play a crucial role in the functioning of host plants by influencing their physiology and development. Moreover, this group of bacteria could produce secondary metabolites to suppress plant pathogens and also produce compounds to promote plant growth. Methods: In this present study, Bacillus amyloliquefaciens was isolated from Oryzae officinalis and screened for antifungal activity against rice sheath blight pathogen, Rhizoctonia solani by dual culture assay. The secondary metabolites produced during the interaction of B. amyloliquefaciens with R. solani were characterized with pathogen-inoculated control and antagonist inoculated control through Gas Chromatography-Mass Spectrophotometry (GC-MS). Result: The isolated Bacillus amyloliqufaciens displayed antagonistic activity against Rhizoctonia solani which showed a 63% inhibiton in the mycelial growth of R. solani over control. Analysis of secondary metabolites from the zone of inhibition, confirmed the presence of unique biomolecules including -Propanamine, N,N,2-trimethyl, 1(2H)-Naphthalenone, 3,4-dihydro-6,7-dimethyl, Diethyl Phthalate, Dichloroacetic acid tridecyl ester, Hexadecen-1-ol trans-9, Glycine N-(N-glycyl-L-leucyl) and 2,6,10-Dodecatrien-1-ol, 3,7,11-trimethyl of antifungal and antimicrobial nature.

Magnaporthe oryzae, the source of the rice blast, is a serious threat to the world’s rice supply, particularly in areas like Tamil Nadu, India. In this study, weather-based models were developed based on count time series and machine learning techniques like INGARCHX, Artificial Neural Networks (ANNs), and Support Vector Regression (SVR), to forecast the incidence of rice blast disease. Between 2015 and 2023, information on rice blast occurrence was gathered weekly from three locations (Thanjavur, Tirunelveli, and Coimbatore), together with relevant meteorological data like temperature, humidity, rainfall, sunshine, evaporation, and sun radiation. The associations between the occurrence of rice blast and environmental factors were investigated using stepwise regression analysis, descriptive statistics, and correlation. Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) were used to assess the model’s prediction ability. The best prediction accuracy was given by the ANN, which outperformed SVR and INGARCHX in every location, according to the results. The complicated and non-linear relationships between meteorological variables and disease incidence were well-represented by the ANN model. The Diebold–Mariano test further demonstrated that ANNs are more predictive than other models. This work shows how machine learning algorithms can improve the prediction of rice blast, offering vital information for early disease management. The application of these models can help farmers make timely decisions to minimize crop losses. The findings suggest that machine learning models offer promising potential for accurate disease forecasting and improved rice management.

Rice (Oryza sativa) is a staple food crucial for food security and economic stability, especially in developing countries. However, rice cultivation faces significant challenges, with rice blast disease, caused by the fungal pathogen Magnaporthe oryzae, being one of the most severe threats, potentially leading to yield losses of up to 30 %. This study aims to develop and apply regression models, including stepwise regression, multiple linear regression (MLR) and ARIMA (Autoregressive Integrated Moving Average), to predict the severity of rice blast disease based on weather parameters. Weekly data over 7 years (2017-2023) were collected from the Paddy Breeding Station at Tamil Nadu Agricultural University, Coimbatore, encompassing various weather factors such as temperature, relative humidity, rainfall and solar radiation. Data pre-processing included handling missing values, detecting outliers and creating time-lagged variables. The study revealed distinct seasonal patterns in rice blast incidence, with peak occurrences observed from mid-November to late January. Among the regression models, the ARIMA model incorporating weather variables as external regressors demonstrated superior performance with an R-squared value of 0.92, compared to 0.55 for stepwise regression and 0.57 for MLR. Accurate predictions of rice blast outbreaks could enable farmers and agricultural managers to implement timely and targeted disease management strategies, reducing dependence on broad-spectrum fungicides and minimizing crop losses. This study contributes to data-driven agriculture and disease management, potentially leading to more effective, economically viable and environmentally sustainable rice cultivation practices.

Cotton, a key cash crop vital to the economy, suffers major yield loss due to Tobacco streak virus (TSV). TSV symptoms are frequently mistaken for physiological disorder, highlighting the need for accurate and timely diagnosis to enable effective disease control. Traditional diagnostics are time-consuming, relying on antibodies and specialized equipment. We developed a rapid, accurate RT-LAMP assay to diagnose TSV in cotton. The TSV inoculum was successfully maintained in Vigna unguiculata, Chenopodium amaranticolor and cotton in glasshouse. Molecular analysis through coat protein resulted in a 929 bp amplicon and the sequences were deposited in GenBank (PQ331227, PQ331228). The RT-LAMP assay employing a newly designed primer set efficiently amplified a 240 bp target region and yielded a distinct ladder-like banding pattern on gel electrophoresis confirming the virus. Colorimetric detection using hydroxy naphthol blue (HNB) dye allows visual identification of the virus with distinct color changes from violet to sky blue. A comparative sensitivity assay revealed that RT-LAMP has 10-fold higher sensitivity (0.05 pg/µl). These findings establish RT-LAMP as a rapid on-site diagnosis technique in comparison to RT-PCR for TSV diagnosis in cotton, facilitating early detection.

The rice genome encodes five non-expressors of pathogenesis-related (NPR) homologs, with OsNPR1/NH1 and OsNPR3/NH3 emerging as pivotal players in salicylic acid (SA)-mediated defense responses. Investigating the functional implications of the remaining NPR/NH genes is crucial for the development of disease-resistant rice cultivars. This study explores the role of OsNH2 in rice defense against sheath blight (ShB) using CRISPR/Cas9-edited mutants of the susceptible cultivar ASD16 and the moderately resistant CO51. OsNH2 knockout mutants showed increased susceptibility to ShB, as evidenced by denser mycelial growth, wider hyphae, and increased superoxide radical content. Two in-frame deletion mutants lacking 15-17 amino acids in the BTB/POZ domain also showed higher susceptibility, highlighting the importance of an intact OsNH2 protein for resistance. qRT-PCR analysis revealed significant downregulation of OsNH1, OsNH3, key transcription factors (OsWRKY4, OsWRKY45, OsWRKY80, OsTGA2, and OsTGA3), pathogenesis-related (PR) genes (OsPR1, OsPR3, and OsPR5), and SA biosynthesis genes (OsPAL and OsICS1) in the mutants. Additionally, OsNH2 mutants in both cultivars showed reduced endogenous SA levels upon Rhizoctonia solani AG1-1A infection. Exogenous SA treatment partially restored resistance and upregulated OsNH1/3 expression in mutants, though not to wild-type (WT) levels. These results suggest that OsNH2 is essential for maintaining SA-mediated defense signaling and optimal expression of NPR1 homologs. Moreover, OsNH2 mutants also showed increased susceptibility to bacterial leaf blight (BLB). Collectively, this research highlights the critical role of OsNH2 in coordinating with OsNH1 and OsNH3 in SA-mediated defense against ShB and BLB in rice.